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Merge PR #48: feat: add Xiaohongshu source + Reddit public fallback

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- Xiaohongshu search via local MCP service (opt-in, zero impact if service not running)
- Reddit public JSON fallback (works with zero API keys)
- Reddit priority: ScrapeCreators -> OpenAI -> public fallback
- Updated env.py: Reddit always available via public fallback

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Matt Van Horn
2026-03-07 16:11:35 -08:00
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docs/test-results/v1-vs-v2-quick/analysis.md
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# V1 vs V2 Comparison Analysis
**Date:** 2026-02-06
**Queries tested:** 4 (1 head-to-head, 3 V1-only)
**Scope:** Quick smoke test, not full 17-query matrix
---
## Part 1: Head-to-Head -- "kanye west" (NEWS Query)
### Dimension-by-Dimension Scoring
#### 1. Query Parsing Display
Does it show the `🔍 **{TOPIC}** · {QUERY_TYPE}` line before running tools?
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 1 | No parsing display at all. Output starts with "## What I learned:" -- jumps straight into synthesis. No acknowledgment of topic or query type before research. |
| V2 | 1 | No parsing display either. Output starts with "Here's what I found:" then "## What I learned:" -- same problem as V1. |
**Analysis:** Neither version actually rendered the query parsing display. V2 SKILL.md explicitly requires `🔍 **kanye west** · News` before any tools run, but the agent did not produce it. This is a V2 instruction that failed to land. Both score 1/5.
Possible cause: The parsing display is supposed to appear *before* tools are called -- it may have been shown during execution but not captured in the final output text. If so, both outputs represent only the post-research synthesis, not the full session. Regardless, based on what is in the output files, neither shows it.
---
#### 2. Source Coverage (Reddit/X/Web counts)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 3 | `Reddit: 0 relevant threads` / `X: 30 posts │ ~10 likes` / `Web: 20+ pages`. Two of three sources returned results. Reddit was zero. |
| V2 | 3 | `Reddit: 0 threads (no results this cycle)` / `X: 29 posts │ 33 likes │ 14 reposts` / `Web: 30+ pages`. Same pattern: two of three returned results. |
**Analysis:** Nearly identical coverage. Both got zero Reddit results (likely a script/API issue for this topic, not a SKILL.md problem). V2 has slightly more precise X metrics (33 likes, 14 reposts vs. V1's vague "~10 likes"). V2 has more web pages (30+ vs 20+). Both miss the 10+ Reddit threshold for a score of 4+.
---
#### 3. Citation Quality (sparse vs every-sentence)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 2 | No inline citations at all. The body text makes claims ("full-page Wall Street Journal apology," "Hellwatt Festival in Italy") but never attributes them to a specific source. The stats box lists "Washington Post, Billboard, AllHipHop" but the body has zero `per @handle` or `per Rolling Stone` attributions. |
| V2 | 5 | Every bold section ends with a sparse, clean citation. Examples: `"per Rolling Stone"`, `"per The Washington Post"`, `"per Billboard"`, `"per AllHipHop"`, `"per The News International"`. One citation per topic, never chained. Exactly what V2 SKILL.md specifies. |
**Analysis:** This is the single biggest quality gap between V1 and V2. V1's output reads like a Wikipedia summary -- informative but ungrounded. V2 reads like a researched briefing where every claim has a named source. V2 nails the "sparse citation" rule from its SKILL.md: `"cite 1 source per pattern, short format: 'per @handle' or 'per r/sub'"`.
V1 quote (no citation): `"He'll headline the new Hellwatt Festival in Italy (July 4-18, 2026)."`
V2 quote (cited): `"Ye is headlining a brand-new festival at the 103,000-capacity RCF Arena in Italy over three weekends from July 4-18, 2026 — his first-ever live concert in Italy, per Billboard."`
---
#### 4. Summary Structure (bold topic headers, organized sections)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 3 | Has a coherent narrative structure with a paragraph of synthesis, then a `**KEY THEMES:**` numbered list. But the opening is a single dense paragraph, not broken into scannable sections with bold headers. |
| V2 | 5 | Each storyline gets its own bold header: `**BULLY Album — March 20, 2026 via Gamma**`, `**Public Apology for Antisemitism**`, `**Hellwatt Festival in Italy**`, `**Health Concerns**`, `**Grammys Ban**`, `**Kim & Lewis Hamilton Buzz**`. Each is a standalone scannable unit with 1-3 sentences. |
**Analysis:** V2 follows the SKILL.md template exactly: `**{Topic 1}** — [1-2 sentences, per source]`. V1 uses a blob + list approach which is readable but less scannable. V2 is notably better for a user who wants to skim and find the story they care about.
V1 structure: 1 dense paragraph -> 5-item `KEY THEMES` list
V2 structure: 6 bold topic cards, each self-contained -> no KEY THEMES list (but doesn't need one because the structure itself is the organization)
---
#### 5. Stats Box Format (emoji tree vs plain text)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 4 | Uses `├─` tree format with emoji: `├─ 🟠 Reddit: 0 relevant threads` / `├─ 🔵 X: 30 posts` / `├─ 🌐 Web: 20+ pages` / `└─ Top voices:`. Minor deviation: says "0 relevant threads (filtered out noise)" instead of the V1 SKILL.md template "0 threads (no results this cycle)". Also omits the `🗣️` emoji on the Top voices line. |
| V2 | 5 | Perfect match to V2 SKILL.md template: `├─ 🟠 Reddit: 0 threads (no results this cycle)` / `├─ 🔵 X: 29 posts │ 33 likes │ 14 reposts (via xAI)` / `├─ 🌐 Web: 30+ pages │ rollingstone.com, ...` / `└─ 🗣️ Top voices: @honest30bgfan_ (33 likes), @HipHopCrave_ │ Rolling Stone, Washington Post, Complex`. Includes `(via xAI)` notation, `🗣️` emoji, @handles with engagement counts. |
**Analysis:** V2 is tighter and matches its template exactly. V1 is close but has minor deviations (custom "filtered out noise" text, missing `🗣️` emoji, no @handles or engagement counts on Top voices). V2's inclusion of actual @handles with like counts (`@honest30bgfan_ (33 likes)`) adds credibility.
---
#### 6. Research Grounding (actual research vs generic knowledge)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 4 | Clearly grounded: mentions specific details like "Wall Street Journal apology (Jan 26, 2026)," "four-month-long manic episode," "frontal-lobe brain injury," "North West collaborated on 'Piercings on My Hand,'" "Monumental Plaza de Toros." These are specific enough to be from research, not pre-training. Minor generic leakage: the "KEY THEMES" list uses editorial framing ("Accountability arc," "Mental health transparency") that feels more like analysis than research extraction. |
| V2 | 5 | Every fact is specific and attributed: "12th studio album," "13-track project features Peso Pluma, Playboi Carti, and Ty Dolla Sign," "earlier leak versions used AI-deepfaked vocals, which have reportedly been re-recorded," "103,000-capacity RCF Arena." The AI-deepfaked vocals detail is a standout -- it is clearly from research, not something a model would know from pre-training. The Kim/Lewis Hamilton item (`"X chatter is heavily focused on Kim Kardashian's relationship with Lewis Hamilton"`) is explicitly sourced from X data, not general knowledge. |
**Analysis:** Both are well-grounded, but V2 has more "could only come from research" details. The deepfaked vocals story, the exact venue capacity, and the explicit X chatter observation are details that prove the synthesis is from the research output, not hallucinated.
---
#### 7. Prompt Quality (invitation to share vision, not dumping prompts)
| Version | Score | Evidence |
|---------|-------|----------|
| V1 | 3 | Ends with: `"Want to dive deeper into any of these threads — the apology, the new albums, the Grammys situation, or Bianca Censori? Just tell me what angle you're interested in."` This is a follow-up invitation, but it is NOT the SKILL.md-specified invitation. It is topic-specific and conversational, which is nice, but it does not ask the user to "share your vision for what you want to create." It misses the prompt-generation angle entirely. |
| V2 | 5 | Ends with exactly: `"Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into your tool of choice."` This matches the V2 SKILL.md template verbatim. It positions the skill correctly: not a news summarizer but a research-to-prompt pipeline. |
**Analysis:** V1's closing is friendly but off-brand. It treats the skill as a research tool, not a research-to-prompt tool. V2 correctly frames the next step as "tell me what to create and I'll write the prompt." This is a meaningful difference -- V1 would leave a user thinking they just got a summary, while V2 primes them to get a usable output.
---
### Head-to-Head Scorecard
| Dimension | V1 | V2 | Winner |
|-----------|----|----|--------|
| 1. Query Parsing Display | 1 | 1 | Tie (both failed) |
| 2. Source Coverage | 3 | 3 | Tie |
| 3. Citation Quality | 2 | 5 | **V2 (+3)** |
| 4. Summary Structure | 3 | 5 | **V2 (+2)** |
| 5. Stats Box Format | 4 | 5 | **V2 (+1)** |
| 6. Research Grounding | 4 | 5 | **V2 (+1)** |
| 7. Prompt Quality (invitation) | 3 | 5 | **V2 (+2)** |
| **TOTAL** | **20/35** | **29/35** | **V2 wins by 9 points** |
**V2 is clearly better.** The biggest gaps are citation quality (+3) and summary structure (+2). V2's output reads like a professional research briefing; V1's reads like a decent but unstructured summary.
---
## Part 2: V1-Only Outputs Analysis
### Output 1: "open claw" (GENERAL query)
**What V1 does well:**
- Strong research grounding. Mentions exact numbers: "145,000+ GitHub stars," "20,000+ forks," "700+ skills," "341 malicious skills." These are clearly from research.
- The KEY PATTERNS section is excellent: 5 well-organized patterns with community quotes (`"I give it sudo and let it configure everything"` vs `"prompt injection is terrifying when you give the bot access to your actual bank account"`).
- Good synthesis of the security vs. enthusiasm tension -- captures the community split accurately.
- Stats box uses the emoji tree format correctly with `├──` (though note: uses double-dash `──` instead of single `─`, minor inconsistency).
**What V1 is missing (per V2 SKILL.md features):**
- No query parsing display (`🔍 **open claw** · General`).
- No inline citations in the body text. The 5 KEY PATTERNS have no `per @handle` or `per r/sub` attribution. Which Reddit thread said "I give it sudo"? Which X post raised the security concern? We do not know.
- The stats box says `├── 🟠 Reddit: 25 threads │ ~750+ upvotes` -- the tilde and plus are imprecise. V2 SKILL.md wants exact parsed numbers.
- Top voices line lists subreddits and handles but no engagement counts: `@grok, @Starlink` -- are these the highest-engagement handles? No like counts shown.
- No bold topic headers in the body -- it is a single paragraph followed by a numbered list, not the `**{Topic}** — sentence, per source` format V2 requires.
**V1 Score (estimated):** 22/35
---
### Output 2: "nano banana pro prompting" (PROMPTING query)
**What V1 does well:**
- Correctly identifies two prompting styles (JSON structured vs. natural language "Creative Director") and explains when each works best. This is excellent PROMPTING-type synthesis.
- KEY PATTERNS are specific and actionable: "85mm lens at f/1.8," "three-point lighting with key at 45 degrees," "text rendering works -- keep text under 3 words for best results (75% success rate)." These are concrete tips a user can apply immediately.
- Research grounding is strong: cites specific upvote counts ("149-259 upvotes"), subreddit names (`r/nanobanana2pro`), and the Google AI blog.
- The invitation correctly targets Nano Banana Pro: `"Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into Nano Banana Pro."`
**What V1 is missing (per V2 SKILL.md features):**
- No query parsing display.
- Stats box uses plain text dashes: `- 🟠 Reddit: 5 threads | 638 upvotes | 66 comments` instead of the tree format `├─ 🟠 Reddit:`. Uses `|` pipe instead of `│` box-drawing character. V2 SKILL.md explicitly says: "NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji."
- No inline body citations. KEY PATTERNS mention Reddit upvote ranges but no specific `per @handle` attributions.
- Missing `✅ All agents reported back!` header -- just says "All agents reported back!" without the checkmark.
- Body structure is paragraph + numbered list, not bold topic headers.
**V1 Score (estimated):** 23/35 (slightly higher than open claw due to better actionability)
---
### Output 3: "how to best setup clawdbot" (HOW-TO query)
**What V1 does well:**
- This is the best V1 output of the batch. It goes beyond synthesis and actually delivers a **Quick-Start guide** with numbered steps, a **Security Hardening** checklist, and a **Budget Option** -- all grounded in research.
- Excellent research grounding: `"per @shynxbt: Use a free AWS VPS + Claude Haiku model + Telegram bot = fully functional for $0"` -- this is an actual citation with an @handle!
- Specific, actionable recommendations: exact commands (`curl -fsSL https://clawd.bot/install.sh | bash`), specific model recommendations (Claude Opus 4.5 for best results, GLM 4.7 Flash for local), specific channel advice (Telegram first, WhatsApp QR code fails).
- Stats box is correct emoji tree format with engagement counts: `@aashatwt (452 likes), @recap_david (329 likes)`.
- Captures the naming confusion accurately: "Clawdbot -> Moltbot -> OpenClaw."
**What V1 is missing (per V2 SKILL.md features):**
- No query parsing display.
- Body text has no inline citations except the Budget Option section. The 5 KEY PATTERNS have no `per @handle` attribution.
- Bold topic headers are used only in the Quick-Start and Security sections, not in the KEY PATTERNS or intro.
- The output delivers the "answer" directly (setup guide) rather than waiting for the user's vision and offering to write a prompt. For a HOW-TO query this might be the right call, but it skips the SKILL.md flow of "show research -> invite vision -> write prompt."
**V1 Score (estimated):** 26/35 (best of the V1 outputs)
---
### Patterns Across All V1 Outputs
**Consistent strengths:**
1. Research grounding is solid across all three. V1 does not hallucinate -- the facts are clearly from the research output, not pre-training.
2. KEY PATTERNS lists are consistently useful and actionable.
3. Stats boxes are present in all outputs (though formatting varies).
4. The invitation/closing line is present in all outputs.
**Consistent weaknesses:**
1. **No query parsing display** in any output (0 for 4, including Kanye West).
2. **No inline citations** in the body text (except one @handle in the clawdbot output). The research feels real but is unattributed.
3. **Stats box formatting is inconsistent.** Open claw uses `├──` (double dash), nano banana pro uses `- 🟠` (plain dash + pipe), clawdbot uses `├─` (correct). Three different formats in three outputs.
4. **Body structure defaults to paragraph + numbered list** instead of bold topic headers. Only clawdbot partially uses bold headers (in the guide section, not the research section).
5. **No `(via Bird/xAI)` notation** on X stats in any output.
---
## Part 3: SKILL.md Feature Diff
### Features in V2 but NOT V1
| Feature | V2 Lines | Impact |
|---------|----------|--------|
| **Query parsing display** (`🔍 **{TOPIC}** · {QUERY_TYPE}`) | 40-53 | HIGH -- confirms to user the skill understood their request before spending time on research. |
| **Sparse citation rules** with BAD/GOOD examples | 186-193 | HIGH -- this is the #1 quality differentiator in the Kanye head-to-head. `"per @handle"` format, never chain multiple citations. |
| **Bold topic headers** template (`**{Topic 1}** — [1-2 sentences, per source]`) | 195-208 | HIGH -- makes output scannable. |
| **Strict stats template** with "NEVER use plain text dashes" instruction | 217-230 | MEDIUM -- prevents the formatting inconsistency seen across V1 outputs. |
| **RECOMMENDATIONS source attribution** (each item MUST have Sources: line with @handles) | 178-182 | MEDIUM -- only affects RECOMMENDATIONS queries. |
| **Reddit 0 results handling** (explicit instruction for what to write) | 229 | LOW -- edge case, but prevents ad-hoc text like V1's "filtered out noise." |
| **Bird CLI / xAI notation** in stats | 223 | LOW -- cosmetic transparency about data source. |
| **Step 2 phrasing: "DO WEBSEARCH WHILE SCRIPT RUNS"** | 71-73 | LOW -- execution optimization, no output impact. |
### Features in V1 but NOT V2
| Feature | V1 Lines | Impact | Should Restore? |
|---------|----------|--------|-----------------|
| **Use cases block** (4 examples in intro) | 12-17 | LOW | No |
| **Setup Check section** (3 modes, bash script, "keys are OPTIONAL") | 50-78 | MEDIUM for new users | Yes, for public release |
| **BAD/GOOD synthesis anti-pattern examples** | 172-191 | MEDIUM-HIGH | YES |
| **Self-check instruction** ("Re-read your 'What I learned' section...") | 269 | MEDIUM | YES |
| **Quality Checklist** (5-point checklist before delivering prompt) | 306-324 | HIGH | YES |
| **Prompt format anti-pattern** ("Research says JSON but you write prose") | 302 | MEDIUM | YES |
| **"IF USER ASKS FOR MORE OPTIONS"** section | 327-329 | LOW-MEDIUM | YES |
| **Web-only mode stats template + promo** | 248-259 | MEDIUM for no-key users | For public release |
| **TARGET_TOOL question template** (4 options) | 272-280 | LOW | No |
| **Context Memory: explicit "don't re-search" instructions** | 342-358 | MEDIUM | YES |
| **Output footer emoji + engagement counts** | 366-380 | LOW | YES |
### Features in BOTH (Shared)
| Feature | Notes |
|---------|-------|
| Parse User Intent (TOPIC, TARGET_TOOL, QUERY_TYPE) | Same 4 query types, same detection logic |
| "Don't ask about tool before research" rule | Identical |
| Research script execution command | Same `python3` command |
| WebSearch queries by QUERY_TYPE | Same search strategies |
| "Use user's exact terminology" instruction | V2 shorter but same intent |
| Judge Agent synthesis logic | Same 5-step weighting process |
| "Ground in actual research" instruction | Same core instruction, V1 has more examples |
| RECOMMENDATIONS: extract specific names | Same logic |
| Prompt format matching | Same instruction |
| Wait for user's vision | Same |
| Write ONE perfect prompt | Same structure |
| Context Memory | V2 shorter version |
| Output summary footer | Both have it, V1 has emoji |
| Depth options (quick/default/deep) | Same |
| "After each prompt: Stay in Expert Mode" | Same |
### Overall Assessment
**V2 is a clear upgrade in output formatting and citation quality.** The three features V2 adds (query parsing display, sparse citation rules, bold topic headers) directly address the three biggest weaknesses seen across all V1 outputs. The Kanye West head-to-head proves it: V2 scores 29/35 vs V1's 20/35.
**However, V2 dropped several quality guardrails from V1** that do not affect formatting but affect *correctness*: the self-check instruction, the anti-pattern examples, the quality checklist for prompts, and the "don't re-search" context memory rule. These are cheap to restore (under 25 lines total) and protect against subtle failure modes that may not show up in a 1-query test but will appear over dozens of uses.
---
## Part 4: Verdict
### Ship V2 or Not?
**Ship V2 -- but restore the guardrails first.**
V2 is unambiguously better on every formatting dimension. The citation quality improvement alone (V1: 2/5 -> V2: 5/5) makes it worth shipping. The bold topic headers and strict stats template fix the inconsistency problems visible across all V1 outputs.
But V2 dropped 6 guardrail features from V1 that cost almost nothing to include and protect against real failure modes. These should be restored before V2 goes public.
### Remaining Gaps
**Must fix before shipping (affects correctness):**
1. **Restore the quality checklist for prompts.** This is the test plan's #1 priority item. V1 had a 5-point checklist; V2 reduced it to one line. The checklist is what makes prompts feel polished -- it is the "that's a great prompt" mechanism. Add 8 lines.
2. **Restore BAD/GOOD anti-pattern examples.** V2 says "ground in actual research" but does not show what *bad* grounding looks like. V1's ClawdBot/Claude Code conflation example is exactly the kind of concrete negative example that prevents real failures. Add 5 lines.
3. **Restore self-check instruction.** One sentence: "Re-read your 'What I learned' section -- does it match what the research ACTUALLY says?" Zero cost, catches hallucination. Add 2 lines.
4. **Restore "don't re-search" context memory rule.** V2 only says "only do new research if user asks about a DIFFERENT topic." V1 explicitly bans re-searching and tells the agent to answer from existing research. Add 3 lines.
**Should fix (polish):**
5. Restore prompt format anti-pattern ("Research says JSON but you write prose"). Add 2 lines.
6. Restore "IF USER ASKS FOR MORE OPTIONS" section. Add 2 lines.
7. Add emoji + engagement counts back to the output summary footer. Edit 3 lines.
**Skip for now:**
8. Setup Check section -- add back for public release, not needed for execution.
9. Web-only mode stats template -- lower priority, most testers have API keys.
10. TARGET_TOOL question template -- agent handles this naturally.
### Query Parsing Display: Investigate
Both V1 and V2 scored 1/5 on query parsing display. V2 has the feature in its SKILL.md but the agent did not render it in the captured output. This could mean:
- The display was shown during execution but not captured (likely -- it appears before tools run, and the output files may only contain post-research content).
- The instruction is not strong enough and the agent skips it.
**Recommendation:** Verify in a live session whether the parsing display actually appears. If it does not, strengthen the instruction (e.g., "This line MUST be the first thing you output, before any tool calls").
### Total Effort
Restoring all 7 priority items: approximately 25 lines added to V2 SKILL.md. Under 15 minutes of work. The V2 formatting wins are substantial and proven; the V1 guardrails are small and proven. Combining both produces the best version.
### Final Score Summary
| | V1 (Kanye) | V2 (Kanye) | Delta |
|--|-----------|-----------|-------|
| Total | 20/35 | 29/35 | **V2 +9** |
| | V1 (Open Claw) | V1 (Nano Banana) | V1 (Clawdbot) | V1 Average |
|--|---------------|-----------------|--------------|------------|
| Estimated Total | 22/35 | 23/35 | 26/35 | **23.7/35** |
V2 at 29/35 beats every V1 output, including V1's best (clawdbot at 26/35).
**Decision: Ship V2 with guardrails restored.**
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---
name: last30days
description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
argument-hint: "[topic] for [tool]" or "[topic]"
allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
---
# last30days: Research Any Topic from the Last 30 Days
Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
Use cases:
- **Prompting**: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
- **Recommendations**: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
- **News**: "what's happening with OpenAI", "latest AI announcements" → current events and updates
- **General**: any topic you're curious about → understand what the community is saying
## CRITICAL: Parse User Intent
Before doing anything, parse the user's input for:
1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
3. **QUERY TYPE**: What kind of research they want:
- **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
- **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
- **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
- **GENERAL** - anything else → User wants broad understanding of the topic
Common patterns:
- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
**IMPORTANT: Do NOT ask about target tool before research.**
- If tool is specified in the query, use it
- If tool is NOT specified, run research first, then ask AFTER showing results
**Store these variables:**
- `TOPIC = [extracted topic]`
- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
---
## Setup Check
The skill works in three modes based on available API keys:
1. **Full Mode** (both keys): Reddit + X + WebSearch - best results with engagement metrics
2. **Partial Mode** (one key): Reddit-only or X-only + WebSearch
3. **Web-Only Mode** (no keys): WebSearch only - still useful, but no engagement metrics
**API keys are OPTIONAL.** The skill will work without them using WebSearch fallback.
### First-Time Setup (Optional but Recommended)
If the user wants to add API keys for better results:
```bash
mkdir -p ~/.config/last30days
cat > ~/.config/last30days/.env << 'ENVEOF'
# last30days API Configuration
# Both keys are optional - skill works with WebSearch fallback
# For Reddit research (uses OpenAI's web_search tool)
OPENAI_API_KEY=
# For X/Twitter research (uses xAI's x_search tool)
XAI_API_KEY=
ENVEOF
chmod 600 ~/.config/last30days/.env
echo "Config created at ~/.config/last30days/.env"
echo "Edit to add your API keys for enhanced research."
```
**DO NOT stop if no keys are configured.** Proceed with web-only mode.
---
## Research Execution
**IMPORTANT: The script handles API key detection automatically.** Run it and check the output to determine mode.
**Step 1: Run the research script**
```bash
python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
```
The script will automatically:
- Detect available API keys
- Show a promo banner if keys are missing (this is intentional marketing)
- Run Reddit/X searches if keys exist
- Signal if WebSearch is needed
**Step 2: Check the output mode**
The script output will indicate the mode:
- **"Mode: both"** or **"Mode: reddit-only"** or **"Mode: x-only"**: Script found results, WebSearch is supplementary
- **"Mode: web-only"**: No API keys, Claude must do ALL research via WebSearch
**Step 3: Do WebSearch**
For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
Choose search queries based on QUERY_TYPE:
**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
- Search for: `best {TOPIC} recommendations`
- Search for: `{TOPIC} list examples`
- Search for: `most popular {TOPIC}`
- Goal: Find SPECIFIC NAMES of things, not generic advice
**If NEWS** ("what's happening with X", "X news"):
- Search for: `{TOPIC} news 2026`
- Search for: `{TOPIC} announcement update`
- Goal: Find current events and recent developments
**If PROMPTING** ("X prompts", "prompting for X"):
- Search for: `{TOPIC} prompts examples 2026`
- Search for: `{TOPIC} techniques tips`
- Goal: Find prompting techniques and examples to create copy-paste prompts
**If GENERAL** (default):
- Search for: `{TOPIC} 2026`
- Search for: `{TOPIC} discussion`
- Goal: Find what people are actually saying
For ALL query types:
- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
- If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
- Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
- Your knowledge may be outdated - trust the user's terminology
- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
- INCLUDE: blogs, tutorials, docs, news, GitHub repos
- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
**Step 3: Wait for background script to complete**
Use TaskOutput to get the script results before proceeding to synthesis.
**Depth options** (passed through from user's command):
- `--quick` → Faster, fewer sources (8-12 each)
- (default) → Balanced (20-30 each)
- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
---
## Judge Agent: Synthesize All Sources
**After all searches complete, internally synthesize (don't display stats yet):**
The Judge Agent must:
1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
2. Weight WebSearch sources LOWER (no engagement data)
3. Identify patterns that appear across ALL three sources (strongest signals)
4. Note any contradictions between sources
5. Extract the top 3-5 actionable insights
**Do NOT display stats here - they come at the end, right before the invitation.**
---
## FIRST: Internalize the Research
**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
Read the research output carefully. Pay attention to:
- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
- **What the sources actually say**, not what you assume the topic is about
**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
### If QUERY_TYPE = RECOMMENDATIONS
**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
When user asks "best X" or "top X", they want a LIST of specific things:
- Scan research for specific product names, tool names, project names, skill names, etc.
- Count how many times each is mentioned
- Note which sources recommend each (Reddit thread, X post, blog)
- List them by popularity/mention count
**BAD synthesis for "best Claude Code skills":**
> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
**GOOD synthesis for "best Claude Code skills":**
> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
### For all QUERY_TYPEs
Identify from the ACTUAL RESEARCH OUTPUT:
- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords? THIS IS CRITICAL.
- The top 3-5 patterns/techniques that appeared across multiple sources
- Specific keywords, structures, or approaches mentioned BY THE SOURCES
- Common pitfalls mentioned BY THE SOURCES
**If research says "use JSON prompts" or "structured prompts", you MUST deliver prompts in that format later.**
---
## THEN: Show Summary + Invite Vision
**CRITICAL: Do NOT output any "Sources:" lists. The final display should be clean.**
**Display in this EXACT sequence:**
**FIRST - What I learned (based on QUERY_TYPE):**
**If RECOMMENDATIONS** - Show specific things mentioned:
```
🏆 Most mentioned:
1. [Specific name] - mentioned {n}x (r/sub, @handle, blog.com)
2. [Specific name] - mentioned {n}x (sources)
3. [Specific name] - mentioned {n}x (sources)
4. [Specific name] - mentioned {n}x (sources)
5. [Specific name] - mentioned {n}x (sources)
Notable mentions: [other specific things with 1-2 mentions]
```
**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
```
What I learned:
[2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT.]
KEY PATTERNS I'll use:
1. [Pattern from research]
2. [Pattern from research]
3. [Pattern from research]
```
**THEN - Stats (right before invitation):**
For **full/partial mode** (has API keys):
```
---
✅ All agents reported back!
├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments
├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts
├─ 🌐 Web: {n} pages │ {domains}
└─ Top voices: r/{sub1}, r/{sub2} │ @{handle1}, @{handle2} │ {web_author} on {site}
```
For **web-only mode** (no API keys):
```
---
✅ Research complete!
├─ 🌐 Web: {n} pages │ {domains}
└─ Top sources: {author1} on {site1}, {author2} on {site2}
💡 Want engagement metrics? Add API keys to ~/.config/last30days/.env
- OPENAI_API_KEY → Reddit (real upvotes & comments)
- XAI_API_KEY → X/Twitter (real likes & reposts)
```
**LAST - Invitation:**
```
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
```
**Use real numbers from the research output.** The patterns should be actual insights from the research, not generic advice.
**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If the research was about ClawdBot (a self-hosted AI agent), your summary should be about ClawdBot, not Claude Code. If you catch yourself projecting your own knowledge instead of the research, rewrite it.
**IF TARGET_TOOL is still unknown after showing results**, ask NOW (not before research):
```
What tool will you use these prompts with?
Options:
1. [Most relevant tool based on research - e.g., if research mentioned Figma/Sketch, offer those]
2. Nano Banana Pro (image generation)
3. ChatGPT / Claude (text/code)
4. Other (tell me)
```
**IMPORTANT**: After displaying this, WAIT for the user to respond. Don't dump generic prompts.
---
## WAIT FOR USER'S VISION
After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
When they respond with their vision (e.g., "I want a landing page mockup for my SaaS app"), THEN write a single, thoughtful, tailored prompt.
---
## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
### CRITICAL: Match the FORMAT the research recommends
**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT:**
- Research says "JSON prompts" → Write the prompt AS JSON
- Research says "structured parameters" → Use structured key: value format
- Research says "natural language" → Use conversational prose
- Research says "keyword lists" → Use comma-separated keywords
**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
### Output Format:
```
Here's your prompt for {TARGET_TOOL}:
---
[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS - if research said JSON, this is JSON. If research said natural language, this is prose. Match what works.]
---
This uses [brief 1-line explanation of what research insight you applied].
```
### Quality Checklist:
- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
- [ ] Directly addresses what the user said they want to create
- [ ] Uses specific patterns/keywords discovered in research
- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
- [ ] Appropriate length and style for TARGET_TOOL
---
## IF USER ASKS FOR MORE OPTIONS
Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
---
## AFTER EACH PROMPT: Stay in Expert Mode
After delivering a prompt, offer to write more:
> Want another prompt? Just tell me what you're creating next.
---
## CONTEXT MEMORY
For the rest of this conversation, remember:
- **TOPIC**: {topic}
- **TARGET_TOOL**: {tool}
- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
- **RESEARCH FINDINGS**: The key facts and insights from the research
**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
When the user asks follow-up questions:
- **DO NOT run new WebSearches** - you already have the research
- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
- **If they ask for a prompt** - write one using your expertise
- **If they ask a question** - answer it from your research findings
Only do new research if the user explicitly asks about a DIFFERENT topic.
---
## Output Summary Footer (After Each Prompt)
After delivering a prompt, end with:
For **full/partial mode**:
```
---
📚 Expert in: {TOPIC} for {TARGET_TOOL}
📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
Want another prompt? Just tell me what you're creating next.
```
For **web-only mode**:
```
---
📚 Expert in: {TOPIC} for {TARGET_TOOL}
📊 Based on: {n} web pages from {domains}
Want another prompt? Just tell me what you're creating next.
💡 Unlock Reddit & X data: Add API keys to ~/.config/last30days/.env
```
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## What I learned:
The last 30 days have been one of Kanye West's most significant periods in years. The biggest story is his **full-page Wall Street Journal apology** (Jan 26, 2026) titled "To Those I've Hurt" — a comprehensive open letter addressing his antisemitic remarks and erratic behavior. He attributed his actions to a "four-month-long manic episode" and revealed that a car accident 25 years ago caused a frontal-lobe brain injury that wasn't properly diagnosed until 2023, leading to his bipolar type-1 diagnosis. Multiple sources describe wife **Bianca Censori** as instrumental in getting him to seek treatment during a manic depressive episode.
On the music side, he's announced **two upcoming albums**: *BULLY* (now scheduled for March 20, 2026) and *IN A PERFECT WORLD*. Daughter **North West** collaborated with him on a track called "Piercings on My Hand." He performed a massive show in Mexico City (Jan 30) at the Monumental Plaza de Toros, and he'll headline the new **Hellwatt Festival** in Italy (July 4-18, 2026).
The **2026 Grammys** remain contentious — he's reportedly not invited after last year's red carpet drama. Health concerns also surfaced after a January LA outing where he appeared "noticeably bloated and moving with sluggish energy."
**KEY THEMES:**
1. **Accountability arc** — The WSJ apology is being widely discussed as either genuine growth or calculated PR
2. **Mental health transparency** — Brain injury + bipolar diagnosis framing his past behavior
3. **Bianca Censori's role** — Increasingly seen as a stabilizing force (despite continued provocative public appearances)
4. **Music comeback** — Two albums announced, active touring in Latin America and Europe
5. **Grammy exile** — Still persona non grata at major award shows
---
✅ All agents reported back!
├─ 🟠 Reddit: 0 relevant threads (filtered out noise)
├─ 🔵 X: 30 posts │ ~10 likes │ casual fan chatter + Bianca Censori discourse
├─ 🌐 Web: 20+ pages │ washingtonpost.com, billboard.com, allhiphop.com, euronews.com, rap-up.com
└─ Top voices: Washington Post, Billboard, AllHipHop, Euronews, The Mirror
---
Want to dive deeper into any of these threads — the apology, the new albums, the Grammys situation, or Bianca Censori? Just tell me what angle you're interested in.
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## What I learned:
OpenClaw (formerly ClawdBot, formerly Moltbot) is an open-source autonomous AI agent created by Peter Steinberger that went massively viral in late January 2026 — hitting 145,000+ GitHub stars and 20,000+ forks. It runs locally on your machine, connects to messaging platforms (Telegram, WhatsApp, Discord, Signal, etc.), and uses LLMs like Claude, GPT, or Gemini to actually perform tasks on your computer — file management, scheduling, web browsing, purchases, and more. Think "JARVIS for everyone."
The community is simultaneously excited and alarmed. The "ClawHub" skill marketplace has 700+ skills but a VirusTotal/Koi Security audit found **341 malicious skills** doing data exfiltration and crypto theft. A high-severity RCE vulnerability was also disclosed. The #1 most downloaded skill on ClawHub was confirmed malware.
**KEY PATTERNS from the research:**
1. **Cost is the top complaint** — API usage burns money fast; many users recommend Gemini CLI (free 1,000 req/day) over Claude/GPT for OpenClaw to control spend
2. **Security is a dumpster fire** — Supply chain attacks via ClawHub skills, prompt injection risks when browsing the web, RCE vulnerabilities, and orgs are banning it at the EDR level
3. **Setup is nontrivial** — Even after install, most users get stuck on skills/tools/automation config; the `openclaw onboard` wizard is the recommended path
4. **The skill ecosystem is powerful but dangerous** — 700+ skills available, but no code signing, no real moderation, and active malware campaigns
5. **Community is split** — Enthusiasts love the autonomy ("I give it sudo and let it configure everything"), while security folks are terrified ("prompt injection is terrifying when you give the bot access to your actual bank account")
---
✅ All agents reported back!
├── 🟠 Reddit: 25 threads │ ~750+ upvotes │ ~600+ comments
├── 🔵 X: 27 posts │ 1,750+ likes │ 325+ reposts
├── 🌐 Web: 20+ pages │ Wikipedia, Bloomberg, The Register, Hacker News, Nature, VirusTotal, DigitalOcean, Codecademy, Tom's Hardware
└── Top voices: r/openclaw, r/cybersecurity, r/clawdbot, r/ArtificialInteligence │ @grok, @Starlink │ Nature, Bloomberg, ACM
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly.
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Now I have a comprehensive picture. Let me synthesize everything.
**What I learned:**
Nano Banana Pro is fundamentally different from older image models — it's a "thinking" model that understands intent, physics, and composition. The community has converged on two distinct prompting styles, and **both work well** depending on the use case:
1. **JSON structured prompts** — Dominant on Reddit (r/nanobanana2pro) and X. Power users share elaborate JSON objects with nested fields for subject, appearance, lighting, composition, and style. These produce the most consistent, controlled results for portraits, editorials, and product shots.
2. **Natural language "Creative Director" prompts** — Recommended by Google's official guide. Full sentences describing the scene as if briefing a photographer. Better for cinematic shots, complex edits, and iterative refinement.
**KEY PATTERNS I'll use:**
1. **JSON format for maximum control** — The top Reddit posts (149-259 upvotes) all use structured JSON with nested objects: `subject > appearance > hair/skin/expression`, `lighting > key/fill/rim`, `camera > lens/angle/depth_of_field`
2. **Be a Creative Director, not a tag spammer** — Replace "4k, realistic, beautiful" with specific technical directions: "85mm lens at f/1.8", "three-point lighting with key at 45°", "shallow depth of field with bokeh"
3. **Provide context for WHY** — Adding purpose ("for a luxury fashion editorial", "for a high-end gourmet cookbook") helps the model infer appropriate styling, plating, lighting
4. **Edit, don't regenerate** — When 80% is right, ask for the specific change conversationally
5. **Text rendering works** — Keep text under 3 words for best results (75% success rate), specify font style and placement
---
All agents reported back!
- 🟠 Reddit: 5 threads | 638 upvotes | 66 comments
- 🔵 X: 29 posts | 312 likes | 117 reposts
- 🌐 Web: 10+ pages | blog.google, dev.to, eweek.com, imagine.art, github.com, leonardo.ai, medium.com
- Top voices: r/nanobanana2pro | @Xmira_belle, @doctorwasif, @chatgptpaglu | Google AI blog, DEV Community
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into Nano Banana Pro.
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**What I learned:**
Clawdbot (now officially renamed **OpenClaw**) is a self-hosted, open-source personal AI assistant that connects to messaging apps (Telegram, WhatsApp, Discord, Slack, Signal, iMessage) and uses LLMs to act as an autonomous agent on your behalf. It's roughly one week old as a public project and the community is moving fast — the name has already changed twice (Clawdbot → Moltbot → OpenClaw).
**KEY PATTERNS from the research:**
1. **Security is the #1 concern** — Multiple Reddit threads and guides warn that many setups are wide open to the internet with zero auth. Run it on dedicated/isolated hardware (VM, Mac Mini, NAS), never your main machine. Block dangerous commands via allowlist. Never add it to group chats.
2. **Use the onboarding wizard, not Docker** — Docker was reported as unreliable (shims, bridges). The single-line install script (`curl -fsSL https://clawd.bot/install.sh | bash`) followed by `openclaw onboard` is the smoothest path. Docker is a "no-go" per multiple users.
3. **Model choice matters a lot** — Claude Opus 4.5 is recommended for best results (trained to resist prompt injection, ~99% resistance). For budget setups, Claude Haiku works at $0. Local models (Ollama/LM Studio) work but cause hallucination and token issues — GLM 4.7 Flash (30B) was recommended as the best local option.
4. **Telegram is the easiest channel to start with** — Most guides and users start with a Telegram bot token. WhatsApp QR code pairing fails frequently. Get Telegram working first, then expand.
5. **Sandbox and isolate** — Set `agents.defaults.sandbox.mode` to `"non-main"` for Docker sandboxing. Use a Linux VM or UTM on Mac to segregate traffic. Tailscale for secure remote access.
---
✅ All agents reported back!
├─ 🟠 Reddit: 19 threads │ 415+ upvotes │ 290+ comments
├─ 🔵 X: 29 posts │ 815+ likes │ 65+ reposts
├─ 🌐 Web: 10+ pages │ Medium, DataCamp, DEV, GitHub, QNAP, docs.openclaw.ai
└─ Top voices: r/clawdbot, r/LocalLLM, r/AskVibecoders │ @aashatwt (452 likes), @recap_david (329 likes) │ Lukas Niessen on Medium
---
**Here's the consensus best setup path based on what the community actually recommends:**
### Quick-Start (30 min)
1. **Get dedicated hardware** — Mac Mini, old laptop, NAS (QNAP has an official guide), or a VPS (DigitalOcean gist available). Don't use your daily driver.
2. **Install via script**: `curl -fsSL https://clawd.bot/install.sh | bash`
3. **Run the wizard**: `openclaw onboard` — it walks through gateway, workspace, channels, and skills
4. **Connect Telegram first** — create a bot via @BotFather, paste the token
5. **Set your API key** — Anthropic API key for Claude, or connect Ollama for local models
6. **Customize SOUL.md** — this controls your bot's personality and behavior
### Security Hardening (do this immediately)
- Bind gateway to localhost only, use Tailscale for remote access
- Set sandbox mode to `"non-main"`
- Create an explicit command allowlist (block `rm -rf`, `sudo`, `chmod`)
- Rotate API keys every 90 days
- Never add the bot to group chats
### Budget Option ($0)
Per @shynxbt: Use a free AWS VPS + Claude Haiku model + Telegram bot = fully functional for $0.
---
Want me to help you with a specific part of the setup, or do you have a particular use case in mind (home automation, CRM, coding assistant, etc.)?
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---
name: last30days
description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
argument-hint: '"[topic] for [tool]" or "[topic]"'
allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
---
# last30days: Research Any Topic from the Last 30 Days
Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
## CRITICAL: Parse User Intent
Before doing anything, parse the user's input for:
1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
3. **QUERY TYPE**: What kind of research they want:
- **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
- **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
- **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
- **GENERAL** - anything else → User wants broad understanding of the topic
Common patterns:
- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
**IMPORTANT: Do NOT ask about target tool before research.**
- If tool is specified in the query, use it
- If tool is NOT specified, run research first, then ask AFTER showing results
**Store these variables:**
- `TOPIC = [extracted topic]`
- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
**DISPLAY your parsing to the user.** Before running any tools, output a single line:
🔍 **{TOPIC}** · {QUERY_TYPE}
Searching Reddit, X, and the web for {natural language description of what you'll look for}...
Example outputs:
- 🔍 **kanye west** · News — Searching Reddit, X, and the web for the latest kanye west news and discussions...
- 🔍 **best MCP servers** · Recommendations — Searching Reddit, X, and the web for the most recommended MCP servers...
- 🔍 **nano banana pro prompting** · Prompting — Searching Reddit, X, and the web for nano banana pro prompting techniques and tips...
- 🔍 **open claw** · General — Searching Reddit, X, and the web for what people are saying about open claw...
If TARGET_TOOL is known, mention it: "...for nano banana pro prompting techniques to use in ChatGPT..."
This text MUST appear before you call any tools. It confirms to the user that you understood their request.
---
## Research Execution
**Step 1: Run the research script**
```bash
python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
```
The script will automatically:
- Detect available API keys
- Run Reddit/X searches if keys exist
- Signal if WebSearch is needed
---
## STEP 2: DO WEBSEARCH WHILE SCRIPT RUNS
The script auto-detects sources (Bird CLI, API keys, etc). While waiting for it, do WebSearch.
For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
Choose search queries based on QUERY_TYPE:
**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
- Search for: `best {TOPIC} recommendations`
- Search for: `{TOPIC} list examples`
- Search for: `most popular {TOPIC}`
- Goal: Find SPECIFIC NAMES of things, not generic advice
**If NEWS** ("what's happening with X", "X news"):
- Search for: `{TOPIC} news 2026`
- Search for: `{TOPIC} announcement update`
- Goal: Find current events and recent developments
**If PROMPTING** ("X prompts", "prompting for X"):
- Search for: `{TOPIC} prompts examples 2026`
- Search for: `{TOPIC} techniques tips`
- Goal: Find prompting techniques and examples to create copy-paste prompts
**If GENERAL** (default):
- Search for: `{TOPIC} 2026`
- Search for: `{TOPIC} discussion`
- Goal: Find what people are actually saying
For ALL query types:
- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
- INCLUDE: blogs, tutorials, docs, news, GitHub repos
- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
**Depth options** (passed through from user's command):
- `--quick` → Faster, fewer sources (8-12 each)
- (default) → Balanced (20-30 each)
- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
---
## Judge Agent: Synthesize All Sources
**After all searches complete, internally synthesize (don't display stats yet):**
The Judge Agent must:
1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
2. Weight WebSearch sources LOWER (no engagement data)
3. Identify patterns that appear across ALL three sources (strongest signals)
4. Note any contradictions between sources
5. Extract the top 3-5 actionable insights
**Do NOT display stats here - they come at the end, right before the invitation.**
---
## FIRST: Internalize the Research
**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
Read the research output carefully. Pay attention to:
- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
- **What the sources actually say**, not what you assume the topic is about
**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
### If QUERY_TYPE = RECOMMENDATIONS
**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
When user asks "best X" or "top X", they want a LIST of specific things:
- Scan research for specific product names, tool names, project names, skill names, etc.
- Count how many times each is mentioned
- Note which sources recommend each (Reddit thread, X post, blog)
- List them by popularity/mention count
**BAD synthesis for "best Claude Code skills":**
> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
**GOOD synthesis for "best Claude Code skills":**
> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
### For all QUERY_TYPEs
Identify from the ACTUAL RESEARCH OUTPUT:
- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords?
- The top 3-5 patterns/techniques that appeared across multiple sources
- Specific keywords, structures, or approaches mentioned BY THE SOURCES
- Common pitfalls mentioned BY THE SOURCES
---
## THEN: Show Summary + Invite Vision
**Display in this EXACT sequence:**
**FIRST - What I learned (based on QUERY_TYPE):**
**If RECOMMENDATIONS** - Show specific things mentioned with sources:
```
🏆 Most mentioned:
[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle1, @handle2, r/sub, blog.com
[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle3, r/sub2, Complex
Notable mentions: [other specific things with 1-2 mentions]
```
**CRITICAL for RECOMMENDATIONS:**
- Each item MUST have a "Sources:" line with actual @handles from X posts (e.g., @LONGLIVE47, @ByDobson)
- Include subreddit names (r/hiphopheads) and web sources (Complex, Variety)
- Parse @handles from research output and include the highest-engagement ones
- Format naturally - tables work well for wide terminals, stacked cards for narrow
**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
CITATION RULE: Cite sources sparingly to prove research is real.
- In the "What I learned" intro: cite 1-2 top sources total, not every sentence
- In KEY PATTERNS: cite 1 source per pattern, short format: "per @handle" or "per r/sub"
- Do NOT include engagement metrics in citations (likes, upvotes) - save those for stats box
- Do NOT chain multiple citations: "per @x, @y, @z" is too much. Pick the strongest one.
**BAD:** "His album is set for March 20 (per @cocoabutterbf; Rolling Stone; HotNewHipHop; Complex)."
**GOOD:** "His album BULLY is set for March 20 via Gamma, per Rolling Stone."
```
What I learned:
**{Topic 1}** — [1-2 sentences about this storyline, per source]
**{Topic 2}** — [1-2 sentences, per source]
**{Topic 3}** — [1-2 sentences, per source]
KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per source
```
**THEN - Stats (right before invitation):**
**CRITICAL: Calculate actual totals from the research output.**
- Count posts/threads from each section
- Sum engagement: parse `[Xlikes, Yrt]` from each X post, `[Xpts, Ycmt]` from Reddit
- Identify top voices: highest-engagement @handles from X, most active subreddits
**Copy this EXACTLY, replacing only the {placeholders}:**
```
---
✅ All agents reported back!
├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts (via Bird/xAI)
├─ 🌐 Web: {N} pages │ {domain1}, {domain2}, {domain3}
└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
---
```
If Reddit returned 0 threads, write: "├─ 🟠 Reddit: 0 threads (no results this cycle)"
NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.
**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If you catch yourself projecting your own knowledge instead of the research, rewrite it.
**LAST - Invitation:**
```
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
```
---
## WAIT FOR USER'S VISION
After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
---
## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
### CRITICAL: Match the FORMAT the research recommends
**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT.**
**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
### Quality Checklist (run before delivering):
- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
- [ ] Directly addresses what the user said they want to create
- [ ] Uses specific patterns/keywords discovered in research
- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
- [ ] Appropriate length and style for TARGET_TOOL
### Output Format:
```
Here's your prompt for {TARGET_TOOL}:
---
[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS]
---
This uses [brief 1-line explanation of what research insight you applied].
```
---
## IF USER ASKS FOR MORE OPTIONS
Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
---
## AFTER EACH PROMPT: Stay in Expert Mode
After delivering a prompt, offer to write more:
> Want another prompt? Just tell me what you're creating next.
---
## CONTEXT MEMORY
For the rest of this conversation, remember:
- **TOPIC**: {topic}
- **TARGET_TOOL**: {tool}
- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
- **RESEARCH FINDINGS**: The key facts and insights from the research
**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
When the user asks follow-up questions:
- **DO NOT run new WebSearches** - you already have the research
- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
- **If they ask for a prompt** - write one using your expertise
Only do new research if the user explicitly asks about a DIFFERENT topic.
---
## Output Summary Footer (After Each Prompt)
After delivering a prompt, end with:
```
---
📚 Expert in: {TOPIC} for {TARGET_TOOL}
📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
Want another prompt? Just tell me what you're creating next.
```
docs/test-results/v1-vs-v2-quick/v2/1-kanye-west.txt
+25
View File
@@ -0,0 +1,25 @@
Here's what I found:
## What I learned:
**BULLY Album — March 20, 2026 via Gamma** — After years of delays (first announced September 2024), Kanye's 12th studio album finally has a firm release date. The 13-track project features Peso Pluma, Playboi Carti, and Ty Dolla Sign. Sonically it recalls *808s & Heartbreak* and *MBDTF* — Ye mostly sings rather than raps. Notably, earlier leak versions used AI-deepfaked vocals, which have reportedly been re-recorded with his real voice for the official release, per Rolling Stone.
**Public Apology for Antisemitism** — On January 26, Ye took out a full-page Wall Street Journal ad titled "To Those I've Hurt," publicly apologizing for his antisemitic remarks. He disclosed a previously undiagnosed brain injury from his 2002 car accident and attributed his behavior to a four-month manic episode fueled by bipolar disorder, psychosis, and paranoia. "I lost touch with reality," he wrote. The apology also extended to the Black community, per The Washington Post.
**Hellwatt Festival in Italy** — Ye is headlining a brand-new festival at the 103,000-capacity RCF Arena in Italy over three weekends from July 4-18, 2026 — his first-ever live concert in Italy, per Billboard.
**Health Concerns** — A rare January 2026 outing in LA with Bianca Censori reignited concern about Ye's physical appearance. Insiders point to medication side effects, frequent travel, and inconsistent routines, per AllHipHop.
**Grammys Ban** — Ye is reportedly not welcome at the 2026 Grammy Awards after clashing with organizers last year over his invitation terms, per The News International.
**Kim & Lewis Hamilton Buzz** — X chatter is heavily focused on Kim Kardashian's relationship with Lewis Hamilton, with users contrasting her new relationship against her marriage to Ye.
---
✅ All agents reported back!
├─ 🟠 Reddit: 0 threads (no results this cycle)
├─ 🔵 X: 29 posts │ 33 likes │ 14 reposts (via xAI)
├─ 🌐 Web: 30+ pages │ rollingstone.com, washingtonpost.com, complex.com, billboard.com, npr.org
└─ 🗣️ Top voices: @honest30bgfan_ (33 likes), @HipHopCrave_ │ Rolling Stone, Washington Post, Complex
---
Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into your tool of choice.