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feat(open): create open variant SKILL.md and reference files

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Router SKILL.md dispatches to mode-specific references:
- research.md: one-shot research with --store for persistence
- watchlist.md: add/remove/list/run topics
- briefing.md: daily/weekly briefing generation
- history.md: query/search accumulated findings
- context.md: agent memory template

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Matt Van Horn
2026-02-14 23:46:35 -08:00
parent 08e2010554
commit 9ff00ce38b
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variants/open/SKILL.md
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---
name: last30days
version: "2.1-open"
description: "Research topics, manage watchlists, and get morning briefings — all from Reddit, X, YouTube, and the web."
argument-hint: 'AI video tools, watch add "NVIDIA news", briefing, history --trending'
allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
---
# last30days (open variant): Research + Watchlist + Briefings
Multi-mode research skill with persistent knowledge accumulation.
## Command Routing
Parse the user's first argument to determine the mode:
| First word | Mode | Reference |
|---|---|---|
| `watch` | Watchlist management | `references/watchlist.md` |
| `briefing` | Morning briefing | `references/briefing.md` |
| `history` | Query accumulated knowledge | `references/history.md` |
| *(anything else)* | One-shot research | `references/research.md` |
## Setup: Find Skill Root
```bash
for dir in \
"." \
"${CLAUDE_PLUGIN_ROOT:-}" \
"$HOME/.claude/skills/last30days" \
"$HOME/.agents/skills/last30days" \
"$HOME/.codex/skills/last30days"; do
[ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
done
if [ -z "${SKILL_ROOT:-}" ]; then
echo "ERROR: Could not find scripts/last30days.py" >&2
exit 1
fi
```
Use `$SKILL_ROOT` for all script and reference file paths.
## Load Context
At session start, read `${SKILL_ROOT}/variants/open/context.md` for user preferences and source quality notes. Update it after interactions.
## Shared Configuration
- **Database**: `~/.local/share/last30days/research.db` (SQLite, WAL mode)
- **Briefings**: `~/.local/share/last30days/briefs/`
- **API keys**: `~/.config/last30days/.env` or environment variables
- **Key priority**: env vars > config file
### API Keys
| Key | Required | Purpose |
|---|---|---|
| `OPENAI_API_KEY` | For Reddit | Reddit search via OpenAI responses API |
| `XAI_API_KEY` | For X (fallback) | X search via xAI Grok API |
| `PARALLEL_API_KEY` | Optional | Web search via Parallel AI |
| `BRAVE_API_KEY` | Optional | Web search via Brave Search |
| `OPENROUTER_API_KEY` | Optional | Web search via Perplexity Sonar Pro |
Bird CLI provides free X search if installed. YouTube search uses yt-dlp (free).
Run `python3 "${SKILL_ROOT}/scripts/last30days.py" --diagnose` to check source availability.
## Routing Logic
After determining the mode, **read the corresponding reference file** using the Read tool:
```
Read: ${SKILL_ROOT}/variants/open/references/{mode}.md
```
Then follow the instructions in that reference file exactly.
variants/open/context.md
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# last30days Context
Agent memory for improving research quality over time.
## User Preferences
<!-- Record preferences discovered during interactions -->
<!-- e.g., "Prefers detailed technical analysis over general summaries" -->
## Source Quality Notes
<!-- Record which sources work best for which topics -->
<!-- e.g., "r/LocalLLaMA is highest signal for AI hardware topics" -->
<!-- e.g., "@kaboratech provides reliable AI tool reviews" -->
## Interaction History
<!-- Record topics researched and useful follow-up patterns -->
<!-- e.g., "2026-02-14: Researched 'AI video tools', user wanted Runway vs Kling comparison" -->
variants/open/references/briefing.md
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# Morning Briefing
Synthesize accumulated findings into a formatted briefing.
## Commands
| Command | Action |
|---|---|
| `briefing` | Generate today's briefing |
| `briefing --weekly` | Weekly digest with trends |
| `briefing --since YYYY-MM-DD` | Briefing since specific date |
## Generate Briefing
```bash
python3 "${SKILL_ROOT}/scripts/briefing.py" generate [--weekly] [--since DATE]
```
The script returns JSON with per-topic findings, staleness info, and cost data.
## Staleness Check
Before synthesizing, check each topic's freshness:
- **Fresh** (< 12h): show normally
- **Aging** (12-36h): note when last run was
- **Stale** (> 36h): warn user, suggest running `watch run-one "topic"`
## Daily Briefing Format
```
Good morning! Here's your research briefing for [DATE].
TL;DR: [One sentence about the top finding across all topics]
---
**[Topic 1]** (N new findings)
Top signal: [Highest engagement finding with source]
Also trending: [2nd finding], [3rd finding]
**[Topic 2]** (N new findings)
Top signal: [Highest engagement finding]
Also trending: [2nd finding]
---
Cost: $X.XX / $Y.YY budget | N topics active | N findings today
```
## Weekly Digest Format
```
Weekly digest for week of [DATE]:
**[Topic 1]**
This week: N findings (up/down X% from last week)
Trending up: [engagement increasing]
Key voices: @handle1, r/sub1
**[Topic 2]**
This week: N findings
Trending down: [engagement decreasing]
```
## Synthesis Rules
- Lead with people, not publications
- 3-5 topics max per briefing
- 2-3 findings per topic
- Include cost/budget footer
- Note any failed or stale topics
## No Data Handling
If no topics or no findings:
```
No briefing data available.
To get started:
1. Add a topic: /last30days watch add "your topic"
2. Run research: /last30days watch run-all
3. Generate briefing: /last30days briefing
```
variants/open/references/history.md
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# History & Knowledge Query
Query the accumulated findings database.
## Commands
| Command | Action |
|---|---|
| `history "topic"` | Show findings for a topic |
| `history "topic" --since=7d` | Findings from last N days |
| `history --search "query"` | Full-text search across all findings |
| `history --trending` | Topics with most recent activity |
| `history --stats` | Watchlist health dashboard |
## Topic History
```bash
python3 "${SKILL_ROOT}/scripts/store.py" query "TOPIC" [--since DAYS]
```
Display findings grouped by date (newest first):
```
**[Topic Name]** — N findings since [date]
[DATE]
- [Reddit] Title (score pts, N comments) — r/subreddit
- [X] Tweet text... (N likes) — @handle
- [YouTube] Video title (N views) — channel
[EARLIER DATE]
- ...
```
Mark updated findings (engagement changed since first seen).
## Full-Text Search
```bash
python3 "${SKILL_ROOT}/scripts/store.py" search "QUERY"
```
Uses FTS5 with BM25 ranking. Show results across all topics:
```
Search: "QUERY" — N results
1. [Reddit] Title — r/subreddit (topic: AI video)
...snippet with **highlighted** matches...
2. [X] Tweet text — @handle (topic: NVIDIA)
...snippet...
```
## Trending Topics
```bash
python3 "${SKILL_ROOT}/scripts/store.py" trending
```
Show topics ranked by recent activity:
```
Trending topics (last 7 days):
1. AI video tools — 12 new findings, engagement up 45%
2. NVIDIA news — 8 new findings, engagement steady
3. Claude Code — 3 new findings, engagement down 20%
```
## Stats Dashboard
```bash
python3 "${SKILL_ROOT}/scripts/store.py" stats
```
Display as a health dashboard:
```
Watchlist Health
- Active topics: N
- Total findings: N
- Database size: N KB
Research Runs (7 days)
- Successful: N
- Failed: N
- Cost: $X.XX
Source Breakdown
- Reddit: N findings
- X: N findings
- YouTube: N findings
- Web: N findings
```
## No Data Handling
If no findings exist:
```
No research history yet.
To start building knowledge:
1. Run research: /last30days "your topic"
2. Or add a watchlist topic: /last30days watch add "topic"
```
variants/open/references/research.md
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# One-Shot Research Mode
Research ANY topic across Reddit, X, YouTube, and the web. Surface what people are actually discussing, recommending, and debating right now.
## Parse User Intent
Before doing anything, parse the user's input for:
1. **TOPIC**: What they want to learn about
2. **TARGET TOOL** (if specified): Where they'll use the prompts
3. **QUERY TYPE**:
- **PROMPTING** — "X prompts", "prompting for X" → copy-paste prompts
- **RECOMMENDATIONS** — "best X", "top X" → list of specific things
- **NEWS** — "what's happening with X" → current events
- **GENERAL** — anything else → broad understanding
**Do NOT ask about target tool before research.** Run research first, ask after.
**Display your parsing** before calling tools:
```
I'll research {TOPIC} across Reddit, X, YouTube, and the web.
Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}
Research typically takes 2-8 minutes. Starting now.
```
---
## Research Execution
**Step 1: Run the research script (FOREGROUND)**
```bash
python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --store 2>&1
```
Use a **timeout of 300000** (5 minutes). The `--store` flag persists findings for watchlist/briefing integration.
The script auto-detects: API keys, Bird CLI, yt-dlp, web search backends.
**Read the ENTIRE output.** It contains Reddit, X, YouTube, AND web sections.
---
**Step 2: WebSearch (supplement)**
After the script finishes, use your WebSearch tool for additional coverage.
Choose queries based on QUERY_TYPE:
- **RECOMMENDATIONS**: `best {TOPIC} recommendations`, `{TOPIC} list examples`
- **NEWS**: `{TOPIC} news 2026`, `{TOPIC} announcement update`
- **PROMPTING**: `{TOPIC} prompts examples 2026`, `{TOPIC} techniques tips`
- **GENERAL**: `{TOPIC} 2026`, `{TOPIC} discussion`
Rules:
- **USE THE USER'S EXACT TERMINOLOGY**
- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
- Do NOT output "Sources:" list
---
## Synthesis
**Judge Agent rules:**
1. Weight Reddit/X HIGHER (engagement signals)
2. Weight YouTube HIGH (views + transcript content)
3. Weight web LOWER (no engagement data)
4. Identify cross-source patterns (strongest signals)
5. Extract top 3-5 actionable insights
**Ground synthesis in ACTUAL research, not pre-existing knowledge.**
### Citation Rules
- Cite sparingly: 1-2 sources per topic
- Priority: @handles > r/subreddits > YouTube channels > web sources
- Use publication names, never raw URLs
- Lead with people, not publications
---
## Display Results
**1. "What I learned"** (format depends on QUERY_TYPE)
**If RECOMMENDATIONS** — show specific items with sources:
```
Most mentioned:
[Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle1, r/sub, blog.com
```
**If PROMPTING/NEWS/GENERAL** — show synthesis:
```
What I learned:
**{Topic 1}** — [1-2 sentences, per @handle or r/sub]
KEY PATTERNS:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
```
**2. Stats box** (calculate from actual output):
```
---
All agents reported back!
|- Reddit: {N} threads | {N} upvotes | {N} comments
|- X: {N} posts | {N} likes | {N} reposts
|- YouTube: {N} videos | {N} views | {N} with transcripts
|- Web: {N} pages (supplementary)
|- Top voices: @{handle1}, @{handle2} | r/{sub1}, r/{sub2}
---
```
**3. Invitation** with 2-3 specific follow-up suggestions based on research.
---
## Follow-Up
After research, you are an **EXPERT** on this topic.
- **QUESTION** → Answer from research (no new searches)
- **GO DEEPER** → Elaborate from findings
- **CREATE/PROMPT** → Write ONE prompt using research insights
- **Different topic** → Run new research
When writing prompts, match the FORMAT the research recommends (JSON, structured, etc.).
variants/open/references/watchlist.md
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# Watchlist Management
Manage topics you want to track continuously. Findings accumulate in the SQLite database for briefings and history queries.
## Commands
| Command | Action |
|---|---|
| `watch add "topic"` | Add a topic (daily schedule) |
| `watch add "topic" --weekly` | Add with weekly schedule |
| `watch "topic"` | Shorthand for `watch add` |
| `watch remove "topic"` | Remove a topic |
| `watch list` | Show all topics with status |
| `watch config delivery [channel]` | Set delivery channel |
| `watch config budget AMOUNT` | Set daily cost budget |
| `watch run-all` | Run research for all topics now |
| `watch run-one "topic"` | Run research for one topic now |
## Adding a Topic
```bash
python3 "${SKILL_ROOT}/scripts/watchlist.py" add "TOPIC_NAME" [--weekly] [--queries "q1,q2"]
```
The script auto-bootstraps the SQLite database on first add.
**Default schedule**: Daily at 8am (`0 8 * * *`).
**Weekly**: Mondays at 8am (`0 8 * * 1`).
**After adding**, confirm to the user:
```
Added "TOPIC_NAME" to watchlist.
Schedule: daily at 8am (or weekly on Mondays)
To run research now: /last30days watch run-one "TOPIC_NAME"
To set up automated runs: add a cron/launchd job for `python3 ${SKILL_ROOT}/scripts/watchlist.py run-all`
```
## Removing a Topic
```bash
python3 "${SKILL_ROOT}/scripts/watchlist.py" remove "TOPIC_NAME"
```
Show confirmation or "not found" message.
## Listing Topics
```bash
python3 "${SKILL_ROOT}/scripts/watchlist.py" list
```
Display as a formatted table:
```
Topic | Schedule | Last Run | Findings | Status
--------------+--------------+--------------+----------+--------
AI video | daily 8am | 2h ago | 47 | ok
NVIDIA news | weekly Mon | 3d ago | 23 | ok
Budget: $0.42 / $5.00 today
```
## Running Research
```bash
# All enabled topics (with budget guard)
python3 "${SKILL_ROOT}/scripts/watchlist.py" run-all
# Single topic
python3 "${SKILL_ROOT}/scripts/watchlist.py" run-one "TOPIC_NAME"
```
Show results: new findings count, updated findings, duration, and any errors.
## Configuration
```bash
# Set delivery channel (for future notification support)
python3 "${SKILL_ROOT}/scripts/watchlist.py" config delivery telegram
# Set daily budget limit
python3 "${SKILL_ROOT}/scripts/watchlist.py" config budget 10.00
```
## Scheduling
The watchlist doesn't auto-schedule. To automate, set up a system job:
**macOS (launchd)**:
```bash
# Run daily at 8am
crontab -e
# Add: 0 8 * * * python3 /path/to/scripts/watchlist.py run-all
```
**Linux (cron)**:
```bash
crontab -e
# Add: 0 8 * * * python3 /path/to/scripts/watchlist.py run-all
```
## Error Handling
- Duplicate topic: update the existing schedule
- Topic not found on remove: show "not found" message
- Budget exceeded: skip remaining topics, show which were skipped
- Research failure: record error, continue to next topic