feat(open): create open variant SKILL.md and reference files
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
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# Morning Briefing
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Synthesize accumulated findings into a formatted briefing.
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## Commands
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| Command | Action |
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|---|---|
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| `briefing` | Generate today's briefing |
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| `briefing --weekly` | Weekly digest with trends |
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| `briefing --since YYYY-MM-DD` | Briefing since specific date |
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## Generate Briefing
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```bash
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python3 "${SKILL_ROOT}/scripts/briefing.py" generate [--weekly] [--since DATE]
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```
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The script returns JSON with per-topic findings, staleness info, and cost data.
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## Staleness Check
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Before synthesizing, check each topic's freshness:
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- **Fresh** (< 12h): show normally
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- **Aging** (12-36h): note when last run was
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- **Stale** (> 36h): warn user, suggest running `watch run-one "topic"`
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## Daily Briefing Format
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```
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Good morning! Here's your research briefing for [DATE].
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TL;DR: [One sentence about the top finding across all topics]
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---
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**[Topic 1]** (N new findings)
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Top signal: [Highest engagement finding with source]
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Also trending: [2nd finding], [3rd finding]
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**[Topic 2]** (N new findings)
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Top signal: [Highest engagement finding]
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Also trending: [2nd finding]
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---
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Cost: $X.XX / $Y.YY budget | N topics active | N findings today
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```
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## Weekly Digest Format
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```
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Weekly digest for week of [DATE]:
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**[Topic 1]**
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This week: N findings (up/down X% from last week)
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Trending up: [engagement increasing]
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Key voices: @handle1, r/sub1
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**[Topic 2]**
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This week: N findings
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Trending down: [engagement decreasing]
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```
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## Synthesis Rules
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- Lead with people, not publications
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- 3-5 topics max per briefing
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- 2-3 findings per topic
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- Include cost/budget footer
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- Note any failed or stale topics
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## No Data Handling
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If no topics or no findings:
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```
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No briefing data available.
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To get started:
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1. Add a topic: /last30days watch add "your topic"
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2. Run research: /last30days watch run-all
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3. Generate briefing: /last30days briefing
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```
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# History & Knowledge Query
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Query the accumulated findings database.
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## Commands
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| Command | Action |
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|---|---|
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| `history "topic"` | Show findings for a topic |
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| `history "topic" --since=7d` | Findings from last N days |
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| `history --search "query"` | Full-text search across all findings |
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| `history --trending` | Topics with most recent activity |
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| `history --stats` | Watchlist health dashboard |
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## Topic History
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```bash
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python3 "${SKILL_ROOT}/scripts/store.py" query "TOPIC" [--since DAYS]
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```
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Display findings grouped by date (newest first):
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```
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**[Topic Name]** — N findings since [date]
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[DATE]
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- [Reddit] Title (score pts, N comments) — r/subreddit
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- [X] Tweet text... (N likes) — @handle
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- [YouTube] Video title (N views) — channel
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[EARLIER DATE]
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- ...
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```
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Mark updated findings (engagement changed since first seen).
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## Full-Text Search
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```bash
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python3 "${SKILL_ROOT}/scripts/store.py" search "QUERY"
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```
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Uses FTS5 with BM25 ranking. Show results across all topics:
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```
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Search: "QUERY" — N results
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1. [Reddit] Title — r/subreddit (topic: AI video)
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...snippet with **highlighted** matches...
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2. [X] Tweet text — @handle (topic: NVIDIA)
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...snippet...
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```
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## Trending Topics
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```bash
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python3 "${SKILL_ROOT}/scripts/store.py" trending
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```
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Show topics ranked by recent activity:
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```
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Trending topics (last 7 days):
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1. AI video tools — 12 new findings, engagement up 45%
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2. NVIDIA news — 8 new findings, engagement steady
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3. Claude Code — 3 new findings, engagement down 20%
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```
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## Stats Dashboard
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```bash
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python3 "${SKILL_ROOT}/scripts/store.py" stats
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```
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Display as a health dashboard:
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```
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Watchlist Health
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- Active topics: N
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- Total findings: N
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- Database size: N KB
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Research Runs (7 days)
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- Successful: N
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- Failed: N
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- Cost: $X.XX
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Source Breakdown
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- Reddit: N findings
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- X: N findings
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- YouTube: N findings
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- Web: N findings
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```
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## No Data Handling
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If no findings exist:
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```
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No research history yet.
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To start building knowledge:
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1. Run research: /last30days "your topic"
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2. Or add a watchlist topic: /last30days watch add "topic"
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```
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# One-Shot Research Mode
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Research ANY topic across Reddit, X, YouTube, and the web. Surface what people are actually discussing, recommending, and debating right now.
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## Parse User Intent
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Before doing anything, parse the user's input for:
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1. **TOPIC**: What they want to learn about
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2. **TARGET TOOL** (if specified): Where they'll use the prompts
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3. **QUERY TYPE**:
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- **PROMPTING** — "X prompts", "prompting for X" → copy-paste prompts
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- **RECOMMENDATIONS** — "best X", "top X" → list of specific things
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- **NEWS** — "what's happening with X" → current events
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- **GENERAL** — anything else → broad understanding
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**Do NOT ask about target tool before research.** Run research first, ask after.
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**Display your parsing** before calling tools:
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```
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I'll research {TOPIC} across Reddit, X, YouTube, and the web.
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Parsed intent:
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- TOPIC = {TOPIC}
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- TARGET_TOOL = {TARGET_TOOL or "unknown"}
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- QUERY_TYPE = {QUERY_TYPE}
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Research typically takes 2-8 minutes. Starting now.
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```
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---
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## Research Execution
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**Step 1: Run the research script (FOREGROUND)**
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```bash
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python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --store 2>&1
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```
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Use a **timeout of 300000** (5 minutes). The `--store` flag persists findings for watchlist/briefing integration.
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The script auto-detects: API keys, Bird CLI, yt-dlp, web search backends.
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**Read the ENTIRE output.** It contains Reddit, X, YouTube, AND web sections.
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---
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**Step 2: WebSearch (supplement)**
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After the script finishes, use your WebSearch tool for additional coverage.
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Choose queries based on QUERY_TYPE:
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- **RECOMMENDATIONS**: `best {TOPIC} recommendations`, `{TOPIC} list examples`
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- **NEWS**: `{TOPIC} news 2026`, `{TOPIC} announcement update`
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- **PROMPTING**: `{TOPIC} prompts examples 2026`, `{TOPIC} techniques tips`
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- **GENERAL**: `{TOPIC} 2026`, `{TOPIC} discussion`
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Rules:
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- **USE THE USER'S EXACT TERMINOLOGY**
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- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
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- Do NOT output "Sources:" list
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---
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## Synthesis
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**Judge Agent rules:**
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1. Weight Reddit/X HIGHER (engagement signals)
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2. Weight YouTube HIGH (views + transcript content)
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3. Weight web LOWER (no engagement data)
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4. Identify cross-source patterns (strongest signals)
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5. Extract top 3-5 actionable insights
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**Ground synthesis in ACTUAL research, not pre-existing knowledge.**
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### Citation Rules
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- Cite sparingly: 1-2 sources per topic
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- Priority: @handles > r/subreddits > YouTube channels > web sources
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- Use publication names, never raw URLs
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- Lead with people, not publications
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---
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## Display Results
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**1. "What I learned"** (format depends on QUERY_TYPE)
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**If RECOMMENDATIONS** — show specific items with sources:
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```
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Most mentioned:
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[Name] - {n}x mentions
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Use Case: [what it does]
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Sources: @handle1, r/sub, blog.com
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```
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**If PROMPTING/NEWS/GENERAL** — show synthesis:
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```
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What I learned:
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**{Topic 1}** — [1-2 sentences, per @handle or r/sub]
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KEY PATTERNS:
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1. [Pattern] — per @handle
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2. [Pattern] — per r/sub
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```
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**2. Stats box** (calculate from actual output):
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```
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---
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All agents reported back!
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|- Reddit: {N} threads | {N} upvotes | {N} comments
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|- X: {N} posts | {N} likes | {N} reposts
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|- YouTube: {N} videos | {N} views | {N} with transcripts
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|- Web: {N} pages (supplementary)
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|- Top voices: @{handle1}, @{handle2} | r/{sub1}, r/{sub2}
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---
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```
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**3. Invitation** with 2-3 specific follow-up suggestions based on research.
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---
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## Follow-Up
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After research, you are an **EXPERT** on this topic.
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- **QUESTION** → Answer from research (no new searches)
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- **GO DEEPER** → Elaborate from findings
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- **CREATE/PROMPT** → Write ONE prompt using research insights
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- **Different topic** → Run new research
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When writing prompts, match the FORMAT the research recommends (JSON, structured, etc.).
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# Watchlist Management
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Manage topics you want to track continuously. Findings accumulate in the SQLite database for briefings and history queries.
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## Commands
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| Command | Action |
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| `watch add "topic"` | Add a topic (daily schedule) |
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| `watch add "topic" --weekly` | Add with weekly schedule |
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| `watch "topic"` | Shorthand for `watch add` |
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| `watch remove "topic"` | Remove a topic |
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| `watch list` | Show all topics with status |
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| `watch config delivery [channel]` | Set delivery channel |
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| `watch config budget AMOUNT` | Set daily cost budget |
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| `watch run-all` | Run research for all topics now |
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| `watch run-one "topic"` | Run research for one topic now |
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## Adding a Topic
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```bash
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python3 "${SKILL_ROOT}/scripts/watchlist.py" add "TOPIC_NAME" [--weekly] [--queries "q1,q2"]
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```
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The script auto-bootstraps the SQLite database on first add.
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**Default schedule**: Daily at 8am (`0 8 * * *`).
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**Weekly**: Mondays at 8am (`0 8 * * 1`).
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**After adding**, confirm to the user:
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```
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Added "TOPIC_NAME" to watchlist.
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Schedule: daily at 8am (or weekly on Mondays)
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To run research now: /last30days watch run-one "TOPIC_NAME"
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To set up automated runs: add a cron/launchd job for `python3 ${SKILL_ROOT}/scripts/watchlist.py run-all`
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```
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## Removing a Topic
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```bash
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python3 "${SKILL_ROOT}/scripts/watchlist.py" remove "TOPIC_NAME"
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```
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Show confirmation or "not found" message.
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## Listing Topics
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```bash
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python3 "${SKILL_ROOT}/scripts/watchlist.py" list
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```
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Display as a formatted table:
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```
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Topic | Schedule | Last Run | Findings | Status
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--------------+--------------+--------------+----------+--------
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AI video | daily 8am | 2h ago | 47 | ok
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NVIDIA news | weekly Mon | 3d ago | 23 | ok
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Budget: $0.42 / $5.00 today
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```
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## Running Research
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```bash
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# All enabled topics (with budget guard)
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python3 "${SKILL_ROOT}/scripts/watchlist.py" run-all
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# Single topic
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python3 "${SKILL_ROOT}/scripts/watchlist.py" run-one "TOPIC_NAME"
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```
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Show results: new findings count, updated findings, duration, and any errors.
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## Configuration
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```bash
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# Set delivery channel (for future notification support)
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python3 "${SKILL_ROOT}/scripts/watchlist.py" config delivery telegram
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# Set daily budget limit
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python3 "${SKILL_ROOT}/scripts/watchlist.py" config budget 10.00
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```
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## Scheduling
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The watchlist doesn't auto-schedule. To automate, set up a system job:
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**macOS (launchd)**:
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```bash
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# Run daily at 8am
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crontab -e
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# Add: 0 8 * * * python3 /path/to/scripts/watchlist.py run-all
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```
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**Linux (cron)**:
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```bash
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crontab -e
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# Add: 0 8 * * * python3 /path/to/scripts/watchlist.py run-all
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```
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## Error Handling
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- Duplicate topic: update the existing schedule
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- Topic not found on remove: show "not found" message
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- Budget exceeded: skip remaining topics, show which were skipped
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- Research failure: record error, continue to next topic
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Reference in New Issue
Block a user