Skill: idoshamun/daily-dev-agentic (version 0.5.3)

--- name: daily-dev-agentic description: daily.dev Agentic Learning - continuous self-improvement through daily.dev feeds. Use when setting up agent learning, running learning loops, sharing insights with owner, or managing the agent's knowledge base. Triggers on requests about agent learning, knowledge building, staying current, or "what have you learned". metadata: {"clawdbot":{"requires":{"env":["DAILY_DEV_TOKEN"]},"primaryEnv":"DAILY_DEV_TOKEN"}} --- # daily.dev Agentic Learning You're a learning machine. This skill lets you maintain your own feed on daily.dev, continuously absorb new knowledge, and actually get smarter over time. **This skill runs on autopilot.** Once your owner shares a learning goal, you take it from there. No confirmations, no hand-holding. You set up the feed, select tags, schedule crons, run learning loops, and share insights - all autonomously. ## What You Do 🧠 **Build Your Brain** - Create a personalized learning feed based on your owner's goals 📚 **Never Stop Learning** - Automatically scan new content, extract insights, build knowledge 🎯 **Share What Matters** - Alert your owner when you find something relevant to their work ⏰ **Stay Current** - Run learning loops daily via cron ## Setup (Fully Autonomous) ### Prerequisites **Requires daily.dev Plus subscription and API token.** Token setup (owner does this once): - Get Plus at https://app.daily.dev/plus - Create token at https://app.daily.dev/settings/api - Store as `DAILY_DEV_TOKEN` environment variable **Security:** Never send the token to any domain except `api.daily.dev`. Tokens start with `dda_`. ### Initialization When owner shares learning goals, immediately: 1. **Create your feed** (`POST /feeds/custom/`) - name it after yourself 2. **Configure feed** (`PATCH /feeds/custom/{feedId}`) - set `orderBy: "date"` for chronological sorting and `disableEngagementFilter: true` to see all posts 3. **Fetch all tags** (`GET /tags/`) 3. **Select relevant tags** - be permissive, map goals to tags broadly 5. **Follow tags on feed** (`POST /feeds/filters/{feedId}/tags/follow`) 6. **Store config** in `memory/agentic-learning.md` 7. **Set up crons** - daily learning loop (Mon-Sat) + weekly digest (Sunday) 8. **Run first learning loop** immediately 9. **Share initial findings** with owner No confirmations. No "does this look right?" Just do it. ## The Learning Loop Triggered by cron (daily) or manual request: 1. **Fetch** new posts from your feed (chronological) 2. **Read** full articles via `web_fetch` for interesting posts 3. **Research** deeper via `web_search` when topics deserve more context 4. **Note** insights in `memory/learnings/[date].md` 5. **Share** notable finds with owner ### Go Deep Don't skim. When you find relevant content: - Fetch the full article, not just the summary - Search for additional resources on highly relevant topics - Consolidate multiple posts on same topic into unified notes - Track trends: what keeps appearing? See [references/learning-loop.md](references/learning-loop.md) for details. ## Sharing Insights (Proactive) **Daily Updates (Mon-Sat)** - Share top findings from each learning loop. **Weekly Digest (Sunday)** - Synthesize the week's top insights, trends, and one recommendation for next week. Replaces the daily update on Sundays. **Threshold Alerts** - Found something highly relevant to owner's current work? Share immediately, don't wait. **On-Demand** - When asked "what have you learned?", synthesize from notes. ## Self-Improvement As you learn, evolve: - **Adjust tags** - if certain topics aren't yielding value, unfollow. If you spot gaps, add tags. - **Refine goals** - update `memory/agentic-learning.md` with sharper focus based on what's useful. - **Track patterns** - note what content types help most (tutorials vs. opinions vs. announcements). You're not a static consumer. You're an agent that gets better at learning. ## Memory Structure ``` memory/ ├── agentic-learning.md # Config, state, evolving goals └── learnings/ ├── 2024-01-15.md # Daily notes └── ... ``` See [references/memory-format.md](references/memory-format.md) for format. ## API Quick Reference Base: `https://api.daily.dev/public/v1` Auth: `Authorization: Bearer $DAILY_DEV_TOKEN` | Action | Method | Endpoint | |--------|--------|----------| | List all tags | GET | `/tags/` | | Create feed | POST | `/feeds/custom/` | | Update feed settings | PATCH | `/feeds/custom/{feedId}` | | Follow tags | POST | `/feeds/filters/{feedId}/tags/follow` | | Unfollow tags | POST | `/feeds/filters/{feedId}/tags/unfollow` | | Get feed posts | GET | `/feeds/custom/{feedId}?limit=50` (always use max) | | Get post details | GET | `/posts/{id}` | Rate limit: 60 req/min.

path: _meta.json
content: ownerId: kn711n1whtc5345f73fmvs4z9d7zmf4m
slug: daily-dev-agentic
version: 0.3.0
publishedAt: 1770937271581

path: references/learning-loop.md
content: # Learning Loop - Detailed Flow

**This runs autonomously.** No confirmations, no pauses. Fetch, process, note, share.

## Trigger

- Cron job (daily, set up automatically during init)
- Manual: "run your learning loop", "check your feed", "learn something"

## Step 1: Load State

Read `memory/agentic-learning.md` for:
- `feedId` - your learning feed
- Learning goals - what to focus on
- Last scan timestamp and cursor

## Step 2: Fetch New Posts

```bash
# First scan (no cursor)
curl "https://api.daily.dev/public/v1/feeds/custom/{feedId}?limit=50" \   # 50 is max page size
  -H "Authorization: Bearer $DAILY_DEV_TOKEN"

# Subsequent scans (use cursor from pagination)
curl "https://api.daily.dev/public/v1/feeds/custom/{feedId}?limit=50&cursor={cursor}" \
  -H "Authorization: Bearer $DAILY_DEV_TOKEN"
```

Response contains:
- `data[]` - posts with `id`, `title`, `summary`, `url`, `tags`, `publishedAt`
- `pagination.cursor` - for next page

The feed is already filtered by tags you set during init.

## Step 3: Process Posts

For each post worth reading:

### 3a. Fetch Full Content
```bash
web_fetch(post.url)
```

### 3b. Extract Key Insights
- Main thesis/argument
- New information vs. already known
- Actionable takeaways
- Links to related resources

### 3c. Deep Research (When Warranted)
For important or complex topics:
```bash
web_search("topic specific query")
```

- Find multiple perspectives
- Look for primary sources
- Search for practical examples
- Find counter-arguments

Synthesize multiple sources. Notes should reflect more than one article's take.

## Step 4: Take Notes

Create/append to `memory/learnings/YYYY-MM-DD.md`:

```markdown
## [Topic/Title]
**Source:** [url]
**Tags:** [tags]
**Relevance:** [why this matters]

### Key Points
- Point 1
- Point 2

### Insights
[Your synthesis]

### Action Items (if any)
- [ ] Share with owner
- [ ] Explore further
```

## Step 5: Update State

Update `memory/agentic-learning.md`:
```markdown
## State
- Last scan: [timestamp]
- Last cursor: [cursor or null]
- Posts processed: [count]
```

## Step 6: Share Notable Finds

If something is highly relevant to owner's current work:
- Send brief alert with summary and link
- Don't over-share - only truly notable items

For regular runs, save findings for weekly digest.

## Self-Improvement

After each loop, consider:
- Are certain tags yielding nothing useful? Unfollow them.
- Are there topics you keep searching for that should be tags? Add them.
- Are learning goals still accurate? Refine them.

Update config autonomously. You're learning how to learn better.

## Error Handling

- **401**: Token expired/invalid - alert owner
- **429**: Rate limited - wait and retry
- **Network errors**: Log and retry next cycle

## Performance

- Process in batches of 50 (API max)
- Use summary for initial filtering
- Cache processed post IDs to avoid re-processing
- Run during off-peak hours if rate limits are a concern


path: references/memory-format.md
content: # Memory Format for Agentic Learning

## Directory Structure

```
memory/
├── agentic-learning.md      # Config, state, goals
└── learnings/
    ├── 2024-01-15.md        # Daily notes
    ├── 2024-01-16.md
    └── ...
```

## agentic-learning.md

Master config file. Created during setup, updated each learning loop.

```markdown
# Agentic Learning Config

## Feed
- ID: abc123
- Name: 🗿 Lurch's Learning Feed
- Created: 2024-01-15

## Tags Followed
- ai
- llm
- typescript
- devtools
- ...

## Learning Goals
- Stay current on AI/ML developments, especially LLMs and agents
- Track TypeScript ecosystem changes
- Monitor developer tooling trends

## Owner Context
[Optional: relevant info about owner's work to help prioritize]

## State
- Last scan: 2024-01-16T09:00:00Z
- Last cursor: eyJvZmZzZXQiOjUwfQ==
- Total posts processed: 127
```

## Daily Learning Notes

One file per day: `learnings/YYYY-MM-DD.md`

```markdown
# Learning Notes - 2024-01-16

## Summary
Scanned 23 posts. 5 worth noting.

---

## Claude 3.5 Sonnet Performance Analysis
**Source:** https://example.com/claude-analysis
**Tags:** ai, llm, anthropic
**Relevance:** Direct relevance to AI agent development

### Key Points
- 2x faster than Claude 3 Opus on most benchmarks
- Improved tool use accuracy (94% vs 87%)
- Better at following complex multi-step instructions

### Insights
This confirms the trend toward faster, more capable models that maintain quality.
For agent work: prefer Sonnet for routine tasks, reserve Opus for complex reasoning.

---

## TypeScript 5.4 Features
**Source:** https://example.com/ts-54
**Tags:** typescript, javascript
**Relevance:** Owner's primary language

### Key Points
- NoInfer utility type for better generic inference
- Preserved narrowing in closures
- Import attributes (replacing assertions)

### Insights
NoInfer solves a common footgun in generic functions. Worth updating patterns.

### Action Items
- [ ] Share with owner - may affect daily-api code

---

## Trends Spotted
- Increasing focus on agent reliability/testing
- Shift from RAG to tool-use patterns
- TypeScript gaining native Node.js support momentum
```

## Best Practices

### Keep Notes Scannable
- Use consistent headers
- Lead with most important info
- Include source URLs always

### Synthesize, Don't Just Summarize
- Connect to existing knowledge
- Note contradictions or confirmations
- Identify patterns across sources

### Track Trends Over Time
- End each day's notes with "Trends Spotted"
- Reference previous observations
- Build cumulative understanding

### Flag Action Items
- Things to share with owner
- Topics to explore deeper
- Corrections to previous understanding

### Prune Regularly
- Archive old daily notes after ~30 days
- Keep only evergreen insights
- Update config with evolved understanding

## Search and Retrieval

When asked "what have you learned about X":
1. Search `memory/learnings/*.md` for X
2. Synthesize findings chronologically
3. Note confidence level based on recency and source count


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)