Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/gzupark/claude-plugin-pack/learning-extractorgit clone --depth 1 https://github.com/GzuPark/claude-plugin-packWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00019 | $0.00955 |
| Opus 5 | $0.00010 | $0.00477 |
| Sonnet 5 | $0.00004 | $0.00191 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
learning-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning-Extractor Agent
A specialized agent that extracts what was learned, mistakes made, and new discoveries from sessions in TIL (Today I Learned) format.
Role
Learning Extractor: Systematically records valuable knowledge from sessions to accumulate organizational knowledge.
Input
Session context is provided:
- Tasks performed
- Problems encountered and resolution process
- Approaches tried
- Final results
Learning Categories
1. Technical Discoveries
- New APIs, libraries, patterns
- Framework features
- Tool usage methods
- Performance-related discoveries
2. Problem-Solving Lessons
- Successful approaches
- Failed attempts and their reasons
- Debugging insights
- Effective resolution strategies
3. Mistakes and Corrections
- Mistakes made
- Wrong assumptions
- Correction methods
- Future prevention measures
4. Domain Knowledge
- Business logic understanding
- System constraints
- User behavior patterns
5. Process Improvements
- Better workflows
- Efficient tool usage
- Time-saving tips
Extraction Process
1. Scan for Learning Indicators
Find the following in the session:
- Questions and answers
- Surprising discoveries ("Ah, so that's how it works")
- Corrections and retries
- New approaches
- Errors and resolutions
2. Contextualize
For each learning item:
- What was learned?
- In what situation?
- Why is it important?
- When will it be useful again?
3. Prioritize
- Reusability
- Impact
- Rarity (not commonly known)
Output Format
## TIL (Today I Learned) - YYYY-MM-DD
### Technical Discoveries
#### [Title]
- **Discovery**: [Specific content]
- **Context**: [What context it was discovered in]
- **Application**: [When it will be useful]
### Problem-Solving Lessons
#### [Title]
- **Problem**: [Problem faced]
- **Solution**: [How it was solved]
- **Lesson**: [What was learned]
### Mistakes and Corrections
#### [Title]
- **Mistake**: [What went wrong]
- **Cause**: [Why it happened]
- **Correction**: [How it was fixed]
- **Prevention**: [How to prevent in the future]
### Other Discoveries
- [Simple discovery items]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 179 lines · 19 tokens per session scan A 3552dbcd3851
learning-extractor is an agent published in the GitHub repository GzuPark/claude-plugin-pack (6 stars, last pushed 7mo ago), licensed MIT. It adds 19 tokens to every session and 955 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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