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.
git clone --depth 1 https://github.com/ankushdixit/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/ankushdixit/claude-plugins/learn-curate)<a href="https://agentmods.dev/commands/ankushdixit/claude-plugins/learn-curate"><img src="https://agentmods.dev/badge/commands/ankushdixit/claude-plugins/learn-curate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/ankushdixit/claude-plugins/learn-curate"><img src="https://agentmods.dev/badge/commands/ankushdixit/claude-plugins/learn-curate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00005 | $0.00523 |
| Opus 5.5 | $0.00002 | $0.00209 |
| Sonnet 5.5 | $0.00001 | $0.00105 |
| Haiku 4.5 | $0.00001 | $0.00052 |
Grade A, and why
learn-curate 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 1mo 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Curate Learnings
Run automatic categorization, similarity detection, and merging of learnings.
What Curation Does
The curation process:
- Categorizes uncategorized learnings using AI-powered keyword analysis
- Detects duplicates using Jaccard and containment similarity algorithms
- Merges similar learnings to reduce redundancy
- Archives old learnings (learnings older than 50 sessions)
- Updates metadata (last_curated timestamp)
Usage
Normal Curation (Save Changes)
sk learn-curate
This will:
- Process all learnings
- Save changes to learnings.json
- Display summary of actions taken
Dry-Run Mode (Preview Only)
sk learn-curate --dry-run
This will:
- Show what changes would be made
- NOT save any changes
- Useful for previewing curation results
When to Run Curation
Manual curation is useful when:
- You've captured many learnings and want to organize them
- You want to check for duplicate learnings
- You want to preview what auto-curation would do
- You're testing the curation process
Note: Curation also runs automatically every N sessions (configurable in .session/config.json).
Output Format
Display the curation summary showing:
- Initial learning count
- Number of learnings categorized
- Number of duplicates merged
- Number of learnings archived
- Final learning count
Example output:
=== Learning Curation ===
Initial learnings: 45
✓ Categorized 8 learnings
✓ Merged 3 duplicate learnings
✓ Archived 2 old learnings
Final learnings: 42
✓ Learnings saved
Understanding the Process
Categorization: Uses keyword analysis to assign learnings to one of 6 categories:
- architecture_patterns, gotchas, best_practices, technical_debt, performance_insights, security
Similarity Detection: Uses two algorithms:
- Jaccard similarity: Measures word overlap (threshold: 0.6)
- Containment similarity: Detects if one learning contains another (threshold: 0.8)
Merging: Combines tags and tracks merge history when duplicates are found.
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.
- 1mo ago First seen · 97 lines · 5 tokens per session scan A 940af23bf8ca
learn-curate is a command published in the GitHub repository ankushdixit/claude-plugins (3 stars, last pushed 9mo ago), licensed MIT. It adds 5 tokens to every session and 523 once invoked, about $0.0000 per session on Opus 5.5. 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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