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-search)<a href="https://agentmods.dev/commands/ankushdixit/claude-plugins/learn-search"><img src="https://agentmods.dev/badge/commands/ankushdixit/claude-plugins/learn-search/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-search"><img src="https://agentmods.dev/badge/commands/ankushdixit/claude-plugins/learn-search.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 | $0.00257 |
| Opus 5.5 | $0.00002 | $0.00103 |
| Sonnet 5.5 | $0.00001 | $0.00051 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
learn-search 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.
What it actually says
Search Learnings
Full-text search across all learning content, tags, and context.
Usage
Extract the search query from $ARGUMENTS and run:
sk learn-search "$@"
How Search Works
The search looks for matches in:
- Learning content (main text)
- Tags
- Context/notes
- Category names
Search is case-insensitive and finds partial matches.
Examples
Search for "CORS":
sk learn-search "CORS"
Search for "FastAPI middleware":
sk learn-search "FastAPI middleware"
Search for "authentication":
sk learn-search "authentication"
Display Format
Show matching learnings with:
- Learning content with matched text highlighted
- Category and tags
- Session where captured
- Relevance score or match indicator
- Learning ID
Present results in order of relevance, grouped by category.
Tips for Users
Suggest to users:
- Use specific keywords for better results
- Try tag names if searching by topic
- Use category names to narrow results
- Combine with
/learn-show --categoryfor focused browsing
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 · 69 lines · 5 tokens per session scan A 080c5646b0ee
learn-search 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 257 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.