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/vladolaru/claude-code-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/vladolaru/claude-code-plugins/learn)<a href="https://agentmods.dev/commands/vladolaru/claude-code-plugins/learn"><img src="https://agentmods.dev/badge/commands/vladolaru/claude-code-plugins/learn/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/vladolaru/claude-code-plugins/learn"><img src="https://agentmods.dev/badge/commands/vladolaru/claude-code-plugins/learn.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 | $0.00017 | $0.00875 |
| Opus 5.5 | $0.00007 | $0.00350 |
| Sonnet 5.5 | $0.00003 | $0.00175 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
learn 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/dex:learn
Capture a learning from the current conversation. Self-contained — extracts from chat history, no arguments needed. Optional focus hint narrows what to extract.
Step 1: Discover Project Infrastructure
Follow the Project Discovery steps from the knowledge-capture skill.
If .claude/docs/ does not exist, use AskUserQuestion:
Question: "No knowledge directory found. Create it?" Options:
- Yes, create
.claude/docs/— scaffoldslearnings/,patterns/,decisions/,research/ - Not now — abort capture
If "Not now", stop here. If "Yes", create directories with mkdir -p and continue.
Step 2: Extract Learning from Conversation
Run the <pre_extraction_analysis> from the knowledge-capture skill on the relevant conversation exchange. If $ARGUMENTS contains a focus hint, narrow extraction to that topic.
If the conversation contains nothing extractable as a learning (no discovery, fix, or gotcha), say so briefly and stop. Do not fabricate knowledge.
Following the Knowledge Extraction from Conversation guidance in the knowledge-capture skill:
- Identify the core insight — what's the one thing an agent should know next time?
- Draft a title as a short directive statement (e.g., "Always pass --user=1 for WP-CLI REST calls")
- Draft the Rule section — a specific, actionable directive: what to do and why, in 1-3 sentences
- Draft brief Context (why this matters, root cause) and Examples (correct vs. incorrect approaches)
- Identify 3-5 tags from the technical domain
- Determine the filename:
YYYY-MM-DD-slug.md
Verify the draft passes the <extraction_quality_checklist> from the knowledge-capture skill before presenting to the user.
Focus on the behavioral change: what should an agent do differently next time?
Step 3: Confirm with User
Use AskUserQuestion:
Question: "Capture this learning?"
Show exactly these fields in the question description:
Title: [drafted title] Rule: [1-2 sentence rule] File:
.claude/docs/learnings/YYYY-MM-DD-slug.md
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 · 92 lines · 17 tokens per session scan A 7b5cd883ad44
learn is a command published in the GitHub repository vladolaru/claude-code-plugins (8 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 875 once invoked, about $0.0001 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.