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/hautc-it/cilWrote 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/hautc-it/cil/learn)<a href="https://agentmods.dev/commands/hautc-it/cil/learn"><img src="https://agentmods.dev/badge/commands/hautc-it/cil/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/hautc-it/cil/learn"><img src="https://agentmods.dev/badge/commands/hautc-it/cil/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 | $0.00021 | $0.00413 |
| Opus 5.5 | $0.00008 | $0.00165 |
| Sonnet 5.5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00041 |
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.
What it actually says
/learn
Arguments: $ARGUMENTS
Persist a learning, decision, or constraint to memory.
Step 1 — Classify category
decision— architecture or design choice madeconstraint— limit or rule that must be respectedlearning— insight, lesson, or corrected assumptiontask— work item for future sessionsarchitecture— structural or system design fact
Step 2 — Pick a topic tag from this taxonomy
Prepend exactly one of these as the first tag, so future memory_search can filter by topic:
navigation— file paths, finding code, project layoutediting— code change patterns, refactoring techniquestesting— test approach, framework quirks, coverage gapsgit— commits, branches, merge/rebase patternsquality— lint, types, formatting, style rulescontext— when to clarify vs assume, scope rulesarchitecture— design decisions, module boundariesperformance— optimization, profiling, bottlenecksclaude-code— sessions, modes, CLAUDE.md, skills, subagents, hooks, MCPprompting— scope, constraints, acceptance criteriadebugging— root-cause findings, reproduction steps
Step 3 — Extract additional tags
From $ARGUMENTS, extract 1–4 more specific keywords (libraries, components, files).
Step 4 — Store
memory_store("[category]", "$ARGUMENTS", ["[topic-tag]", "tag2", "tag3"])
Step 5 — Confirm
Report: Stored [category] (topic: [topic-tag]): [brief summary]
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 · 51 lines · 21 tokens per session scan A 80b82b89c6a1
learn is a command published in the GitHub repository hautc-it/cil (1 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 413 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
unwrap
You are resuming a Claude Code session. Execute all steps below in order — no confirmation needed.
wrap-find
Search session logs for a keyword or phrase. The search term is everything after /wrap-find in the user's message.
claudikins-automatic-context-manager:manual-handoff
Manually trigger context handoff - skip waiting for 60% threshold.
claude-flow-memory
Interact with Claude-Flow memory system.
session-memory
Maintain context and learnings across Claude Code sessions for continuous improvement.
memory-manager
Knowledge management with Memory tools for persistent insights.