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 skills/jhlee0409/all-for-claudecode/learnernpx skills add jhlee0409/all-for-claudecode --skill learnergit clone --depth 1 https://github.com/jhlee0409/all-for-claudecodeWrote 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/skills/jhlee0409/all-for-claudecode/learner)<a href="https://agentmods.dev/skills/jhlee0409/all-for-claudecode/learner"><img src="https://agentmods.dev/badge/skills/jhlee0409/all-for-claudecode/learner.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.01757 |
| Opus 5 | $0.00007 | $0.00879 |
| Sonnet 5 | $0.00003 | $0.00351 |
| Haiku 4.5 | $0.00001 | $0.00176 |
Grade A, and why
afc:learner 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 6d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/afc:learner — Pattern Learning & Rule Promotion
Reviews correction patterns detected from your sessions and promotes approved ones to project rules in
.claude/rules/afc-learned.md.
Arguments
$ARGUMENTS— (optional) action:review(default) — review pending patterns and promote to rulesstatus— show learner status (enabled/disabled, queue size, rule count)reset— clear the signal queue without promotingenable— create learner config to start collecting signalsdisable— remove learner config to stop collecting
Execution Steps
0. Action Routing
Parse $ARGUMENTS:
- If "enable": create
.claude/afc/learner.jsonwith{"enabled": true, "createdAt": "{ISO timestamp}"}, then output status and exit - If "disable": remove
.claude/afc/learner.jsonif it exists, then output "Learner disabled" and exit - If "reset": remove
.claude/.afc-learner-queue.jsonlif it exists, then output "Queue cleared" and exit - If "status" or empty with no queue: show status and exit
- Otherwise: proceed to review flow
1. Load Context
- Read
.claude/.afc-learner-queue.jsonl(JSONL format — one JSON object per line) - If queue is empty or file does not exist:
- Fallback: git history analysis — scan recent fix/chore commits (
git log --oneline --grep="fix" --grep="chore" -n 25) for recurring correction patterns (e.g., repeated lint fixes, naming changes, convention enforcement). Extract up to 5 candidate patterns from commit messages and diffs. - If fallback also finds nothing: output "No pending patterns and no recurring corrections in recent history. Use
/afc:learner enableto start collecting signals automatically." and exit - If fallback finds candidates: continue to Step 2 with git-derived entries (mark
source: "git-history"to distinguish from queue entries)
- Fallback: git history analysis — scan recent fix/chore commits (
- Read
.claude/rules/afc-learned.mdif it exists (for deduplication) - Read
CLAUDE.md(project root) if it exists (for conflict detection) - Count pending signals:
{N} patterns pending(note source: queue vs git-history)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 132 lines · 14 tokens per session scan A da89b72c6868
afc:learner is a skill published in the GitHub repository jhlee0409/all-for-claudecode (7 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 1,757 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.
Other skills, from other repositories
issue-triage
Issue triage: audit open issues, categorize, detect duplicates, cross-ref PRs, risk assessment, post comments. Args: "all" for deep analysis of all, issue numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
performance
CLI performance optimization - startup time, memory usage, token savings benchmarking.
build-teaql-app
Build or change a TeaQL application in Java, Rust, Go, Swift, Python, C#/.NET, or TypeScript, including Kotlin/JVM applications that consume Java-generated libraries. Mandatory order: first draft and save a complete KSML model, then verify the client and evaluate that saved model, repair it through repeated evaluation…
moai-workflow-worktree
Git worktree management for parallel SPEC development with isolated workspaces, automatic branch registration, and seamless MoAI-ADK integration. Use when setting up parallel development environments.
moai-kanban-foreman
One unattended kanban foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test…
moai-workflow-thinking
Sequential Thinking MCP for structured step-by-step analysis via --deepthink flag. Separate from UltraThink which is Claude's native extended reasoning mode. Use for multi-step analysis or architecture decisions.