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 skills add imisic/claude-marketplace --skill a-self-learnergit clone --depth 1 https://github.com/imisic/claude-marketplaceWrote 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/imisic/claude-marketplace/a-self-learner)<a href="https://agentmods.dev/skills/imisic/claude-marketplace/a-self-learner"><img src="https://agentmods.dev/badge/skills/imisic/claude-marketplace/a-self-learner/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/skills/imisic/claude-marketplace/a-self-learner"><img src="https://agentmods.dev/badge/skills/imisic/claude-marketplace/a-self-learner.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 | $0.05545 |
| Opus 5.5 | $0.00007 | $0.02218 |
| Sonnet 5.5 | $0.00003 | $0.01109 |
| Haiku 4.5 | $0.00002 | $0.00554 |
Grade A, and why
a-self-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 14d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Learner: Review → Rule/Skill Feedback Loop
Closes the loop between review skills and rule/skill files. When a project review catches the same class of issue repeatedly, this skill proposes a preventive update (a new rule, a new preflight check, or a whitelist entry) and hands it off to a-rules-optimizer or a-review-optimizer for application.
Input: optional flags
--dry-run(default): analyze and propose, never write--apply: after each proposal, ask user, apply on approval--since YYYY-MM-DD: only consider findings from this date forward--threshold N: override recurrence threshold (default 3 for recurring, 5 for chronic)
If the project has no .claude/reviews/ directory yet, the skill reports "no history to learn from" and offers to scaffold the convention (see references/review-log-schema.md).
Core Principles
Propose, never silently write. Every rule/skill change must be shown to the user as a diff with rationale, then applied only after explicit approval. Propose only; never auto-apply and never auto-commit. A proposal workflow is not commit authorization, whatever your project or global commit rules say.
Delegate writes to the other optimizers. This skill never edits rule files or review SKILL.md directly. It generates a structured proposal and invokes a-rules-optimizer (for rule writes) or a-review-optimizer (for preflight/agent writes). Keeps each skill's scope tight.
Evidence > opinion. A recurrence claim needs ≥ N findings of the same category across distinct dates. "Feels recurring" is not good enough; the cluster must survive the grouping algorithm in references/recurrence-detection.md.
Record rejections. Proposals the user declines get logged to rejected-proposals.md with the reason. Next run shouldn't re-raise the same rejection: only surface if new evidence appears (e.g., the issue recurred another 5 times since rejection).
Per-project scope. Each project owns its own .claude/reviews/ directory. The skill does not cross-pollinate learnings between projects (that's a future extension; for now, deliberate isolation keeps project conventions separate).
What ships with it
6 files 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.
- 14d ago Changed · +14 lines 305ec7fe9daa
- 21d ago Changed · +30 lines 18cea7de2202
- 1mo ago First seen · 202 lines · 17 tokens per session scan A c8cb8d27d328
a-self-learner is a skill published in the GitHub repository imisic/claude-marketplace (1 stars, last pushed 8d ago), licensed MIT. It adds 17 tokens to every session and 5,545 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.
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