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 chengkj99/kj-skills --skill ai-learning-loopgit clone --depth 1 https://github.com/chengkj99/kj-skillsWrote 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/chengkj99/kj-skills/ai-learning-loop)<a href="https://agentmods.dev/skills/chengkj99/kj-skills/ai-learning-loop"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/ai-learning-loop/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/chengkj99/kj-skills/ai-learning-loop"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/ai-learning-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00118 | $0.01169 |
| Opus 5 | $0.00059 | $0.00584 |
| Sonnet 5 | $0.00024 | $0.00234 |
| Haiku 4.5 | $0.00012 | $0.00117 |
Grade A, and why
ai-learning-loop 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 10d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Learning Loop
Run a compact learning workflow that turns a topic or material into a concrete output and a small number of reusable knowledge cards. Optimize for feedback density, not note volume.
Core Loop
Use this sequence:
learning goal
-> real question
-> minimal input or research pack
-> expression card
-> output review
-> one necessary card/sink decision
-> next loop question
Prefer one loop at a time. Do not turn the task into a broad research report or a large note-taking project unless the user explicitly asks.
Workflow
1. Clarify the learning target
Identify the user's intended output before collecting or summarizing information.
If the target is unclear, ask one concise question. Otherwise infer a practical target from context, such as a口播稿、公众号段落、课程小节、playbook、咨询框架, or decision memo.
Read references/question-planning.md when the request starts from a broad topic, has no material, or needs a minimal learning path.
2. Decide the input path
Choose one:
| Situation | Action |
|---|---|
| User provided material | Use that material as the first input. |
| Material exists in a wiki/repo | Search the local wiki/repo first, then read the closest sources. |
| User has no material | Build a minimal research pack before writing the expression card. |
| Topic is current or unstable | Use web search or official/current sources when available; mark any source-quality caveats. |
For no-material requests, do not gather many links. Produce a smallest useful pack: 3 must-read resources, up to 5 optional resources, skip list, reading order, and first output task.
3. Build an expression card
Create one publishable or near-publishable expression card around one core judgment. Read references/expression-card.md when drafting a口播稿、短文、课程小节、or other output card.
Treat口播稿 as an expression card:
raw/studio/funnel/formatted/shipin/ = pre-publish expression card
raw/studio/funnel/transcripts/ = post-publish asset card
What ships with it
4 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.
- 10d ago First seen · 110 lines · 118 tokens per session scan A abb3b0a4c7a5
ai-learning-loop is a skill published in the GitHub repository chengkj99/kj-skills (14 stars, last pushed 9d ago), licensed MIT. It adds 118 tokens to every session and 1,169 once invoked, about $0.0006 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 2026-08-30.
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