Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/assafkip/kipi-systemnpx agentmods add skills/assafkip/kipi-system/learn-from-correctionWrote 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/assafkip/kipi-system/learn-from-correction)<a href="https://agentmods.dev/skills/assafkip/kipi-system/learn-from-correction"><img src="https://agentmods.dev/badge/skills/assafkip/kipi-system/learn-from-correction/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/assafkip/kipi-system/learn-from-correction"><img src="https://agentmods.dev/badge/skills/assafkip/kipi-system/learn-from-correction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 172 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00045 | $0.02060 |
| Opus 5 | $0.00023 | $0.01030 |
| Sonnet 5 | $0.00009 | $0.00412 |
| Haiku 4.5 | $0.00005 | $0.00206 |
Grade A, and why
learn-from-correction 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 9d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn From Correction
You take a correction (what an agent proposed vs. what the human actually did) and propose a principle edit to the skill/persona file that should have caught it. The proposal goes to q-system/output/skill-proposals/ for the founder to review and merge through normal git flow.
This skill exists because the best prompt today is not the best prompt a month from now. Corrections the founder is already making (rewrites of agent drafts, anti-pattern additions to Skeptic, copy edits) carry the signal needed to keep skills sharp - but only if something captures that signal as a durable principle.
Before writing anything, read references/principle-vs-rule.md. The guardrails there are load-bearing: principles transfer, rules overfit. A correction turned into a rule produces a brittle decision tree. The same correction turned into a principle reshapes how the agent reasons.
Constraints (ENFORCED)
- Never edit the target skill file directly. Output is always a proposal markdown in
q-system/output/skill-proposals/. The founder reviews, edits, and merges via normal git flow so Codex review fires on the diff (same gate as any other code change). - One correction at a time. If the founder hands you a batch of corrections, process them sequentially. Each one gets its own proposal file or its own section. Do not bundle unrelated corrections into one principle.
- Always include the source correction in the proposal. The founder needs to verify your interpretation. Quote the agent output, the human output, and your inferred diff.
- If the correction does not generalize, say so. Not every correction maps to a missing principle. Some are one-off context. The honest answer is sometimes: "this is a one-off, no principle change recommended."
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.
- 9d ago First seen · 173 lines · 45 tokens per session scan A 9fe953220c45
learn-from-correction is a skill published in the GitHub repository assafkip/kipi-system (110 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 2,060 once invoked, about $0.0002 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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