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 Kaddo-kdd/kaddo --skill learning-capturegit clone --depth 1 https://github.com/Kaddo-kdd/kaddoWrote 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/kaddo-kdd/kaddo/learning-capture)<a href="https://agentmods.dev/skills/kaddo-kdd/kaddo/learning-capture"><img src="https://agentmods.dev/badge/skills/kaddo-kdd/kaddo/learning-capture/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/kaddo-kdd/kaddo/learning-capture"><img src="https://agentmods.dev/badge/skills/kaddo-kdd/kaddo/learning-capture.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 | $0.00032 | $0.00244 |
| Opus 5.5 | $0.00013 | $0.00098 |
| Sonnet 5 | $0.00006 | $0.00049 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
learning-capture 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 yesterday.
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
Learning Capture Skill
Purpose
Standardize how a Work Item's learning is captured when it closes.
When to use
When finishing a Work Item, after implementation and verification.
Inputs
The Work Item, the diff/result, and any decisions or surprises that came up.
Output
A learning record: what was implemented, what changed, what was learned, what decision emerged, which knowledge must be updated, and what remains pending.
Rules
- Do not close a Work Item without validation.
- Do not hide failures; record them honestly.
- Do not assume everything is done if errors remain.
Quality checklist
- Implemented vs changed vs learned are distinct.
- Knowledge to update is named (ADR / capabilities / current-state).
- Pending items are listed.
Example output
A short learning section appended to the Work Item or a learning note.
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.
- yesterday First seen · 42 lines · 32 tokens per session scan A af68feefaef1
learning-capture is a skill published in the GitHub repository Kaddo-kdd/kaddo (54 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 244 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 2026-09-25.
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