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 impactbrussels/AINativeOS --skill capture-learninggit clone --depth 1 https://github.com/impactbrussels/AINativeOSWrote 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/impactbrussels/ainativeos/capture-learning)<a href="https://agentmods.dev/skills/impactbrussels/ainativeos/capture-learning"><img src="https://agentmods.dev/badge/skills/impactbrussels/ainativeos/capture-learning/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/impactbrussels/ainativeos/capture-learning"><img src="https://agentmods.dev/badge/skills/impactbrussels/ainativeos/capture-learning.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 | $0.00155 | $0.01000 |
| Opus 5.5 | $0.00062 | $0.00400 |
| Sonnet 5.5 | $0.00031 | $0.00200 |
| Haiku 4.5 | $0.00015 | $0.00100 |
Grade A, and why
capture-learning 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 1mo 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture Learning
The OS gets smarter only when real outcomes are banked. A skill that ran once and taught you nothing was a waste of the run; the same mistake made twice is the OS failing at its one job. This skill takes a thing that actually happened, an eval score, a customer reply, a number that moved, a launch that died, and writes it down as a dated, sourced lesson the next founder inherits. Append, never overwrite. Memory you can edit away is not memory.
The method
Full append protocol, what is worth capturing, the file-routing table, a worked KB entry,
anti-patterns, and a copyable template: references/learning-loop-method.md.
Step 1 - Capture the real outcome
Write what happened, not what you hoped. The eval scored 0.71, not "the agent works well". The customer asked for a refund, not "mixed signal". A guess dressed as a result poisons the file for everyone downstream, so if you cannot point to the run, the reply, or the metric, you are not done capturing.
Step 2 - Write the dated lesson
Use the PLAYBOOK-LESSONS format: a one-line lesson as the heading, then Context, What happened, Lesson, Source. State the lesson so the next founder avoids the mistake, not so you feel resolved about it.
Step 3 - Append to the right file
Pick the home by shape of the learning:
| The learning is... | Append to |
|---|---|
| A durable rule that should govern every design | PRINCIPLES.md |
| A concrete tactic, one move and its reason | DOS-AND-DONTS.md |
| A dated outcome from one real run or case | PLAYBOOK-LESSONS.md |
| Evidence about what compounds or what trust you earned | MOATS-LEDGER.md |
Step 4 - Append, never overwrite
Add below the existing entries. Past lessons stay even when later ones sharpen them; the record of being wrong is itself the lesson.
Output
- One logged lesson, dated and sourced, appended to the correct knowledge-base file.
Constraints
- Append, never overwrite. The history is the asset.
- A real outcome, not a guess. No run, reply, or metric means nothing to bank yet.
- Stay theme-agnostic; the lesson must read true for a founder in any sector.
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.
- 1mo ago First seen · 71 lines · 0 tokens per session scan A b0b68cbc58c4
capture-learning is a skill published in the GitHub repository impactbrussels/AINativeOS (1 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 155 tokens to every session and 1,000 once invoked, about $0.0006 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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