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 agentmods add skills/impactbrussels/ainativeos/capture-learningnpx skills add impactbrussels/AINativeOS --skill capture-learninggit clone --depth 1 https://github.com/impactbrussels/AINativeOSWhat 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 | $0.00155 | $0.01000 |
| Opus 5 | $0.00077 | $0.00500 |
| Sonnet 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 2d 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.
- 2d 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 2mo ago), licensed Apache-2.0. It adds 155 tokens to every session and 1,000 once invoked, about $0.0008 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 2026-08-31.
Other skills, from other repositories
self-assessment
Interactive skill assessment with personalized learning path generation.
teach-me
Teach the learner to deeply understand a body of work — the current session's changes, a PR, an ADR, or any topic they name — the problem and why it existed, the solution and why it was built that way, and why it matters, incrementally and confirming mastery at each stage before advancing. Use when the user wants to…
ase-meta-eli5
Explain a topic in "Explain Like I'm 5" (ELI5) style, optionally grounded in Internet/Web facts. Use when the user wants a topic explained in a very simple, child-friendly way, or mentions "eli5" or "explain like I'm 5".
onboard
Quick onboarding to an existing codebase. Generates a mental model of the architecture, key patterns, and how things connect. Use when joining a new repo or when a teammate needs to get up to speed fast.
quickstart
Guided interactive tour of the framework. Walks through your first sprint from zero to shipped PR.
prospec-learn
Feedback Promotion Pipeline - Collect session corrections, repeated verify FAILs and recurring review criticals into a version-controlled lessons ledger; score them with an explicit, reproducible rule (frequency + impact modules); and promote - only with explicit human approval - across three tiers (accumulating…