Borrowing it
Nothing to install: this file belongs to FilipNowakowicz/nixos-config. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/FilipNowakowicz/nixos-config/main/.agents/skills/review-learning-candidates/SKILL.mdgit clone --depth 1 https://github.com/FilipNowakowicz/nixos-configWrote 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/filipnowakowicz/nixos-config/review-learning-candidates)<a href="https://agentmods.dev/skills/filipnowakowicz/nixos-config/review-learning-candidates"><img src="https://agentmods.dev/badge/skills/filipnowakowicz/nixos-config/review-learning-candidates/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/filipnowakowicz/nixos-config/review-learning-candidates"><img src="https://agentmods.dev/badge/skills/filipnowakowicz/nixos-config/review-learning-candidates.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 | $0.00039 | $0.01617 |
| Opus 5.5 | $0.00016 | $0.00647 |
| Sonnet 5.5 | $0.00008 | $0.00323 |
| Haiku 4.5 | $0.00004 | $0.00162 |
Grade A, and why
review-learning-candidates 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Learning Candidates
Review compact learning candidates under .agents/learning/candidates/.
Candidate review is explicit, batch-oriented work. Do not run it as part of
normal task wrap-up.
End state of a review pass: every candidate touched is either implemented in a
PR (and promoted), superseded, rejected, or consciously left open with a
reason. The promotion and the candidate's status flip travel together in the
same branch, and promoted/rejected/superseded files are moved out of the
open queue so the index stays lean.
Goal
Turn candidate proposals into the strongest useful repo artifact:
- assertion / test / CI gate
- hook
- skill
- doc
- rejection
Prefer executable enforcement over prose. A candidate that can become a check,
test, or invariant should not be promoted to CLAUDE.md.
For candidates already promoted by review and routed implement-fix with
best_form: test, best_form: assertion, best_form: invariant, or
best_form: check, use
.agents/learning/scripts/scaffold-promotion-check.sh to create the failing
check stub and scripts/validate.sh wiring before writing prose. The generated
stub is intentionally red until you replace its TODO body with the real
assertion, and it keeps a back-reference to the candidate for audit.
Workflow
1. Triage from metadata
- Run
bash .agents/learning/scripts/validate-candidates.sh. - Run
bash .agents/learning/scripts/review-candidates.sh— read status counts, the route/form grouping, and the Expired open candidates list. - Run
bash .agents/learning/scripts/check-candidate-liveness.sh— it flags open candidates whose evidence already landed (PR merged / commit on HEAD). - Choose a small batch by
route,best_form, or relatedtargets. Open only the candidate files in that batch.
2. Decide one outcome per candidate
Resolve liveness first: if check-candidate-liveness.sh reports LIKELY
RESOLVED, or you confirm by hand that the fix already landed, the route is
moot — mark it superseded and do not re-implement. NO SIGNAL is not a clean
bill of health (the evidence just lacks a pointer); judge it from evidence and
targets. Treat an implement-fix candidate as "is a fix still needed?", never
as "a fix is owed" — the fix is often already in tree.
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 · 138 lines · 39 tokens per session scan A 39749f305328
review-learning-candidates is a skill published in the GitHub repository FilipNowakowicz/nixos-config (5 stars, last pushed 6d ago), licensed MIT. It adds 39 tokens to every session and 1,617 once invoked, about $0.0002 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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