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 tomzx/agents --skill review-learningsgit clone --depth 1 https://github.com/tomzx/agentsWrote 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/tomzx/agents/review-learnings)<a href="https://agentmods.dev/skills/tomzx/agents/review-learnings"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-learnings/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/tomzx/agents/review-learnings"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-learnings.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 | $0.00020 | $0.00698 |
| Opus 5 | $0.00010 | $0.00349 |
| Sonnet 5 | $0.00004 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
review-learnings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Learnings
Audits a learnings document and reports findings across four categories: actionability, specificity, completeness, and balance.
Prerequisites
- Apply the shared SDLC conventions in
skills/sdlc/references/shared.md. - If no argument is provided, locate the feature directory under
.sdlc/features/whose frontmatterissuefield references$ISSUE_NUMBER. .sdlc/knowledge/learnings/N-<slug>.md, or a learnings document provided in context or as a file path
Steps
- Read the learnings document from
.sdlc/knowledge/learnings/N-<slug>.mdif present, otherwise from context or as a file path. - Evaluate it against the checklist below.
- Report findings by category. Omit categories with no findings.
- After all findings are resolved: set the learnings document's frontmatter
statustocomplete.
Review Checklist
Actionability
- Do process improvements have a concrete action, an owner, and a target date?
- Are action items specific enough to execute without further clarification?
- Are improvements measurable so progress can be tracked?
Specificity
- Are observations specific to this project or sprint (not generic platitudes)?
- Do root causes go deeper than symptoms ("we ran late" → "why did we run late")?
- Are technical insights detailed enough to be useful in the next project?
Completeness
- Are both positive and negative learnings captured?
- Are technical and process dimensions both covered?
- If metrics were available, are they included?
- Are significant events (scope changes, blockers, surprises) addressed?
Balance
- Is the document balanced between what went well and what didn't?
- Are team contributions recognized in the positives?
- Is the tone constructive rather than critical of individuals?
Output Format
## Actionability
<Findings or "No issues found.">
## Specificity
<Findings or "No issues found.">
## Completeness
<Findings or "No issues found.">
## Balance
<Findings or "No issues found.">
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.
- 6d ago First seen · 95 lines · 20 tokens per session scan A c8e0a5780d45
review-learnings is a skill published in the GitHub repository tomzx/agents (6 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 698 once invoked, about $0.0001 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 2026-09-03.
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