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 fmind/dot --skill full-reviewgit clone --depth 1 https://github.com/fmind/dotWrote 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/fmind/dot/full-review)<a href="https://agentmods.dev/skills/fmind/dot/full-review"><img src="https://agentmods.dev/badge/skills/fmind/dot/full-review/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/fmind/dot/full-review"><img src="https://agentmods.dev/badge/skills/fmind/dot/full-review.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 | $0.00021 | $0.01443 |
| Opus 5.5 | $0.00008 | $0.00577 |
| Sonnet 5.5 | $0.00004 | $0.00289 |
| Haiku 4.5 | $0.00002 | $0.00144 |
Grade A, and why
full-review 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 10d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Review
Answer "perform a full review", "final check before release", or "make sure everything works and is up to date" with one deep pass that ends in a fixed, proven tree. The default outcome is applied verified fixes, not a findings list. repository-review owns read-only audits and severity. Unlike the recurring repository-maintenance upkeep pass, this skill inventories and reads every tracked area across all dimensions below, then fixes and proves the result with the full gate. When a repository ships its own verify skill (such as dot-verify), follow it and use this skill only for what it omits.
Arguments
Parse the request into one line and state it before starting: scope (paths, other repositories), exclusions ("skip data/"), depth (default deep: every tracked file in scope), mode (default fix; report stays read-only, skips the Fix and Prove steps, and lists findings under Fixed renamed Findings, ranked by repository-review severity), and follow-ups (release, commit, propagation; none unless requested). Ask only when an ambiguity changes scope, cost, or reversibility.
Workflow
- Baseline: record
git status --short, staged and unstaged diffs, the last tag, andgit fetchstate. Existing uncommitted work is part of the candidate unless the user says otherwise; never discard or restage it. Run the existing gates once to separate preexisting failures from regressions. - Load decisions: read
AGENTS.md,README.md, skills, changelog, and stored decisions (agent memory,## Decisionsnotes, ADRs). Do not re-raise kept or declined choices; mention one only when a new change reopens it. - Inventory coverage: list tracked files by area with
git ls-files, minus exclusions and generated or vendored files. Track each area to reviewed; "don't scratch the surface" means no unread area. For large trees, fan out read-only reviews by area to subagents or one session per area when the host supports it, then verify every returned finding yourself before acting. - Review each dimension in the review matrix, plus what reviews here repeatedly request:
- Works: every task and CLI entry point runs clean with no warnings (smoke); production or deployed endpoints are probed read-only when they exist.
- Current: tools, dependencies, actions, and runtimes against their latest releases (upgrade-tools); report drift and upgrade when the request says so.
- Simple: dead code, legacy paths, orphan files, stale config, unearned abstractions, and verbose docs (trim); prefer deletion and consolidation, never a heavier mechanism.
- Consistent: names, descriptions, versions, and metadata match across code,
pyproject.toml, docs, GitHub (github-repository), and the website; ignore files fit the stack (update-ignores). - Secure and private: secrets, permissions, dependencies, and agent tools (code-security); private repositories stay private and public ones disclose nothing sensitive.
- Documented:
README.md,AGENTS.md, skills, and*.mdmatch behavior, stay concise, and convince their audience (repository-docs). - Fast: slow tasks, tests, CI, or pages with a measured cause (benchmark).
- Fix: apply each verified finding with the smallest change; add a regression test for behavior fixes. Prefer one coherent change per finding so selective reverts stay easy. Hold back changes that are irreversible, outward-facing, costly, or that change a deliberate design, and list them as decisions.
- Prove: rerun only the checks a fix invalidated, then the full gate (
mise run allor the repository's equivalent) on the final tree. Confirm the tested snapshot equals the candidate. - Report: per the template below. Keep it short; evidence goes in file:line references, not narration.
- Follow up on request: release or commit through git-delivery or the repository's release skill; propagate to dependent repositories by repeating this pass there; when the user says "apply all" after a report-mode run, resume at the Fix step.
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.
- 10d ago Changed 366396bc2db4
- 12d ago Changed 09354349e277
- 14d ago Changed c8fdc7dd9103
- 15d ago Changed · +9 lines · -16 tokens per session 17c79bb1cc4f
- 1mo ago First seen · 53 lines · 37 tokens per session scan A bbd7ea1d9913
full-review is a skill published in the GitHub repository fmind/dot (12 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,443 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 2026-09-03.
Other skills, from other repositories
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fix-and-retry
Diagnose a failing CI run, apply the code fix, commit it, push, and watch the re-run until it passes or fails -- the full fix-and-retry loop in one shot. Use after a pipeline fails and the user says "fix it and retry", "fix the CI and push", or "make CI green". Do NOT use for a read-only investigation that stops at a…
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Configure pinned mise tools and canonical install, format, check, test, build, and watch tasks shared by hooks and CI.
dependabot
Automated dependency updates using GitHub Dependabot — configuration, ecosystem mapping, and validation.
github-actions
Configure GitHub Actions CI/CD so workflows run the same mise format, check, test, and build gate as local hooks; lint with actionlint and Zizmor; use keyless deploy templates.
systematic-debugging
Diagnose unknown-cause bugs, test/build or auth failures, flakes, and runtime performance regressions. Investigate, reduce, localize, falsify hypotheses, and explain root cause before implementation.