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 heyAyushh/stacc --skill reviewgit clone --depth 1 https://github.com/heyAyushh/staccWrote 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/heyayushh/stacc/review)<a href="https://agentmods.dev/skills/heyayushh/stacc/review"><img src="https://agentmods.dev/badge/skills/heyayushh/stacc/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/heyayushh/stacc/review"><img src="https://agentmods.dev/badge/skills/heyayushh/stacc/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 | $0.00035 | $0.00193 |
| Opus 5.5 | $0.00014 | $0.00077 |
| Sonnet 5.5 | $0.00007 | $0.00039 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
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 26d 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.
What it actually says
Review stack
Choose one review interface:
- Branch-local cleanup edits:
deslop. - Explicit deep structural, maintainability review:
thermo-nuclear-code-quality-review. - React diagnostics:
react-doctor. - Surgical coding defaults:
karpathy-guidelines. - Implementation constraint:
ponytail. - Non-mutating over-engineering review:
ponytail-review(diff) orponytail-audit(whole repository). - Ponytail utilities:
ponytail-debt,ponytail-gain, orponytail-help.
The Ponytail entrypoints have distinct triggers; keep them separate. Read only
the selected child SKILL.md.
What ships with it
20 files 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.
- deslop/LICENSE.txt 1.0 KB
- deslop/SKILL.md 732 B
- karpathy-guidelines/SKILL.md 2.5 KB
- ponytail-audit/LICENSE.txt 1.0 KB
- ponytail-audit/SKILL.md 1.6 KB
- ponytail-debt/LICENSE.txt 1.0 KB
- ponytail-debt/SKILL.md 1.7 KB
- ponytail-gain/LICENSE.txt 1.0 KB
- ponytail-gain/SKILL.md 1019 B
- ponytail-help/LICENSE.txt 1.0 KB
- ponytail-help/SKILL.md 1.8 KB
- ponytail-review/LICENSE.txt 1.0 KB
- ponytail-review/SKILL.md 2.3 KB
- ponytail/LICENSE.txt 1.0 KB
- ponytail/SKILL.md 6.6 KB
- react-doctor/LICENSE.txt 1.7 KB
- react-doctor/references/explain.md 2.7 KB
- react-doctor/SKILL.md 2.0 KB
- thermo-nuclear-code-quality-review/LICENSE.txt 1.0 KB
- thermo-nuclear-code-quality-review/SKILL.md 12 KB
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.
- 26d ago First seen · 22 lines · 35 tokens per session scan A 1d0ab99d35f1
review is a skill published in the GitHub repository heyAyushh/stacc (3 stars, last pushed 26d ago), licensed MIT. It adds 35 tokens to every session and 193 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 2026-09-22.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…