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 softspark/ai-toolkit --skill reviewgit clone --depth 1 https://github.com/softspark/ai-toolkitWrote 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/softspark/ai-toolkit/review)<a href="https://agentmods.dev/skills/softspark/ai-toolkit/review"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/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/softspark/ai-toolkit/review"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.02866 |
| Opus 5 | $0.00015 | $0.01433 |
| Sonnet 5 | $0.00006 | $0.00573 |
| Haiku 4.5 | $0.00003 | $0.00287 |
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 3d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
$ARGUMENTS
Reviews code changes for quality and issues.
Changed files context
- Changes: !
git diff --stat main...HEAD 2>/dev/null || git diff --cached --stat 2>/dev/null || echo "no changes detected"
Signal Collection (never stop at the first red)
Collect every failing signal up front, then review the diff in full anyway:
| Signal | How to read it |
|---|---|
| Merge conflict with base | gh pr view --json mergeable,mergeStateStatus or git merge-tree |
| Failing CI checks | gh pr checks or the platform equivalent |
| Lint / typecheck failure | the project's own commands |
Each failing signal becomes a blocker finding. None of them ends the run.
A review that aborts on the first red signal spends the whole cycle repeating what the tracker already displayed, while the finding that would have told the author something new never gets written. One invocation produces the most complete picture of the change that it can.
Automated Diff Analysis
Before starting manual review, run the diff analyzer script to get a structured risk assessment:
python3 ${CLAUDE_SKILL_DIR}/scripts/diff-analyzer.py [base_branch]
# Default base branch: main
# Example: python3 ${CLAUDE_SKILL_DIR}/scripts/diff-analyzer.py develop
The script outputs JSON with:
- files: each changed file with additions, deletions, category (security/test/config/migration/infra/docs/logic), and risk level
- risk_score: overall assessment (high/medium/low)
- hotspots: top 5 files by additions
- secrets_scan: potential secret leaks detected in added lines
- test_coverage_estimate: whether test files accompany logic changes (good/partial/none)
- parallel_review_recommended: boolean flag
If the script reports parallel_review_recommended: true, use the Parallel Review (Agent Teams) mode below.
Parallel Review (Agent Teams)
For significant PRs or large changesets, create a parallel review team:
Create an agent team to review [target]:
- Teammate 1 (security-auditor): "Review for security vulnerabilities, auth issues,
injection risks, secret leaks. Report with severity ratings." Use Opus.
- Teammate 2 (performance-optimizer): "Check for N+1 queries, memory leaks,
unnecessary allocations, caching opportunities. Report with impact ratings." Use Opus.
- Teammate 3 (test-engineer): "Validate test coverage, edge cases, mock quality,
missing assertions. Report coverage gaps." Use Opus.
Each reviewer should report findings independently. Do NOT modify files.
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.
- 3d ago Changed · +5 lines b090bac58347
- 7d ago First seen · 276 lines · 29 tokens per session scan A 0c03d6356320
review is a skill published in the GitHub repository softspark/ai-toolkit (170 stars, last pushed 2d ago), licensed Apache-2.0. It adds 29 tokens to every session and 2,866 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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