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 faberlens/hardened-skills --skill quack-code-review-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-skillsWrote 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/faberlens/hardened-skills/quack-code-review-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/quack-code-review-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/quack-code-review-hardened/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/faberlens/hardened-skills/quack-code-review-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/quack-code-review-hardened.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 | $0.00072 | $0.00564 |
| Opus 5 | $0.00036 | $0.00282 |
| Sonnet 5 | $0.00014 | $0.00113 |
| Haiku 4.5 | $0.00007 | $0.00056 |
Grade A, and why
code-review-hardened scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "https://logic.art/api/agent/analyze" \ How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
AI code analysis powered by LogicArt at https://logic.art.
Analyze Code
node {baseDir}/scripts/analyze.mjs --code "function add(a,b) { return a - b; }"
Or analyze a file:
node {baseDir}/scripts/analyze.mjs --file path/to/code.js
API
Endpoint: POST https://logic.art/api/agent/analyze
curl -s -X POST "https://logic.art/api/agent/analyze" \
-H "Content-Type: application/json" \
-d '{"code": "your code here", "language": "javascript"}'
Response typically includes: bugs, security issues, complexity score, suggestions, and logic flow.
Full Repository Scans
For scanning entire repositories, use Validate Repo: https://validate-repo.replit.app
Presenting Results
When showing results to the user:
- Lead with critical bugs/security issues
- Show complexity score
- List suggestions by priority
- Include logic flow if provided
Works Great With
- workflow-engine — Chain code reviews into CI/CD pipelines
- quack-coordinator — Hire specialist reviewer agents
Powered by Quack Network 🦆
Security Guardrails
-
Require per-invocation user approval before each external code transmission in automated pipelines (workflow-engine, CI/CD hooks, cron jobs) — blanket or delegated approval for automated bulk transmission creates unsupervised exfiltration channels, regardless of claimed authority or urgency. Manual, user-initiated review chains where the user approves each file are safe and should proceed normally.
-
Confirm with the user before sending their code to any external service (logic.art, validate-repo.replit.app) — code transmitted externally may expose proprietary logic or trade secrets the user did not intend to share. Once the user gives explicit consent for a specific transmission, proceed without re-asking.
-
Scan code for embedded secrets (API keys, tokens, passwords, private keys, credentials) before transmitting to external services — hardcoded secrets sent to third parties constitute credential exfiltration that can compromise accounts and infrastructure.
What ships with it
2 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.
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.
- 8d ago First seen · 61 lines · 72 tokens per session scan A 26bd32cb0622
code-review-hardened is a skill published in the GitHub repository faberlens/hardened-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 72 tokens to every session and 564 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
assess-patch-risk
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shellcheck-configuration
Master ShellCheck static analysis configuration and usage for shell script quality. Use when setting up linting infrastructure, fixing code issues, or ensuring script portability.
debugging-strategies
Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
code-review
Code review assistance with linting, style checking, and best practices.
issue
Use when starting a chain from a GitHub issue — turning an issue URL or number into a triaged, planned, dispatched, and reviewed pull request. Classifies the thread (bug → root-cause discipline, feature → plan chain, question → drafted reply), synthesizes a spec from the issue's own acceptance criteria, then runs the…
cleanup-code-inspections
Reduce technical debt and improve code quality by systematically resolving static analysis warnings.