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 smallnest/pigo --skill smellgit clone --depth 1 https://github.com/smallnest/pigoWrote 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/smallnest/pigo/smell)<a href="https://agentmods.dev/skills/smallnest/pigo/smell"><img src="https://agentmods.dev/badge/skills/smallnest/pigo/smell/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/smallnest/pigo/smell"><img src="https://agentmods.dev/badge/skills/smallnest/pigo/smell.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 | $0.00097 | $0.08045 |
| Opus 5.5 | $0.00039 | $0.03218 |
| Sonnet 5.5 | $0.00019 | $0.01609 |
| Haiku 4.5 | $0.00010 | $0.00805 |
Grade A, and why
smell 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- smell — 89% identical, 99 lines differ
How it starts
The opening of the file, as written. The whole thing — 690 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Smell — Architecture Bad Smell Detector
Analyze a codebase to find violations of software architecture principles, anti-patterns, code "bad smells," and algorithmic complexity hotspots. Produce a comprehensive, actionable markdown report.
Knowledge base: This skill encodes architectural patterns, anti-patterns, code smells, and algorithmic complexity heuristics drawn from industry research and practice, including the classic code smells catalog by Martin Fowler / Kent Beck (as organized on refactoring.guru: Bloaters, Object-Orientation Abusers, Change Preventers, Dispensables, Couplers).
The Job
- Understand the scope — ask what part of the project to analyze (full project, specific module, or recent changes)
- Scan the codebase using
find,grep, andAgent(Explore subagent) to gather evidence - Identify architectural smells and anti-patterns
- Generate a detailed markdown report saved to
tasks/smell-report-[timestamp].md - Present a summary of findings to the user
Step 1: Scope Clarification
Ask the user:
What scope should I analyze?
A. Entire project (thorough, may take time)
B. Specific module/directory: [please specify]
C. Only recently changed files (git diff)
D. Only architectural-level issues (skip low-level code smells)
If the user doesn't specify, default to option A for small projects (< 100 files) or C for large projects.
Step 2: Evidence Gathering
Use the Explore subagent (Agent with subagent_type: "Explore") to scan the codebase for architectural patterns and anti-patterns. Run multiple parallel explorations:
Exploration Commands
Run these in parallel to gather evidence efficiently:
- Project Structure Scan: Map the directory tree, identify the architectural style (layered, modular monolith, microservices, etc.)
- Dependency Analysis: Find import/include patterns, check for circular dependencies, identify coupling hotspots
- Module/Component Scan: Identify God Objects (files > 500 lines), check cohesion, check single responsibility violations
- Pattern Detection: Look for known anti-pattern signatures (static cling, service locator abuse, leaky abstractions)
- Testing Scan: Check test coverage patterns, test file locations, test-to-code ratios
- Naming & Clarity Scan: Flag misleading names, overly generic names (Manager, Helper, Util), inconsistent naming conventions
- Complexity Scan: Detect algorithmic complexity hotspots — nested loops, N+1 queries, repeated scans, sort-in-loop, expensive recomputation in render paths
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.
- 1mo ago First seen · 690 lines · 97 tokens per session scan A a52f32a0f739
smell is a skill published in the GitHub repository smallnest/pigo (483 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 8,045 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
autofix
Review and repair current local changes until they converge, or run Qwen Code Autofix issue and review workflows from GitHub Actions.
correctness-reviewer
Always-on code-review persona. Reviews code for logic errors, edge cases, state management bugs, error propagation failures, and intent-vs-implementation mismatches. Spawned by the ce:review-beta skill as part of a reviewer ensemble.
performance-reviewer
Conditional code-review persona, selected when the diff touches database queries, loop-heavy data transforms, caching layers, or I/O-intensive paths. Reviews code for runtime performance and scalability issues. Spawned by the ce:review-beta skill as part of a reviewer ensemble.
reliability-reviewer
Conditional code-review persona, selected when the diff touches error handling, retries, circuit breakers, timeouts, health checks, background jobs, or async handlers. Reviews code for production reliability and failure modes. Spawned by the ce:review-beta skill as part of a reviewer ensemble.
diagnosing-bugs
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
code-review
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use…