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 agentmods add skills/smallnest/goal-workflow/smellnpx skills add smallnest/goal-workflow --skill smellgit clone --depth 1 https://github.com/smallnest/goal-workflowWrote 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/goal-workflow/smell)<a href="https://agentmods.dev/skills/smallnest/goal-workflow/smell"><img src="https://agentmods.dev/badge/skills/smallnest/goal-workflow/smell.svg" alt="Measured on agentmods" 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 | $0.00097 | $0.09205 |
| Opus 5 | $0.00048 | $0.04602 |
| Sonnet 5 | $0.00019 | $0.01841 |
| Haiku 4.5 | $0.00010 | $0.00920 |
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 6d 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.
This is a copy
89% identical to smell — 99 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 751 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 candidate signals and evidence - Validate candidates against context, callers, history, workload, and measurements before confirming findings
- Generate a detailed markdown report saved to
tasks/smell-report-[timestamp].md - Present a summary of confirmed findings and separate candidates 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.
- 6d ago First seen · 751 lines · 97 tokens per session scan A 0823878cb8b8
smell is a skill published in the GitHub repository smallnest/goal-workflow (278 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 9,205 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to smell, differing in 99 lines, and is treated as a copy.
Other skills, from other repositories
mass-line
触发：当你需要收集多方意见、把零散反馈整合成可执行方案，或把方案带回真实使用者/执行者验证时调用；常见信号包括 stakeholder input、user feedback、意见汇总、对齐与验证。 English: Trigger when input must be gathered from many people, synthesized into a clearer plan, and returned to the affected users or executors for validation. Use this skill for a collect-synthesize-validate loop.
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触发：当你面临的任务明显需要多个思想武器协作时调用；常见信号包括：从零启动新项目、攻坚复杂疑难问题、对已有方案进行迭代优化。此 skill 提供标准化的跨 skill 工作流组合，解决"应该先用哪个 skill、怎么衔接"的问题。 English: Trigger when a task clearly requires multiple skills in sequence. Use this skill to select a standard workflow that chains skills together, defines data handoff between steps, and specifies…
concentrate-forces
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protracted-strategy
触发：当目标长期、任务复杂、资源暂时处于劣势，或短期无法速胜但又不能放弃时调用；常见信号包括 long-term effort、phased plan、endurance、战略耐心、需要分阶段推进。 English: Trigger when the work is long-horizon, difficult, and unlikely to be won quickly. Use this skill to divide the effort into stages, keep strategic confidence, and accumulate small wins into overall victory.
practice-cognition
触发：当你提出了方案、假设或判断，需要通过实践验证、试错迭代或复盘升级认知时调用；常见信号包括 experiment、prototype、validate、iterate、feedback loop。 English: Trigger when an idea, hypothesis, or plan must be tested in practice and improved through iteration. Use this skill to move from action to understanding and back to action in a spiral learning loop.
contradiction-analysis
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