Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add jeffrigby/somepulp-agents/plugin install codebase-healthWrote 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/jeffrigby/somepulp-agents/dead-code)<a href="https://agentmods.dev/skills/jeffrigby/somepulp-agents/dead-code"><img src="https://agentmods.dev/badge/skills/jeffrigby/somepulp-agents/dead-code/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/jeffrigby/somepulp-agents/dead-code"><img src="https://agentmods.dev/badge/skills/jeffrigby/somepulp-agents/dead-code.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 | $0.00034 | $0.00807 |
| Opus 5.5 | $0.00014 | $0.00323 |
| Sonnet 5.5 | $0.00007 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
dead-code 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dead Code Detection
Analyze the project for dead code using automated tools with agent verification.
Mode
$ARGUMENTS
- detect (default): Find and report dead code, no modifications
- cleanup: Find dead code, then offer to remove after user approval
Tools Used
- JavaScript/TypeScript: knip (
npx knip) - Python: deadcode (
pip install deadcode)
Project type is auto-detected from package.json, tsconfig.json, requirements.txt, or pyproject.toml.
IMPORTANT: False Positive Handling
Dead code tools return many false positives. The agent MUST verify each finding before reporting:
Verify Before Reporting
For each flagged item:
- Read the flagged code to understand its context
- Check for dynamic references:
import(variable),require(variable), reflection - Check framework patterns: React components, decorators, middleware
- Check re-exports: Is this in an index.ts barrel file for public API?
- Check entry points: CLI scripts, serverless handlers, workers
Common False Positives to Filter
- Dynamic imports:
import(modulePath),require(variableName) - React/Vue components used in JSX/templates
- Decorated classes (Angular, NestJS, Python decorators)
- Re-exports for public API in index files
- Entry points configured outside standard patterns
- Test utilities only used in test files
- CLI scripts or serverless handlers
Workflow
- Invoke the
dead-code-cleanupagent to run detection and verification:- The agent runs the detection tool via
"${CLAUDE_PLUGIN_ROOT}"/scripts/dead-code-detect.sh - It parses findings into categories and verifies each item (reads code, filters false positives)
- It returns a verified report plus a proposed cleanup plan grouped by confidence (safe / needs-review)
- The agent runs the detection tool via
- Present the returned report and plan to the user
- If cleanup mode: use AskUserQuestion to ask which groups to remove
- Apply only the approved removals
Report Format
Present findings as:
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 · 93 lines · 34 tokens per session scan A 3180e23dfa4f
dead-code is a skill published in the GitHub repository jeffrigby/somepulp-agents (8 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 807 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 2026-09-09.
Other skills, from other repositories
audit
Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.
anti-patterns
Catalogue of known SDLC anti-patterns that greatcto agents must actively reject when reviewing architecture, plans, code, or post-mortems. Used by architect (pre-impl), pm (planning), senior-dev (impl), l3-support (post-incident).
skeptical-triage
Reusable 3-round self-challenge + arbiter pattern for filtering false positives from findings/verdicts. Use when the cost of a false-positive gate block exceeds the cost of 4 extra LLM turns.
roslyn-query
Query .NET/C# codebases using Roslyn AST analysis via dotnet run file. Use for tracing data flow, auditing API usage, finding pattern violations, or ad-hoc codebase queries.
apex-review
Cross-cutting review of recent work — catches gaps between specialists. Use when asked to "review what we built", "check the work", "pre-launch review", or after completing a significant chunk of work.
shipyard-debugging
Investigates root cause systematically before any fix is proposed. Use when encountering any bug, test failure, unexpected behavior, or error message. Also use for tracebacks, exceptions, "not working" complaints, build failures, performance problems, or integration issues, and whenever the next step would be to…