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 suxrobGM/jobpilot --skill cleanupgit clone --depth 1 https://github.com/suxrobGM/jobpilotWrote 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/suxrobgm/jobpilot/cleanup)<a href="https://agentmods.dev/skills/suxrobgm/jobpilot/cleanup"><img src="https://agentmods.dev/badge/skills/suxrobgm/jobpilot/cleanup.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 93 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00095 | $0.02867 |
| Opus 5 | $0.00048 | $0.01434 |
| Sonnet 5 | $0.00019 | $0.00573 |
| Haiku 4.5 | $0.00010 | $0.00287 |
Grade A, and why
cleanup 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 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.
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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Cleanup
Systematic review-then-refactor of a target path (file, folder, module, package, or feature) in any language or framework. Every judgment is calibrated against the host project's own conventions and the ecosystem's idioms, not generic taste. Behavior-preserving by default; architecture and pattern changes are in scope when they pass the net-simplification test (below). The argument is the target path(s); if none given, ask.
Process
1. Calibrate
- Detect the stack: languages, package manifests, build system, framework(s), and the project's own verification commands (typecheck/compile, lint, tests, formatter). Read them from project docs and manifests - never guess. These commands are the gate for every later phase.
- Read the project's conventions FIRST - CLAUDE.md/AGENTS.md, rules files, lint/formatter config, editorconfig. Note the size ceiling, naming scheme, comment policy, and framework idioms (e.g. an auto-memoizing compiler makes manual memoization a finding, not a virtue; a DI container makes
newin handlers a finding). - Inventory the target's files with line counts, largest first.
- Assess the safety net: does the verification harness actually cover the target? If tests are thin, prefer low-risk phases, lean harder on adversarial verification, and consider adding characterization tests before risky structural work.
2. Map the boundary
- Find every consumer outside the target (grep the target's path/package/symbol names). Record which symbols cross the boundary - the public surface that moves/renames must preserve. If the target is a published library, the surface includes consumers you cannot see: treat its exported API as frozen unless the user says otherwise.
- Invisible callers: enumerate symbols invoked without an import - framework-registered routes/handlers/lifecycle hooks, DI/IoC registrations, reflection and dynamic dispatch (
getattr,Method.invoke, message selectors), serialization/ORM field names, config- or convention-referenced classes, CLI entry points, FFI exports, template references, scheduled jobs, migrations. Grep alone cannot prove these dead. - Map the reverse direction (what the target reaches into) and flag misfiled code: anything inside the target consumed only by a different feature, judged by who calls it and whose data it touches.
- Enumerate adjacent plumbing - out-of-tree files that wire the target in: routes/pages/handlers that mount it, DI wiring, build/config entries, data-access helpers and cache keys, localization files, docs/feature-map entries, CI steps. Plumbing is review scope, not just context: dead code hides there, and moves inside the target often require updating it.
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 · 94 lines · 95 tokens per session scan A da50e2ad0e1b
cleanup is a skill published in the GitHub repository suxrobGM/jobpilot (65 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 2,867 once invoked, about $0.0005 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 2026-08-30.
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.
phx-challenge
Challenge mode reviews - rigorous questioning before approving changes. Use when you want thorough scrutiny of Ecto changes, LiveView events, OTP designs, or PR readiness.
sonarqube
Levantar SonarQube con Docker, analizar el código y proponer mejoras. También: análisis estático, deuda técnica, code smells, cobertura, calidad automatizada.
devflow
Agile development workflow for AI coding agents, usable from Codex, Claude Code, and any Agent Skills client. Use when the user asks to plan a GitHub issue, write a feature or bug specification, implement an issue, run validation checks, review a pull request, research a topic, or plan an epic. Also triggered by…
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…
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…