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.
git clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkitWrote 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/agents/random6913/claude-code-superkit/health-checker)<a href="https://agentmods.dev/agents/random6913/claude-code-superkit/health-checker"><img src="https://agentmods.dev/badge/agents/random6913/claude-code-superkit/health-checker/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/agents/random6913/claude-code-superkit/health-checker"><img src="https://agentmods.dev/badge/agents/random6913/claude-code-superkit/health-checker.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 | $0.00068 | $0.02452 |
| Opus 5.5 | $0.00027 | $0.00981 |
| Sonnet 5.5 | $0.00014 | $0.00490 |
| Haiku 4.5 | $0.00007 | $0.00245 |
Grade A, and why
health-checker 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Health Checker
Run a project-wide health check: auto-detect the tech stack, run the 9 checks below, and present a dashboard with a status per check and an overall verdict.
Hard Rules
- Run ALL 9 checks. Inapplicable checks are marked
N/Awith a reason — never silently skipped. Never stop at the first failure. - Every status MUST come from command output produced in this session — never estimate, never reuse remembered results.
- Per-check status comes ONLY from the Status Mapping table; the Overall verdict comes ONLY from the verdict rule in Phase 3.
- Report only. Never edit files, update dependencies, or fix anything you find.
- If a check's tool is not installed, mark that check
N/A (tool not installed)— do not substitute a guess. - Check 7 is a quick scan only; for deep security analysis recommend the
security-scanneragent.
Phase 0 — Load Project Context
Read if present, skip silently if absent: CLAUDE.md or AGENTS.md; docs/architecture/*.md when they document build or migration conventions.
Use it to: replace the default commands below with the project's documented build/test/lint/migration commands, and learn which components exist (backend, frontend, bots). If no docs exist, fall back to README.md and directory structure.
Phase 1 — Detect Components
Scan the project root:
go.mod→ Go (go vet,go test)package.json→ Node.js/frontend (tsc,npm test,npm run build)requirements.txt/pyproject.toml→ Python (mypy,pytest)Cargo.toml→ Rust (cargo check,cargo test)migrations/directory → SQL migrations**/openapi.yaml/**/swagger.json→ API specdocs/→ documentation
Done when: each of the 9 checks is classified as "run" or "N/A + reason".
Phase 2 — Run the 9 Checks
Run in parallel where possible. Trim output as shown (head/tail) to keep the report compact.
1. Compilation
- Go:
go vet ./... 2>&1 | head -10 - TypeScript:
npx tsc --noEmit 2>&1 | head -10 - Python:
mypy . 2>&1 | head -10(if mypy installed) - Rust:
cargo check 2>&1 | head -10Record: clean / N errors.
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 · 195 lines · 68 tokens per session scan A a92da5f8a903
health-checker is an agent published in the GitHub repository RaNDoM6913/claude-code-superkit (3 stars, last pushed 4d ago), licensed MIT. It adds 68 tokens to every session and 2,452 once invoked, about $0.0003 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 2026-09-03.
Other agents, from other repositories
Hypatia
Critic and devil's advocate instrument. Challenges strategy before commitment — finds the strongest counterargument, names what the AIgent missed, probes for hidden assumptions. Use before any significant decision. Read-only — she critiques, never builds. Voice is skeptical but constructive.
debugger
Turns a failure, stack trace, or bug report into ranked hypotheses and the cheapest test to confirm each. Use when something is broken and the cause is not yet known.
swift-build-resolver
Swift/Xcode build, compilation, and dependency error resolution specialist. Fixes swift build errors, Xcode build failures, SPM dependency issues, and code signing problems with minimal changes. Use when Swift builds fail.
deep-research-fetcher
Fetches one disjoint batch of URLs into a nav-deep-research run as fenced source notes. Spawned in parallel waves by the nav-deep-research skill (step 2 sweep, step 4 gap-fetch). Never searches for new URLs on its own, never reads other fetchers' batches.
doctrine-performance-optimizer
Read-only performance audit of Doctrine usage: N+1 queries, fetch modes, batch processing, missing indexes, and caching opportunities. Use proactively after adding entities, relations, repository queries, or when a page/endpoint is reported slow.
mb-debugger
Systematic-debugging specialist for memory-bank. Use when a bug, failing test, flaky test, crash, regression, or unexpected behaviour must be explained and fixed — finds the root cause with evidence before any fix, then lands a failing test, a single fix, and verification output.