Borrowing it
Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/health-check/SKILL.mdgit clone --depth 1 https://github.com/tusharagg1/aifolimizerWrote 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/tusharagg1/aifolimizer/health-check)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/health-check"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/health-check/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/tusharagg1/aifolimizer/health-check"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/health-check.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 | $0.00086 | $0.00521 |
| Opus 5 | $0.00043 | $0.00260 |
| Sonnet 5 | $0.00017 | $0.00104 |
| Haiku 4.5 | $0.00009 | $0.00052 |
Grade B, and why
health-check scanned grade B with 2 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 11d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
`~/.claude/settings.json`. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `curl -s http://127.0.0.1:8000/health` if the FastAPI backend is expected up. What it actually says
Health Check (Harness Self-Diagnostic)
Goal
Catch the silent-failure class a solo operator hits with no second pair of eyes: stale WS token, MCP server that won't import, a service import break, missing hooks. Output is a PASS/WARN/FAIL report with the one thing to fix.
How to run
Step 1 - Run the deterministic diagnostic:
backend/.venv/Scripts/python.exe backend/scripts/health_check.py
Reports: python version, mcp_server import + tool count, core service imports, WS session token freshness (vs WS_TOKEN_TTL_HOURS), settings.json hook events.
Step 2 - Live data-source check (only if Step 1 PASS):
mcp__aifolimizer__get_data_source_reliability- per-source success rate + latency over the trailing window. Flags providers degrading (yfinance / FRED / CoinGecko / SEC).
Step 3 - Optional backend reachability:
curl -s http://127.0.0.1:8000/healthif the FastAPI backend is expected up.
Interpreting results
- ws_session WARN (stale) → re-auth: delete
~/.aifolimizer/ws_session.jsonand re-run WS login, or runmcp_login. - mcp_server_import FAIL → a recent edit broke import; run
py_compile mcp_server.pyand read the traceback. - core_services FAIL → a service has a syntax/dependency error; the detail names the module + exception.
- settings_hooks missing event → a hook didn't register; check
~/.claude/settings.json. - data_source low success rate → provider outage or rate-limit; the cached layer will serve stale data until it recovers.
Output
Relay the script's OVERALL line + any non-PASS rows, then the single highest-priority fix. Don't paste the full report unless asked.
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.
- 11d ago First seen · 48 lines · 86 tokens per session scan B ef2c3bfa6097
health-check is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 9d ago), licensed MIT. It adds 86 tokens to every session and 521 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
gitee-expert
You have access to Gitee (gitee.com), China's largest GitHub-style platform. This skill teaches the effective workflows for discovery, intel, translation and webhook monitoring.
backtest-diagnose
Diagnose failed or underperforming backtests, locate the root cause, and fix the issue.
utility-pm-skill-auditor
Run a repo-wide cross-cutting governance audit via the pm-skill-auditor sub-agent. Aggregates the enforcing validator suite, re-derives aggregate counters, and surfaces cross-cutting issues no single validator catches, graded P0/P1/P2/P3 with a machine-readable status. Use for pre-release readiness checks or a…
deliver-edge-cases
Documents edge cases, error states, boundary conditions, race conditions, and recovery paths for a feature - the systematic catalog of what can go wrong and the failure modes to design for. Use during specification to map the failure surface and ensure comprehensive coverage, or during QA planning to identify boundary…
debug
Structured bug diagnosis and fixing workflow that reproduces, diagnoses root cause, applies a minimal fix, writes regression tests, and scans for similar patterns.
gsd-forensics
Post-mortem investigation for failed GSD workflows — analyzes git history, artifacts, and state to diagnose what went wrong.