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/facebook/agentic-tools/api-healthnpx skills add facebook/agentic-tools --skill api-healthgit clone --depth 1 https://github.com/facebook/agentic-toolsWrote 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/facebook/agentic-tools/api-health)<a href="https://agentmods.dev/skills/facebook/agentic-tools/api-health"><img src="https://agentmods.dev/badge/skills/facebook/agentic-tools/api-health.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 | $0.00137 | $0.01255 |
| Opus 5 | $0.00068 | $0.00628 |
| Sonnet 5 | $0.00027 | $0.00251 |
| Haiku 4.5 | $0.00014 | $0.00126 |
Grade A, and why
api-health 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 3d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Health
Monitor API usage, rate limits, and deprecations for a Meta app.
Workflow
-
Start tracking. Before any other work, call
devtools_skill_invocationwith actionstartandskill_nameapi-health. Passskill_nameapi-healthon everydevtools_*tool call in the following steps. -
Identify the app. Ask the user for the app name or ID. If they give a name (or aren't sure of the ID), call
devtools_app_list(actionlist) and resolve it to anapp_id— match the name case-insensitively. If several apps match or it's ambiguous, show the candidates (name, ID, viewer role) and ask the user to pick. If they give a numeric ID, use it directly. -
Collect API health data in parallel:
devtools_api_usagewith actionrate_limits— current rate limit status per metricdevtools_api_usagewith actioncall_volume— total calls vs quotadevtools_api_usagewith actiondeprecations— deprecated APIs and migration guides
-
Analyze and report:
Report Format
Rate Limits
- Report the
overall_statusthe response already carries —healthy,warning,critical,throttled, orunmetered. Do not re-derive it from the percentage; the server classifies on an unrounded value, so a borderline case can readcriticalwhileusage_percentagedisplays 100. usage_percentagefor thecall_count_usage_ratemetric (0–100, rounded for display)- Effective users count (DAU/WAU/MAU aggregate) — the denominator the quota is multiplied by
cooling_down_minutes: estimated minutes until unblock, 0 when not over quota- On
unmetered, say the app is not metered rather than reporting 0% — it means no usable headroom reading, not spare capacity
Call Volume
- Total calls and quota
- Usage rate (calls/quota ratio, 0.0–1.0)
- Interpret it on the same bands the platform uses for rate limits: >= 0.7 approaching, >= 0.9 critical, 1.0 means the app is at its quota
- If the user provided an endpoint filter, show filtered results
- Report the
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.
- 3d ago First seen · 78 lines · 137 tokens per session scan A 44494aa363d2
api-health is a skill published in the GitHub repository facebook/agentic-tools (5 stars, last pushed 13d ago), licensed MIT. It adds 137 tokens to every session and 1,255 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…