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 chipi/agentic-ai-homelab --skill perf-diagnosegit clone --depth 1 https://github.com/chipi/agentic-ai-homelabWrote 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/chipi/agentic-ai-homelab/perf-diagnose)<a href="https://agentmods.dev/skills/chipi/agentic-ai-homelab/perf-diagnose"><img src="https://agentmods.dev/badge/skills/chipi/agentic-ai-homelab/perf-diagnose/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/chipi/agentic-ai-homelab/perf-diagnose"><img src="https://agentmods.dev/badge/skills/chipi/agentic-ai-homelab/perf-diagnose.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 | $0.00106 | $0.01228 |
| Opus 5.5 | $0.00042 | $0.00491 |
| Sonnet 5.5 | $0.00021 | $0.00246 |
| Haiku 4.5 | $0.00011 | $0.00123 |
Grade A, and why
perf-diagnose 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 17d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
perf-diagnose
A stall has a cause you can measure. This is the order that found one fast (podcast_scraper,
2026-10-16), written down so the next one does not start from scratch. The ladder ends where
replay-proven-fix begins.
1. Read what production already recorded (no SSH yet)
- Load
o11y-reviewfor the window: logs, errors and traces around the stall. - Look for: the exact request that did not answer (an access-log line missing is itself the signal), what else was running then (warmers, migrations, backfills), and per-step timings if the service logs them.
- Write down what is known and what is not, before forming a theory (rule: never invent root causes).
2. Name the candidate mechanism
| Symptom | Usual mechanism | How to tell |
|---|---|---|
| A request that is fast alone does not answer while a background job runs, then answers in seconds | GIL starvation: a CPU-bound thread in the same Python process; a syscall-heavy request gives up the GIL on every call and queues to get it back | step 3 bench |
| Every request waits the same long time, then all answer together | a lock or single-flight around a cold build | grep for locks / single_flight on the path; time with the job off |
| Only under many concurrent requests | worker pool or connection pool exhausted | count workers; the access log's concurrency |
| Box-wide slowness, every process | CPU or memory saturation (other agents' runs, an OOM loop) | uptime, ps aux | sort -nrk3 | head |
3. Reproduce locally
For GIL starvation, the bench times a probe (what the request does) idle, under the suspected load in-process, and with the load in a child process:
python3 ~/.claude/skills/perf-diagnose/scripts/contention_bench.py --demo # see the effect
<project>/.venv/bin/python ~/.claude/skills/perf-diagnose/scripts/contention_bench.py \
--path <repo>/src --probe pkg.mod:walk_like_the_request --load pkg.mod:score_like_the_warmer
Both functions take no arguments; write two small wrappers in the scratchpad if needed.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 17d ago First seen · 85 lines · 106 tokens per session scan A bdd791fec3a1
perf-diagnose is a skill published in the GitHub repository chipi/agentic-ai-homelab (3 stars, last pushed today), licensed MIT. It adds 106 tokens to every session and 1,228 once invoked, about $0.0004 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 2026-10-18.
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