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 OctoPerf/octoperf-claude-plugins --skill octoperf-scenario-diagnosisgit clone --depth 1 https://github.com/OctoPerf/octoperf-claude-pluginsWrote 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/octoperf/octoperf-claude-plugins/octoperf-scenario-diagnosis)<a href="https://agentmods.dev/skills/octoperf/octoperf-claude-plugins/octoperf-scenario-diagnosis"><img src="https://agentmods.dev/badge/skills/octoperf/octoperf-claude-plugins/octoperf-scenario-diagnosis/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/octoperf/octoperf-claude-plugins/octoperf-scenario-diagnosis"><img src="https://agentmods.dev/badge/skills/octoperf/octoperf-claude-plugins/octoperf-scenario-diagnosis.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 | $0.00131 | $0.05779 |
| Opus 5 | $0.00066 | $0.02890 |
| Sonnet 5 | $0.00026 | $0.01156 |
| Haiku 4.5 | $0.00013 | $0.00578 |
Grade A, and why
octoperf-scenario-diagnosis 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 12d 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 — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OctoPerf — Scenario / bench-result diagnosis
A scenario run produced metrics that look bad — high error rate, high response times, low throughput, premature stop. This skill walks the diagnosis: read metrics → narrow down → match the symptom to one of four root-cause classes → surface the right fix.
Inputs
You need a benchResultId from one of:
- A user-supplied id (often from a Slack / email link they paste).
- The return of
mcp__octoperf__run_scenario(scenarioId). mcp__octoperf__list_bench_reports_by_project(projectId)filtered onbenchResultIdsfor the UI deep-link.
If mcp__octoperf__get_bench_result(benchResultId) shows
state ∉ {FINISHED, ABORTED, ERROR} the test is still running.
Either wait and re-check, or surface what has been measured so far
with the caveat that it may change. When you do wait, follow
octoperf-async-polling — bounded Bash sleep between polls, cadence
sized to the scenario's expected duration. Use get_bench_result.state
as the terminal check; get_bench_status returns elapsed-% and is for
progress display only.
Steps
0. Did the run even start?
Before reading metrics, confirm the run actually produced samples.
run_scenario can fail before any HTTP traffic is generated —
infrastructure error, no matching plan, deserialisation issue,
configuration rejected. A diagnosis built on metrics from a run that
never started will mislead the user.
mcp__octoperf__get_bench_result(benchResultId)
The exhaustive state machine is CREATED → PENDING → SCALING → PREPARING → INITIALIZING → (ERROR | RUNNING) → (FINISHED | ABORTED).
Any other label is a transport / UI artefact.
-
state = FINISHED→ proceed to step 1. -
state = ABORTED→ either manual stop or stall-abort; jump to the jmeter.log signature catalogue. -
state = ERROR→ the run errored during provisioning or startup, no samples to read. Pull the orchestration logs:mcp__octoperf__list_bench_docker_logs(benchResultId)
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.
- 12d ago First seen · 361 lines · 131 tokens per session scan A f83efa3bdad2
octoperf-scenario-diagnosis is a skill published in the GitHub repository OctoPerf/octoperf-claude-plugins (0 stars, last pushed 5d ago), licensed Apache-2.0. It adds 131 tokens to every session and 5,779 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.
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