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 LegendApp/legend-skills --skill diagnosegit clone --depth 1 https://github.com/LegendApp/legend-skillsWrote 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/legendapp/legend-skills/diagnose)<a href="https://agentmods.dev/skills/legendapp/legend-skills/diagnose"><img src="https://agentmods.dev/badge/skills/legendapp/legend-skills/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/legendapp/legend-skills/diagnose"><img src="https://agentmods.dev/badge/skills/legendapp/legend-skills/diagnose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00118 | $0.01299 |
| Opus 5 | $0.00059 | $0.00649 |
| Sonnet 5 | $0.00024 | $0.00260 |
| Haiku 4.5 | $0.00012 | $0.00130 |
Grade A, and why
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 10d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
Use this workflow only after the explicit opt-in described in the frontmatter. A normal bug report or debugging request does not opt in to this skill.
Find and explain the cause of a symptom with 100% operational confidence.
Start from the user's concrete anchor: file, route, error, log, screen, branch, artifact, or reproduction step. Consult relevant architecture notes, ADRs, glossaries, and test docs. Inspect code, run safe tools or tests, and analyze existing artifacts.
Explicit invocation of Diagnose constitutes approval for behavior-neutral temporary instrumentation within the requested scope. Ask separately only for risky actions, inaccessible-state reproduction, or mutations that affect product behavior or external systems.
References
Load only what applies:
- Browser or React web: browser-react.md
- iOS, Android, React Native, macOS, TV, or physical device: app-device.md
- Logs, runtime probes, metrics, or debug hooks: instrumentation.md
Evidence Loop
-
Locate likely causes. Inspect the code path and existing evidence around the user's anchor. Form the smallest useful set of likely areas and falsifiable candidate causes, including independent or contributing causes when applicable. Give each cause a distinguishing prediction and identify where those predictions diverge.
-
Instrument before reproducing. Load instrumentation.md and add structured logging across the relevant boundaries, extensive enough to reconstruct the causal sequence rather than only record the visible symptom. Instrument competing causes in the same pass when practical so one reproduction can distinguish them.
Before reproducing, define the probe contract:
- the exact trigger and visible symptom
- the questions this run will answer
- each candidate cause's distinguishing prediction
- the events, fields, and correlation IDs that test those predictions
- how the evidence will be tied to the exact visible failure
- the intended process, build, window, document, and runtime identity when applicable
What ships with it
5 files 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.
- 10d ago First seen · 80 lines · 118 tokens per session scan A d588f9cdae8f
diagnose is a skill published in the GitHub repository LegendApp/legend-skills (39 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 1,299 once invoked, about $0.0006 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 2026-08-30.
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