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 runxhq/runx --skill diagnose-skill-rungit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/diagnose-skill-run)<a href="https://agentmods.dev/skills/runxhq/runx/diagnose-skill-run"><img src="https://agentmods.dev/badge/skills/runxhq/runx/diagnose-skill-run/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/runxhq/runx/diagnose-skill-run"><img src="https://agentmods.dev/badge/skills/runxhq/runx/diagnose-skill-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00035 | $0.01113 |
| Opus 5 | $0.00017 | $0.00557 |
| Sonnet 5 | $0.00007 | $0.00223 |
| Haiku 4.5 | $0.00003 | $0.00111 |
Grade A, and why
diagnose-skill-run 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 6d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose Skill Run
Diagnose what went wrong in a skill or graph execution and propose the smallest change that fixes it.
Resolve receipt_id through the native ledger read runner, then combine its
redacted receipt detail with the supplied failure summary or harness output.
The native detail is authoritative for status, verification, authority, acts,
decisions, criterion status, references, and seal posture. It deliberately
excludes hydrated step output, stdout, stderr, credential values, context
bodies, and local paths; those details must come from supplied bounded failure
evidence when they are necessary.
Distinguish root cause from symptoms. A graph may report failure at step 4, but the root cause may be bad output from step 2 that propagated through context passing. Trace data flow backward through context edges to find where the problem originated.
Classify the failure:
- Input error — required input missing or malformed. Fix: input validation or input resolution.
- Scope denial — step requested scopes outside the graph grant. Fix: scope declarations or grant configuration.
- Tool failure — CLI tool or adapter returned an error. Fix: tool invocation (args, env, cwd) or the tool itself.
- Schema mismatch — step output did not match expected shape for downstream context. Fix: output parsing or artifact contract.
- Timeout — step exceeded time budget. Fix: increase timeout, reduce work, or split the step.
- Policy denial — transition gate blocked the step. Fix: gate conditions or upstream output.
- Review rejection — adversarial review found blocking issues. Fix: the code or spec, not the review process.
- Harness assertion — fixture expectations did not match actual output. Fix: skill logic or stale fixture expectations.
Composes
ledger#read
Agent-mediated suspension is not a failure
A receipt sealed with reason needs_agent, or whose graph status is deferred, denotes a healthy
agent-mediated suspension, not a defect. The runtime yielded to the
caller for missing agent or human input.
This is a normal part of graph execution, not one of the failure
classes above. When the only evidence is needs_agent without
any exit code, scope denial, schema mismatch, or other concrete
failure signal, return verdict: pass with an empty
improvement_proposals array and note that the graph is paused as
designed.
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.
- 6d ago First seen · 112 lines · 35 tokens per session scan A 561b70f0514e
diagnose-skill-run is a skill published in the GitHub repository runxhq/runx (84 stars, last pushed yesterday), licensed Apache-2.0. It adds 35 tokens to every session and 1,113 once invoked, about $0.0002 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 2026-09-03.
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