Diagnose Skill Run logo

Diagnose Skill Run

Organization
runxhq
diagnose-skill-run

Diagnose a failed or suspicious Runx skill execution from receipts and harness evidence, then propose the smallest bounded improvement without replaying the failed operation.

Overview

Publisherrunxhq
Repositoryrunx
Skill namediagnose-skill-run
Stars
105
Forks
109
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by runxhq on GitHub. Read the source before you install it.

Installation

Install the Diagnose Skill Run AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/runxhq/runx/tree/main/skills/diagnose-skill-run
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/diagnose-skill-run .claude/skills/diagnose-skill-run
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diagnose Skill Run in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Diagnose Skill Run on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Diagnose Skill Run is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 receipt-query 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

  • receipt-query#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.

One failure, one fix. Propose the smallest change that addresses the root cause. Do not bundle unrelated improvements.

Output

The stable failure packet, runx/diagnose-skill-run.diagnosis.v1, is consumed directly by skill-build improve. Agents should self-validate before returning, and downstream consumers (notably the skill-build improve runner) may validate on receipt.

  • verdict: pass, needs_update, or blocked.
  • failure_summary: which step, which failure class, what root cause. One to three sentences.
  • improvement_proposals: array of bounded changes. Each:
    • target: what to change (SKILL.md, execution profile, graph step, input, fixture)
    • change: what specifically to change
    • rationale: why this fixes the root cause
    • risk: what could go wrong
  • next_harness_checks: replayable checks that should pass after the fix.

Inputs

Supply whichever evidence is available:

  • receipt_id: receipt id to inspect.
  • receipt_summary: sanitized receipt or harness summary.
  • receipt_details: native redacted receipt projections for deterministic replay only; live runs resolve them from receipt_id.
  • harness_output: failed harness output or assertion text.
  • skill_path: path to the skill being improved.
  • receipt_rows: native-projection rows for deterministic replay only; live runs resolve receipt_id from the configured receipt store.

Agent task contracts

diagnose-skill-run

Diagnose one failure from native redacted receipt detail plus the supplied summary or harness output. Treat needs_agent or a deferred receipt as a healthy suspension unless another concrete failure signal exists. Ground authority, acts, decisions, criteria, and seal posture in receipt_evidence.receipt_details; treat caller summaries as supplemental evidence, never as a replacement for native facts. Distinguish input, scope, tool, schema, timeout, policy, review, and harness failures. If receipt_id was supplied but native evidence did not match it, return blocked unless the supplied harness output alone proves the defect. Return verdict, a concise failure_summary, at most three bounded improvement_proposals, and replayable next_harness_checks. Each proposal must name target, change, rationale, and risk. Do not write files or weaken a refusal.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Diagnose Skill Run AI skill do?

Diagnose a failed or suspicious Runx skill execution from receipts and harness evidence, then propose the smallest bounded improvement without replaying the failed operation.

Why use Diagnose Skill Run on TypingMind?

Because you install it once and use it with any model. Diagnose Skill Run is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Diagnose Skill Run in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/diagnose-skill-run. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Diagnose Skill Run?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Diagnose Skill Run?

As many as you like. As long as a model supports skills, you can use Diagnose Skill Run with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Diagnose Skill Run AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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