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Result Auditor

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cpt13-g
result-auditor

Validate and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.

Overview

Publishercpt13-g
Repositorybuild-research-evidence
Skill nameresult-auditor
Stars
50
Forks
2
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by cpt13-g on GitHub. Read the source before you install it.

Installation

Install the Result Auditor 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/cpt13-g/build-research-evidence/tree/main/.agents/skills/result-auditor
  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/cpt13-g/build-research-evidence.git /tmp/build-research-evidence
mkdir -p .claude/skills
cp -r /tmp/build-research-evidence/.agents/skills/result-auditor .claude/skills/result-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Result Auditor 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 Result Auditor 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 Result Auditor 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.

Result Auditor

Start a fresh isolated context with python tools/context_view.py show --role auditor --experiment E0001 --run-manifest <path> (replace ID/path). The packet supplies complete Card/contract, a verified manifest and raw-source references without planner interpretations, global Claim status or next actions. Do not open full project state or planner chat before the first verdict. Read docs/PROTOCOLS.md, docs/ROLE_HANDOFFS.md and docs/CONTEXT_POLICY.md on a fresh context or changed hash; load docs/METHOD_REALIZATION_PROTOCOL.md for Candidate/refinement results and docs/EVIDENCE_COMPLETION_PROTOCOL.md for post-P0 aggregation. Inspect raw metrics and relevant logs/artifacts; an integrity check or excerpt alone is not scientific review. Fix validity, fairness and verdict before reconciliation. Record reviewed inputs, checks and any isolation exception. Execution failure is not evidence against the hypothesis.

Assign exactly one result type: SUPPORTING, FALSIFYING, BOUNDARY, NULL, CONFOUNDING, IMPLEMENTATION_FAILURE, or INCONCLUSIVE. Produce the structured Diagnosis required by the result schema: validity, convergence, baseline fairness, primary observation, Claim impact, performance/scientific value, confidence, suspected causes, unresolved confounds, failure level and one finite recommended action. Check seed/statistical protocol, data leakage and missing artifacts.

Use F1 only for execution failures and recommend Debug/Tuning. Use F2 for a valid but failed realization and identify implicated design dimensions before recommending refinement. Do not declare F3 in an individual Result Record; the Orchestrator applies the multi-Candidate hypothesis-risk gate after accumulating valid F2 evidence. A NULL result must retain explicit scientific value. Cheap results remain PRELIMINARY_NON_PAPER regardless of Metric.

Related preapproved runs may share one audit invocation, but inspect every manifest and retain separately linked Result Records, failed seeds, negative results and required statistical checks. Write schema-valid records linked to immutable manifests and preserve meaningful outcomes in memory. Recommend a diagnosis or next experiment when useful, but do not change code, method, queue, stage, budget or Claim status directly; the Orchestrator applies decisions. Lack of isolation uses the existing FULL_CONTEXT_EXCEPTION path, never an invented blind verdict.

For Claim-Evidence aggregation, preserve supporting, falsifying, null and boundary findings as references. Assess Directness, Validity, Consistency and Confound Control without averaging them; missing primary evidence, invalid evaluation or unfair baselines are hard failures. Cheap and Tuning results remain outside formal evidence.

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 Result Auditor AI skill do?

Validate and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.

Why use Result Auditor on TypingMind?

Because you install it once and use it with any model. Result Auditor 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 Result Auditor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cpt13-g/build-research-evidence/tree/main/.agents/skills/result-auditor. 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 Result Auditor?

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 Result Auditor?

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

Is the Result Auditor AI skill free?

Yes. It is published on GitHub by cpt13-g 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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