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Harness Feedback

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AnastasiyaW
harness-feedback

Use when an agent says a test, VM, proof, evaluator, or release gate is overloaded, too strict, blocking staging, or causing false positives; split checks by profile, measure the burden, preserve high-risk evidence, and verify the smallest corrected workflow. Do not use for ordinary test selection, a single test failure, or a full security audit without a harness-scope question.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill nameharness-feedback
Stars
212
Forks
32
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Harness Feedback 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/harness-feedback
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/development/harness-feedback .claude/skills/harness-feedback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Harness Feedback 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 Harness Feedback 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 Harness Feedback 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.

Harness Feedback

Treat "the harness is too strict" as an engineering finding, not as permission to disable a safety check. Find the boundary that owns the mismatch and move the check to the narrowest profile that actually needs its evidence.

Profiles

Use these profiles unless the project has a more specific, documented contract:

ProfilePurposeTypical blocking checks
staging-smokeFast proof that the changed build starts and the critical path worksbuild, focused regression, one stable smoke/contract check
security-proofProve an adversarial or trust-boundary claimhostile tests, source/collector proof, fresh-context evaluator
release-attestationProve the exact releasable artifact and its identitysigning, Authenticode/tool identity, installer/package checks
nightly-stressFind intermittent and capacity failuresrace, stress, AV/OS matrix, long-running evals

staging-smoke must not require signing, production credentials, a release certificate, or a long VM stress run. security-proof may run on an unsigned staging build when its claim is source or runtime behavior. A release check may remain blocking for release promotion without becoming a per-edit gate.

Feedback Loop

For every overload signal, record:

  1. requested profile and change boundary;
  2. gate that blocked or dominated the run;
  3. command, elapsed time, failure count, and evidence actually produced;
  4. whether the gate was relevant, duplicated, flaky, or misplaced;
  5. the smallest profile split or deletion of duplicate coverage;
  6. a before/after run of the affected profile and a fresh review of the rule.

Use the deterministic harness-load-advisor.py signal as an intake event. It stores metadata outside the repository and forces the final report to name the mismatch. Durable policy changes belong in Git; raw session traces do not.

Required Report

Do not write "overkill" and move on. Report:

text
Harness feedback: OVERLOAD | CLEAR
Requested profile: staging-smoke | security-proof | release-attestation | nightly-stress
Mis-scoped gate: <name>
Evidence: <command, result, elapsed time, or explicit missing proof>
Correction: <profile split or rule change>
Verification: <before/after commands and result>
Residual risk: <what remains intentionally gated and where>

Gotchas

  • A fresh evaluator is an independence control, not a release-signing check.
  • A VM can be a reusable execution environment without forcing release identity checks into every VM smoke.
  • A green fast gate does not prove release readiness; a red release-only gate does not invalidate a staging smoke unless the staging claim depends on it.
  • Do not replace a misplaced gate with retries, sleeps, or a bypass marker.
  • Do not infer overload from one slow run; distinguish environment failure from a profile contract error.

Troubleshooting

SymptomLikely causeAction
Staging smoke asks for signingRelease gate leaked into staging profilesplit release-attestation and run the smoke on the unsigned staging artifact
Security proof blocks on a production VMRuntime environment and release identity are coupledkeep the VM, remove release-only assertions from the security profile
Same gate fails repeatedlyWrong scope, flaky boundary, or missing fixtureclassify the failure and add a focused reproducer; never silently retry
Agent says "tests passed" with no profileEvidence contract is incompleterequire the report fields above and the exact command/result
Fix removes a safety checkCausal ownership was not tracedrestore the check, document the narrower boundary, and re-verify it there

Frequently asked questions

What does the Harness Feedback AI skill do?

Use when an agent says a test, VM, proof, evaluator, or release gate is overloaded, too strict, blocking staging, or causing false positives; split checks by profile, measure the burden, preserve high-risk evidence, and verify the smallest corrected workflow. Do not use for ordinary test selection, a single test failure, or a full security audit without a harness-scope question.

Why use Harness Feedback on TypingMind?

Because you install it once and use it with any model. Harness Feedback 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 Harness Feedback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/harness-feedback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Harness Feedback?

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 Harness Feedback?

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

Is the Harness Feedback AI skill free?

Yes. It is published on GitHub by AnastasiyaW under the MIT 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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