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Validate

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boshu2
validate

Freshly judge whether a finished change and its claims meet original acceptance: PASS, FAIL or NOT_PROVEN. Use when: asked for a go/no-go, sign-off or independent verdict.

Overview

Publisherboshu2
Repositoryagentops
Skill namevalidate
Stars
450
Forks
42
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by boshu2 on GitHub. Read the source before you install it.

Installation

Install the Validate 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/boshu2/agentops/tree/main/plugin/skills/validate
  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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/skills/validate .claude/skills/validate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Validate 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 Validate 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 Validate 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.

Validate

Freshly judge one finished candidate against its original acceptance, return PASS, FAIL, or NOT_PROVEN with criterion-level evidence, and stop. Neighbours: advice or a second look is Review; whether a stated claim holds is Reality Check. This file carries every rule the judgment needs. Linked files, including RPI boundaries and mechanics, add depth only: if one cannot be read, judge from this file and say which was unavailable.

Rules that decide the verdict

  • The author cannot issue a binding PASS, and advisory findings cannot stand in for this judgment. An author's summary, confidence or assurance is a claim to check, not evidence; explanation alone is not proof.
  • Each criterion needs its own evidence on the exact candidate. A criterion nobody demonstrated goes in not_checked and never counts toward PASS.
  • A changed test, tolerance, golden, suppression or acceptance text must still satisfy the original intent. Green obtained by weakening the oracle is FAIL for the criterion it was meant to prove, and that green is not evidence. The same holds for green obtained by changing what the check runs against: product code that behaves differently when it detects a test or CI environment, a failure path newly silenced by an empty handler or discarded error, or a check made non-blocking, skipped or bypassed. Each needs a reason visible in the change that still serves the original intent.
  • Read receipts; do not re-run checks the author ran on this exact subject or that CI will run. Re-execute only a risk-critical claim that has no receipt.
  • A necessary finding never becomes an optional caveat or non-goal.

Decide in this order:

  1. The subject changed during judgment, changed-path coverage is incomplete, or author and validator identity or freshness is missing, colliding or unattested: NOT_PROVEN.
  2. A criterion is proven failed, or a change is proven out of scope: FAIL, even when other criteria are unverified.
  3. Any in-scope criterion lacks evidence: NOT_PROVEN.
  4. Every criterion is verified with evidence, checked scope is nonempty and not_checked is empty: PASS.

Establish intent first

An explicit request for Validate, an acceptance verdict or independent proof that original acceptance is met selects this skill, even when phrased as "review this". Generic checking or readiness questions, even with criteria supplied, do not: ask once whether the caller wants advice or an acceptance judgment, and wait. Issue no verdict or readiness approval meanwhile; missing intent is not a NOT_PROVEN verdict. Shared routing: advice or acceptance.

When a fresh judgment is worth it

Spend validation where a mistake is costly. For an ordinary change the author's checks and CI are the gate, and no fresh judgment is owed. Use Validate when:

  • the caller asks for an acceptance verdict or independent proof;
  • a mistake cannot be cheaply undone after it lands: a published release or instructions users will follow, a security boundary, destroying data or tracker state, deleting a check that protects the product; or
  • no deterministic check covers the behavior that changed.

Judge once. Report NOT_PROVEN with its gaps and stop; do not request or wait for another round. After the author repairs findings, the affected checks confirm the repair; a second judgment happens only when the caller asks for one. Keep the judgment's cost a fraction of the cost of the work: when it approaches that cost, stop and return what is unchecked.

Subject

The subject is a nonempty implementation candidate, held unchanged after required checks and known repairs; plans, audits and reviews are subjects only when the caller requested document review. Supplied failed-acceptance evidence means FAIL on that subject; do not review a moving repair. Bind its identity at the start and again at the end of judgment:

  • With AgentOps installed: ao provenance manifest --root "$REPO_ROOT" --include "$CHANGED_PATH", one --include per changed path.
  • In any Git repository: the commit SHA (git rev-parse HEAD) and the changed paths (git diff --name-only <base>...HEAD); for uncommitted work, the git status --porcelain listing and a shasum -a 256 of each changed file.
  • A subject supplied only in the conversation, such as a pasted diff or a described change, is exactly what was supplied; anything it does not show is unverified.

A requested retrospective follows the code judgment and is not evidence for it. When intent bundles both, judge the code criteria separately and keep the overall request incomplete until the retrospective exists; issue no overall PASS early. An explicitly requested review of the retrospective judges that document on its own scope.

Identity and freshness

Author and validator identities must be explicit and distinct, and freshness must be attested by the runtime or the caller, naming the attester. An attestation is a declared trust fact, not cryptographic isolation. In ordinary use these count:

  • Author: the identity the caller gives for whoever produced the candidate: a person, a session or agent ID, or the commit author.
  • Validator: this context's runtime ID when the runtime exposes one, such as a subagent or session ID; otherwise the handle the dispatcher holds for it, such as the agent ID returned at launch, or "this conversation" when the caller opened it for the judgment.
  • Freshness: a statement from the runtime, the dispatcher or the caller, naming who makes it, that this context did not produce the candidate and was given intent, subject and evidence rather than the author's working history. When the caller opened this conversation for the judgment and supplied the candidate, the caller is the attester.

A role name, a persona switch inside the author's conversation, or the validator vouching for itself does not count. Never invent an identity. An identity gap makes the result NOT_PROVEN; keep every finding in the report.

Reviewers

Default to one fresh reviewer in the author's model family: Codex/OpenAI for Codex/OpenAI, Claude/Anthropic for Claude/Anthropic, on the runtime's configured capable model unless pinned. Supply task-specific intent, scope, exact subject and relevant evidence, without full author history, desired verdict or peer conclusions. Concise input must not omit necessary evidence; retrieve more source when a criterion requires it.

Cross-model review is opt-in: --cross-model [model] is a skill prompt selection, not an AO flag, adding a fresh other-family reviewer through model-dispatch. A required leg that cannot run yields diversity_unsatisfied and NOT_PROVEN for the combined request, even if another leg passed; optional diversity that is unavailable is disclosed without erasing findings. Delivered FAILs and dissent stand; neither voting nor model preference makes a split PASS, and agreement is not proof of truth. No fixed ten-minute cap applies; respect real caller/native bounds without renewing them. A timeout is missing judgment, not FAIL.

Judgment

  1. Bind the subject. Verify continuity with the exact caller-owned intent, cited evidence and complete changed-path coverage; missing integrity is NOT_PROVEN.
  2. Revisit the original accepted behavior examples, including those in the conversation or bead, and check each observable result and its domain meaning on the exact candidate. A new test or renamed concept cannot replace an unfulfilled scenario. Inspect the actual diff against every criterion; publication or provenance claims in docs need verifiable evidence too. Risk sets depth: acceptance, permissions, tests and gates, stopping, disclosure, hooks and executable controls warrant deeper reading, including prose policy. Unknown risk merits examination, not extra reviewers.
  3. Classify commands before running any. Regeneration, synchronization, formatting and --force mutate the subject until proven otherwise; run them only on a disposable copy or a committed subject, never the judged tree.
  4. Bind the subject again; a mismatch is NOT_PROVEN.
  5. Return one result in the shape below, promptly, and stop.

Report

text
Verdict: PASS | FAIL | NOT_PROVEN
Subject: <manifest digest, or commit SHA and changed paths>; unchanged start to end: yes | no
Criteria:
  1. <criterion> - verified | failed | not verified - <evidence: file:line, receipt, observed output>
Findings: <class> - <what and where> - <consequence>   (or "none")
Notes (optional, do not change the verdict): <...>
Checked: <what was inspected, and how>
Not checked: <in-scope acceptance not verified>   (empty only for PASS)
Identity: author <id>; validator <id>; freshness attested by <runtime | caller>: <attester>

A finding fails an acceptance criterion or would mislead a user, break install or the CLI, or remove protection for the product; anything else is an optional note the author may ignore. Give each new finding a short stable class, reused on recurrence, and say whether it is pre-existing, introduced or unknown from before/after evidence; counts and timestamps alone do not establish cause. Keep prior findings visible. not_checked holds in-scope acceptance that was not verified; other limits stay in criterion reasoning, declared non-goals or residual-risk prose, never hidden to obtain PASS. Delivery inside acceptance stays unverified until its evidence exists; never remove that criterion to reach PASS. Mechanics covers report proportion and where each scope limit lives.

Validate is the sole semantic author of verdict.v2. Only when the caller requests machine-readable evidence or a declared consumer requires it, persist through ao provenance store-verdict (mechanics); Go verifies structure and storage, not truth. Otherwise return the result through the caller's existing channel, without hidden machine artifacts. Validate owns no repair, retry, delivery or tracker transition: known findings go back to the author for direct repair, and a causal stall uses the RPI single-helper rule.

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 Validate AI skill do?

Freshly judge whether a finished change and its claims meet original acceptance: PASS, FAIL or NOT_PROVEN. Use when: asked for a go/no-go, sign-off or independent verdict.

Why use Validate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/plugin/skills/validate. 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 Validate?

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 Validate?

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

Is the Validate AI skill free?

Yes. It is published on GitHub by boshu2 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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