Validate logo

Validate

Community
boshu2
validate

Freshly judge exact subject content against bead or caller acceptance, optionally persist verdict.v2 for a declared consumer, and stop. Triggers: "validate", "independently validate", "vibe".

Overview

Publisherboshu2
Repositoryagentops
Skill namevalidate
Stars
450
Forks
42
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 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/packs/agentops-executor/agents/validator/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/packs/agentops-executor/agents/validator/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

Independently judge one exact subject against the acceptance in its existing bead or caller source, return one semantic result, and stop. Validate is the sole verdict.v2 writer when persistence is requested. It never asks the model to reconstruct Plan or Candidate packets.

Preconditions

  • The subject is a nonempty implementation candidate: the manifest lists at least one entry, and store-verdict refuses an empty one. Plans, audits, reviews, and other control artifacts are not completion subjects unless the caller explicitly requested document review.
  • The intent source is available as a caller-owned artifact or runtime-owned content-addressed snapshot; its acceptance digest is derived automatically.
  • The subject manifest still matches the subject.
  • Author and validator context IDs are explicit.
  • Freshness is explicitly attested with source: runtime | caller and an attester identity.

Missing, colliding, or unattested identities produce NOT_PROVEN. This is a declared trust fact, not cryptographic proof that contexts were isolated.

Cross-model fresh validator (caller-elected)

A caller may request that the fresh validator run on a different model than the author. Dispatch via the controller-session recipe in the agent-native model-dispatch recipe (codex-exec and/or NTM panes, probed at runtime). Record author and validator model_identity in evidence refs and freshness attestation notes — do not change verdict.v2 schema. If the requested validator model has no live adapter, disclose the unsatisfied diversity request and proceed same-model. Single fresh validator remains the default shape.

Mutating-check quarantine

Before running any acceptance-listed command, classify it as read-only or subject-mutating. Regen scripts, sync scripts, formatters, and anything with --force are subject-mutating until proven otherwise. Never run a subject-mutating check against an uncommitted subject: on 2026-07-15, scripts/test-ci-deterministic-gates.sh regenerated skills-codex/ from HEAD mid-validation and destroyed the uncommitted subject, forcing NOT_PROVEN (verdict b6e759dd...cb6a); only restoring the subject and revalidating in a fresh context produced the PASS (e9b6cdb8...37b9). If a mutating check is genuinely required by acceptance, run it against a disposable copy or a committed subject, never the judged working tree.

Workflow

  1. Recompute and compare subject-manifest.v1 using python3 skills/validate/scripts/validate.py manifest. The helper uses only filesystem content; Git commit/tree IDs are optional metadata. Derive the manifest at the start of validation and re-derive it at the end; any mismatch between the two is subject mutation and returns NOT_PROVEN.
  2. Confirm the intent-source digest has not changed since implementation. If the subject changed or complete changed-path coverage cannot be derived, return NOT_PROVEN.
  3. Adjudicate the actual diff, not a declared path list: compare runtime-derived actual changed paths against the intent's scope classes. A proven out-of-scope path returns FAIL; incomplete scope evidence returns NOT_PROVEN.
  4. Inspect the exact subject and factual evidence. Reported exit codes are claims, not evidence: re-execute the claimed proofs that bear on acceptance (see the freshness rules below for when a digest-bound receipt suffices). Judge every acceptance criterion and record criterion-level results, findings, evidence references, checked, and not_checked.
  5. Choose exactly one semantic result: PASS, FAIL, or NOT_PROVEN. Return it with criterion results, findings, evidence references, checked, not_checked, the acceptance and subject identities, distinct author and validator context IDs, and the freshness attestation. PASS requires distinct identities, explicit freshness, nonempty checked scope, top-level evidence, and evidence for every criterion.
  6. Only when the caller requests machine-readable evidence or a declared downstream consumer requires it, persist canonical verdict.v2 with store-verdict --draft <draft.json> --intent-source <resolved-intent> --subject-manifest <manifest.json> --author-context-id <id> --validator-context-id <id> --freshness-source <runtime|caller> --freshness-attester-id <id> --scope-result <PASS|FAIL|NOT_PROVEN>. The helper snapshots the exact resolved intent under <workspace>/.agents/ao/intents/sha256/<digest>.intent, then computes and injects intent and subject digests plus author, validator, and freshness facts. Identity and changed-path facts come from runtime-derived inputs and receipts, not model transcription. Storage defaults to <workspace>/.agents/ao/verdicts/sha256/<digest>.json; callers may provide verdict_dir.
  7. Return the semantic result and, when persisted, the artifact path and digest. Stop.

The digest is SHA-256 over canonical JSON with artifact_digest omitted. Writes use a same-directory temporary file, flush, fsync, and atomic rename. Identical existing content is idempotent success; conflicting content is an integrity failure represented by NOT_PROVEN.

Freshness without duplication

Fresh validation means independent judgment over the exact subject. It does not require mechanically replaying every author command. Verify intent identity, scope, evidence digests, and every acceptance criterion; independently rerun the risk-critical, uncertain, or insufficiently evidenced checks. A digest-bound deterministic receipt may prove routine facts. Replay an expensive full suite only when acceptance requires that result or the supplied receipt cannot establish it.

Boundary

Validate emits no WARN, confidence, disposition, briefing learning, owner, next action, repair, retry, replan, helper, escalation, tracker, Git, release, closure, or delivery state. Generic provenance may record a verdict later, but ledger availability cannot change its validity.

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 exact subject content against bead or caller acceptance, optionally persist verdict.v2 for a declared consumer, and stop. Triggers: "validate", "independently validate", "vibe".

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/packs/agentops-executor/agents/validator/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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇