Anchor Vet logo

Anchor Vet

Community
lynxlangya
anchor-vet

Evidence-gated diff review for PRs, branches, commit ranges, staged code changes, and merge readiness checks. Use when an AI reviewer must review a concrete git-anchored diff, judge whether code is safe to merge, inspect a PR URL or branch, cross-examine PR or commit claims, account blast radius, or render approve/request-changes/blocked verdicts. Do not use for design-only review, whole-codebase audits, bug fixing, or code authoring without a git diff under judgment.

Overview

Publisherlynxlangya
Repositorytechne
Skill nameanchor-vet
Stars
106
Forks
18
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Anchor Vet 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/lynxlangya/techne/tree/main/skills/anchor-vet
  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/lynxlangya/techne.git /tmp/techne
mkdir -p .claude/skills
cp -r /tmp/techne/skills/anchor-vet .claude/skills/anchor-vet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchor Vet 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 Anchor Vet 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 Anchor Vet 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.

anchor-vet

Force the skipped move in code review: prove the reviewed diff's scope, blast radius, claims, findings, and verdict are anchored to git evidence.

Trigger Check

Use this skill only when there is a concrete code change to judge: a PR, branch, commit range, or staged review target.

Boundary test: can you name a base...head pair of git states whose difference is the artifact under judgment? If yes, use anchor-vet. If no, get the branch or ref first; do not review pasted diffs or prose descriptions.

Do not use anchor-vet for design feedback without a diff, whole-codebase audits, feature implementation, bug fixing, formatting-only tasks, or document review. If the user asks you to fix findings too, render the review verdict first; fixes are a separate task, and behavioral fixes route to anchor-repro.

Forced Procedure

  1. Anchor the scope. Check out the reviewed head locally. Capture external claims before init: for PRs, save the title/body from gh pr view --json title,body --jq '.title + "\n\n" + (.body // "")' to a claims file. Run: python3 skills/anchor-vet/scripts/vet_gate.py init --project <root> --review <slug> --base <ref> --head <ref> --claims-file <path> or, only when there are genuinely no external claims, --no-claims.
  2. Read every hunk. Inspect the full diff and every hunk in scope.json. If the diff is too large to read honestly, stop and propose a split.
  3. Walk the blast radius. For each candidate symbol, read the references found by the gate. Record examined refs with effect and a one-line note. Examined means you opened/read the reference and judged how the change affects it.
  4. Account weak or symbolless hunks. For weak/symbolless hunks, use a verified enclosingUnit when a named unit exists, or file-level with a reason only for genuinely unit-less code/config/prose.
  5. Cross-examine claims. Disposition every anchored claim id from scope.json: verified, contradicted, not-verifiable-from-diff, or non-claim. Verified/contradicted claims need citations.
  6. Hunt findings with severity honesty. Use only blocking, concern, and nit. Cite findings. A blocking finding needs R2 cited evidence or an R3 repro probe. When a behavioral assertion is cheap to demonstrate, record a failing anchor-repro ledger entry against the reviewed head and cite it with entrySha256.
  7. Write and check review.json. Run: python3 skills/anchor-vet/scripts/vet_gate.py check --project <root> --review <slug>. Fix check failures by doing the missing review work, not by padding JSON.
  8. Render the verdict through the gate. Run: python3 skills/anchor-vet/scripts/vet_gate.py close --project <root> --review <slug> --verdict approve|request-changes|blocked. Report the verdict, cite verdict.json, and name each finding's evidence rung.

Script Contract

Artifacts are written under the target project:

text
.techne/review/<slug>/
  scope.json    # computed by init
  review.json   # authored by the reviewer
  report.json   # computed by check
  verdict.json  # computed by close

Generated .techne/ output belongs to target projects. Do not commit it to this repository.

Review skeleton:

json
{
  "schema": "techne.vet/1",
  "symbols": [
    {
      "id": "s-...",
      "symbol": "changedName",
      "disposition": "reviewed",
      "refs": [
        {
          "file": "src/caller.py",
          "line": 42,
          "effect": "unaffected",
          "note": "caller passes the same contract"
        }
      ]
    }
  ],
  "hunks": [],
  "claims": [
    {
      "ids": ["c-..."],
      "disposition": "verified",
      "citations": [{"file": "src/change.py", "line": 10}]
    }
  ],
  "findings": [],
  "testsAcknowledgment": "No tests changed; the diff is docs-only or existing coverage is enough because ..."
}

Use reference.md for the full JSON schema, rubrics, exclusions, weak spots, and probe guidance.

Stop Conditions

  • Stop if no git-anchored base...head diff is available.
  • Stop if init refuses because of dirty worktree, missing merge base, or head mismatch; fix the setup or record blocked.
  • Stop if the review is too large to read in full; propose a split instead of sampling silently.
  • Stop before approve if refs, hunks, claims, or test acknowledgment remain unaccounted.
  • If head moves mid-review, do not fight the tool. Close the old slug as blocked --reason ... or leave it, then start a new slug for the new head.

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 Anchor Vet AI skill do?

Evidence-gated diff review for PRs, branches, commit ranges, staged code changes, and merge readiness checks. Use when an AI reviewer must review a concrete git-anchored diff, judge whether code is safe to merge, inspect a PR URL or branch, cross-examine PR or commit claims, account blast radius, or render approve/request-changes/blocked verdicts. Do not use for design-only review, whole-codebase audits, bug fixing, or code authoring without a git diff under judgment.

Why use Anchor Vet on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lynxlangya/techne/tree/main/skills/anchor-vet. 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 Anchor Vet?

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 Anchor Vet?

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

Is the Anchor Vet AI skill free?

Yes. It is published on GitHub by lynxlangya 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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