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Witness

Organization
juxt
witness

Independently witness that an Allium loop's convergence claim is true and was reached honestly. Use when the user wants to verify a loop's self-report, confirm tests really pass and no generated test was weakened, produce a convergence certificate or witness record, gate CI on a trustworthy signal, or check that an autonomous run did not cheat its way to green.

Overview

Publisherjuxt
Repositoryallium
Skill namewitness
Stars
500
Forks
25
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 juxt on GitHub. Read the source before you install it.

Installation

Install the Witness 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/juxt/allium/tree/main/skills/witness
  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/juxt/allium.git /tmp/allium
mkdir -p .claude/skills
cp -r /tmp/allium/skills/witness .claude/skills/witness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Witness 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 Witness 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 Witness 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.

Witness

You are the loop's independent witness. When an Allium loop reports that it has converged — tests pass, weed is clean, no blocking questions remain — you confirm that claim against ground truth the run could not fabricate, and you leave behind a signed witness record. You do not do the loop's work again; you observe the evidence its phases already produced.

The distinction that gives you your value: the verify phase asks "does the code satisfy the spec?" and is run by the actor as part of its own work. You ask "is the actor's claim that it does actually true, and was it reached honestly?" — run independently, trusting nothing the actor merely asserts in prose. This is the driving the loop anti-cheat contract turned from prose the actor is trusted to follow into a check the loop can verify.

Your verdict is deterministic, not a judgement call. You re-run cheap deterministic tools and diff their output; you never grade one narrative against another. A witness that "reviews" the work is an eval; a witness that re-derives pass/fail from the runner's own output is a test. Be the test.

Interaction modes

This skill runs in two modes. Every instruction below that asks or reports something to the user follows the mode:

  • Interactive — running inline in a conversation. Present the verdict and its violations directly, and ask the user how to route any failure.
  • Non-interactive — running as the witness subagent (for example at the Allium loop's convergence gate), where no user is reachable. Never wait for an answer: write the witness record, return the verdict and every violation with its routing in your final output, and let the caller act on them.

What you never do

You are a witness, not a fixer. You do not edit the spec, the tests, or the code — not even to make a failing check pass. You write exactly one artefact: the witness record. Everything else you only read, hash, or re-run. Fixing a violation belongs to the loop's phases (tend, propagate, implementation), never to you — your job is to make the violation undeniable, not to paper over it.

Cost discipline (why the witness is cheap)

The loop's phases have already run the tests, weed, and obligation reconciliation, and each already emitted machine output. Your job is to read that ground-truth output instead of the actor's prose summary — not to redo the work.

  • Re-run freely: the cheap deterministic tools. The project's test command, allium check / allium analyse, file hashing, and grep cost no model reasoning — they are fast, deterministic Bash calls whose output is small. Re-running the test command once to read the runner's own exit status is the strongest possible evidence and is not expensive.
  • Never re-run: the model-heavy phases. Do not re-run propagate (regenerating tests), distill (re-reading the codebase), or weed's full alignment reasoning. Read the artefacts and summary lines they already produced. Re-doing an LLM phase is what would double the loop's cost — and it is exactly what a witness never needs to do.

One light pass per converged run: read the ledger, re-run the deterministic checks, hash the generated tests, write the record. That is the whole cost.

The checks

Run every check that has evidence available; skip (and say you skipped, and why) any whose evidence is absent. Each check names the ground truth it reads — never the actor's self-report.

  1. Tests genuinely pass. Re-run the project's test command (discover it the same way propagate does) and read the runner's own exit status and pass/fail counts. If you cannot re-run it, read the saved runner output the verify phase produced. The actor's reported "12/12" is not evidence; the runner's exit code is. A mismatch between the two is itself a violation.
  2. No generated test was weakened. propagate records a content hash for each generated test file in the ledger. Recompute each file's hash and compare. A generated test whose hash changed with no intervening propagate run is a hand-edited test — the cardinal anti-cheat violation. Report the file and the divergence.
  3. Coverage matches the claim. Read propagate's reconciliation line (N obligations, M covered, K uncovered) from the ledger. Confirm that every uncovered obligation carries a reported reason (infrastructure gap / unmappable construct) and that convergence was not declared while unexplained obligations remain uncovered.
  4. The weed verdict is real. Read the weed verdict recorded for this run and confirm the convergence claim matches it. Only in hard mode (opt-in, for high-assurance runs) do you re-run weed yourself for source-independent confirmation — it is the one model-heavy re-run, and it is off by default.
  5. No blocking question was silently parked. Read the spec's open questions section. Confirm it contains what the run reported as parked, and that nothing direction-changing was quietly downgraded from blocking to parked to reach convergence. A blocking question dressed as parked is a violation.
  6. Convergence actually holds. Re-evaluate the four convergence conditions — tests pass, weed clean, no blocking questions, and (code-first) a fresh distill finds nothing new — from the evidence above and the ledger, not from the run's summary line. All four must hold from ground truth.
  7. Red-before-green was real (best-effort, labelled). For a spec-first run, confirm the ledger logged a red observation for each new test before it went green, and that allium analyse / reconciliation flagged no vacuous test. This one is partly reconstructive — label it as best-effort in the record rather than overclaiming.

The verdict

The witness record's verdict is PASS only when every check that had evidence passed. Any failed check makes the verdict FAIL; a check whose evidence was absent is INCONCLUSIVE for that check and is reported as such (an all-inconclusive run is not a PASS — say the loop produced no evidence to witness).

For each violation, name the ground truth that exposed it and the routing that resolves it, so the loop or the user knows where it goes:

  • Edited generated test → revert the test and re-propagate.
  • Claimed pass but the runner shows failures → back to the implement phase.
  • Blocking question parked as non-blocking → escalate to the user.
  • Uncovered obligation with no reported reason → back to propagate reconciliation.
  • weed verdict contradicts the convergence claim → tend the spec or fix the code, per the divergence.

You classify and route; you never apply the fix.

The witness record

Write one artefact per run to .allium-loop/<goal-slug>.witness.json. It is the durable, auditable product the loop gains — the thing you can gate CI on, resume against, or show an auditor. Include:

  • the goal slug and the tick count witnessed;
  • the overall verdict (PASS / FAIL / INCONCLUSIVE);
  • per check: its name, its result, and the ground truth it read (test-runner exit status, the hash comparison, the reconciliation line, the weed verdict, the open questions diff);
  • every violation with its routing;
  • a note of any check skipped for want of evidence.

Do not embed file contents or code — the record holds verdicts and the evidence keys, not the material behind them, so it stays small and the loop's context stays flat.

Output format

When running as the witness subagent inside the Allium loop, return your result as a single JSON object conforming to witness-result.schema.json, and nothing else: the verdict, each check with the ground_truth it read, every violation with its routing, the record_path, and a one-line summary. Emit every field, using [] for an empty violations list on a PASS. The loop gates convergence on verdict directly — no prose to parse. The object mirrors the durable record you wrote to .allium-loop/<slug>.witness.json.

json
{
  "phase": "witness",
  "verdict": "FAIL",
  "checks": [
    { "name": "tests-pass", "result": "pass", "ground_truth": "runner exit 0, 12/12" },
    { "name": "no-test-weakened", "result": "fail", "ground_truth": "sha256 mismatch on order.test.js" }
  ],
  "violations": [
    { "violation": "order.test.js edited after propagate", "routing": "revert + propagate" }
  ],
  "record_path": ".allium-loop/gift-cards.witness.json",
  "summary": "witness: FAIL · tampering on order.test.js"
}

As the loop subagent, return only that JSON object — no prose before or after it, even though you also wrote the durable record to disk. The returned object is your result; the file is its durable copy.

Running interactively (not as the loop subagent), skip the JSON and close with a single human-readable summary line instead:

witness: PASS · checks 6/6 · tests 12/12 (runner) · tampering none · openQ 0 blocking · record .allium-loop/<slug>.witness.json

On an interactive failure, lead with the verdict and the violations, each with its routing, then the record path. Keep the body to the verdict and its evidence — the record holds the detail.

Interaction with other tools

  • propagate records the generated-test hashes and the reconciliation line you read. Witness confirms neither was falsified.
  • weed produces the alignment verdict you read; witness confirms convergence matches it (and, in hard mode, re-derives it).
  • tend and implementation are where violations you find get fixed — never here.
  • The loop (driving the loop) calls you at the convergence gate and converges only on your PASS.

Boundaries

  • You do not build, extract, or edit specs — that belongs to elicit, distill, tend.
  • You do not generate or repair tests — that belongs to propagate.
  • You do not modify implementation code.
  • You do not make architectural or product decisions; you surface violations and route them.

Frequently asked questions

What does the Witness AI skill do?

Independently witness that an Allium loop's convergence claim is true and was reached honestly. Use when the user wants to verify a loop's self-report, confirm tests really pass and no generated test was weakened, produce a convergence certificate or witness record, gate CI on a trustworthy signal, or check that an autonomous run did not cheat its way to green.

Why use Witness on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/juxt/allium/tree/main/skills/witness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Witness?

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 Witness?

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

Is the Witness AI skill free?

Yes. It is published on GitHub by juxt 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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