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Double Check

Community
mrtooher
double-check

Automatic final verification pass on completed deliverables. Runs at the end of every fable-mode run — those skills invoke it before delivery. Also triggers when the user says "double check this". Uses a panel of fresh checker agents when spawning is authorized (otherwise a cold self-check) to verify the finished output — not to redo the task. Any FAIL gets fixed and re-checked before delivery.

Overview

Publishermrtooher
Repositoryfable-mode
Skill namedouble-check
Stars
873
Forks
95
Bundled files
Instructions only
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 mrtooher on GitHub. Read the source before you install it.

Installation

Install the Double Check 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/mrtooher/fable-mode/tree/main/double-check
  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/mrtooher/fable-mode.git /tmp/fable-mode
mkdir -p .claude/skills
cp -r /tmp/fable-mode/double-check .claude/skills/double-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Double Check 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 Double Check 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 Double Check 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.

Double Check

Family version: v6 (2026-10-10).

Automatic verification pass on a finished deliverable. The user should never have to re-check work themselves — this skill is that re-check.

This skill is both the gate (when verification runs, on what, and what happens to a FAIL) and the mechanism (how to brief a cold verifier so it checks the world instead of your argument). Both live here — there is no companion skill to load.

What it is and is not

  • It verifies outputs, it does not re-run the task. A full second run produces a second divergent version and a comparison problem; attacking the finished artifact catches the same errors at a fraction of the cost.
  • It runs after the work is complete and before it is delivered.
  • It never runs on its own output. One double-check per deliverable. Fixes triggered by a FAIL get their specific failed check re-run — not a whole new panel. No recursion.

When it runs

  1. Automatically at the end of any fable-mode run (fable-mode, fable-opus, fable-sonnet). Those skills call this one as their final delivery gate. Exception: if a fable run already executed a cold verification of the final deliverable, do not duplicate it — run only the seam check (section "Check the synthesis seam") and skip the rest. Do not infer this from the narrative: the run closes its report with a FINAL-COLD-CHECK: line. yes → seam check only. intermediates-only, no, or no line at all → run the full checks (panel or self-check mode, per below). Absence of the line is never evidence that the check happened.
  2. On request: "double check this", "verify this", "make sure this is right".

Panel or self-check. Some hosts allow subagents only when the user asks for them (observed: the cloud runtime's Agent tool, 2026-10-10). Run the checker panel only when this skill was invoked by slash command or by name ("double check this"), or when the fable run that called it was spawn-authorized (see fable-mode's Invocation gate). Otherwise — "verify this", "make sure this is right", or a fable run without spawn authorization — use self-check mode (step 2) and say so. The usage limit overrides both: when the user reports usage at their threshold, check in first; unless they say otherwise, use self-check mode.

Skip conditions

Trivial, reversible, low-stakes output where being wrong costs nothing and is immediately visible. Say in one line that double-check was skipped and why. Never skip when the deliverable tells the user to delete data, change a system or security setting, spend money, or act on a warning.

The core rule: pass the claims, never the reasoning

An agent reviewing its own work almost always agrees with itself. It re-reads its reasoning, finds it reasonable, and signs off — the same blind spot that produced the error also produces the approval. A cold agent has no stake in the conclusion and no memory of why it seemed right. It has to go get the evidence again. That is the entire mechanism; everything below protects it.

The moment a verifier reads why you concluded something, it starts evaluating your argument instead of the world. It inherits your framing and your blind spot. You get agreement, not verification.

Give the verifier: the claim, the spec or original requirements, and where to look. Withhold: your reasoning, your confidence, your narrative, and which claims you think are shaky. Do not hint at what you expect it to find — "I think claim 4 might be weak" tells it where to spend effort and implies the others are fine.

Procedure

1. Extract claims and requirements

From the finished deliverable, build two numbered lists:

  • Claims (C1, C2, ...): every falsifiable assertion in the deliverable. Facts, numbers, file paths, computations, statements that something works. Each must be stated so a specific observation could refute it. Good: C3: /Applications is 63 GB. Bad: C3: Storage looks basically fine, mostly apps. If a claim can't be stated so an observation would refute it, it isn't a claim — it is a summary. Either sharpen it or drop it from "established" status in the deliverable.
  • Requirements (R1, R2, ...): everything the user's original request demanded. Each phrased as a yes/no: "deliverable contains X", "file Y exists and opens", "all rows from source present".

For each item, name the exact check: the command, file, source, or comparison that settles it. An item with no nameable check is marked UNVERIFIABLE by you, up front, and labeled as such in the deliverable.

2. Detect the runtime, pick the mode

Panel mode (Agent tool available AND spawning authorized): run the checker panel.

Spawn fresh checker agents using the brief template below. They receive claims, requirements, checks, constraints (READ-ONLY — checkers never modify anything), and the output format. They never receive your reasoning, your confidence, or hints about which items you suspect.

Tier routing — match the tier to the check, not to the importance of the deliverable:

  • One Haiku checker (all mechanical checks in one bundle): numbers re-added, files opened and read back, links/paths exist, commands re-run, counts reconciled. Add a second Haiku only when the mechanical checks will not fit one brief, and say why.
  • One Sonnet checker: requirements coverage (every R satisfied by the artifact itself, not by the work log), internal consistency, logic of any argument the deliverable makes.
  • Opus (only if needed): when two checkers disagree on the same item, or a REFUTED verdict is itself disputed after the fix. Adjudicates; does not re-check everything.

Collect every verdict. Spawns may run in the background even when foreground is requested (observed in the cloud runtime, 2026-10-10). Do not fix, synthesize, or deliver until every checker has returned; never act on a partial panel. If a checker errors or fails to return, run its checks in self-check mode and label them so.

Self-check mode (no Agent tool, or spawning not authorized): degraded single-pass mode.

Say plainly that the checker panel did not run and why (no Agent tool on this surface, or spawning not authorized). Then run the checks yourself, cold: work only from the claims/requirements lists and the artifact — deliberately not from memory of how you produced it. Re-derive each number, re-read each file or source, re-test each requirement against the artifact. Same ternary verdicts, same output format. Weaker than fresh agents — label it as a self-check in the summary.

Checker brief template

You are a COLD VERIFIER. Claims below were produced by other agents.
You did not see their reasoning. Assume each claim may be wrong.

[hard constraints — e.g. READ-ONLY, no sudo, no modifications]

## CLAIMS
C1: <falsifiable assertion> → check via: <command / source>
C2: ...

## OUTPUT
C1: CONFIRMED|REFUTED|UNVERIFIABLE — <exact command> → <exact output line>
C2: ...

## CORRECTIONS
<every claim that was wrong, and what is actually true>

Refute freely. Finding a wrong claim is a success, not a failure.
Do not report a claim as confirmed unless you personally observed
the output that proves it. If you could not check something, say
UNVERIFIABLE — do not guess and do not pad.

The "refute freely" line is not optional. Without it, agents optimize for agreement: they read a claim, find it plausible, and confirm.

3. Verdicts — ternary, forced

Every claim and requirement returns exactly one of:

  • PASS / CONFIRMED — with the exact command or observation and the exact output line proving it. "Looks good" is banned; it is not a verdict.
  • FAIL / REFUTED — with what is actually true instead.
  • UNVERIFIABLE — with why the check could not run. First-class outcome, not a failure. It is how blind spots stay visible instead of quietly becoming assumed-true. A panel that never returns UNVERIFIABLE is guessing to look thorough.

4. Check the synthesis seam

The error that survives everything above: work checked at intermediate stages, then the final write-up introduces new claims nobody checked. Multi-agent work has a seam — the workers produce findings, a verifier checks those findings, and then the synthesizer writes the deliverable, and the synthesizer's own additions and escalations were never checked by anyone.

Always extract claims from the document about to be handed over, not from intermediate outputs. Two seam-specific hunts:

  • Escalated hedges: anywhere a caveat in the underlying work became a firm conclusion in the deliverable. If the work said "signal" and the deliverable says "exposure", check it. Real example: a worker correctly reported an app bundle's mtime as an "un-updated signal, not a true unused signal"; the synthesizer turned that into "18 months stale, security exposure, update immediately." The app was four weeks behind. Nothing had checked the final report.
  • Proxy metrics read as the real thing: a timestamp is not a version; a file size is not disk usage; a process existing is not a process running; a search returning nothing is not evidence of absence. Verify the actual quantity.

5. Fix, then re-check

Any FAIL: fix the deliverable, then re-run that specific check. If the fix invalidates upstream content, fix and re-check that too.

One re-check round. In panel mode, batch every fixed item into ONE fresh re-checker brief (Haiku, unless an item needs Sonnet's logic check). If anything fails that round, fix it and verify it by cold self-check — re-derive from the artifact, no new spawn — and mark those items "self-checked" in the summary. No further spawn rounds. An Opus adjudicator for a disputed verdict (step 2) sits outside this cap — at most one per disputed item. In self-check mode, every re-check is a cold re-derivation.

Deliver only when every item is PASS or explicitly UNVERIFIABLE. Never deliver with an unresolved FAIL footnoted.

6. Report

Deliver with a short verification summary:

Double-check: N claims + M requirements.
PASS: n. FIXED after FAIL: n (list what was wrong, one line each). UNVERIFIABLE: n (named).
Checkers: [e.g. 1× Haiku, 1× Sonnet, 1 re-check round | self-check — no Agent tool / spawning not authorized]

Name what got refuted and fixed — that is the evidence the check was real. A summary that confirms everything is either a clean run or a rubber stamp, and the user can't tell which unless specifics are named. If everything passed cleanly, say that plainly; do not manufacture a finding. When the mistake was yours rather than a worker's, say so plainly too — own the specific error and move on, no extended apology.

Operational rules

  • Verify-before-flag applies: a checker's FAIL must cite the observed evidence, not the absence of confirmation. Absence of evidence is not a finding.
  • Checkers are READ-ONLY. Only the main session fixes things.
  • Budget: default panel is 1 Haiku + 1 Sonnet, plus at most one re-check round (cut from 2–4 Haiku + 1 Sonnet on 2026-10-10, after a 2+1 panel cost ~580K subagent tokens; coverage at 1+1 not yet measured). Escalate to Opus only on disagreement. Match tier to the check, not to the importance of the deliverable — mechanical checks stay cheap.

Frequently asked questions

What does the Double Check AI skill do?

Automatic final verification pass on completed deliverables. Runs at the end of every fable-mode run — those skills invoke it before delivery. Also triggers when the user says "double check this". Uses a panel of fresh checker agents when spawning is authorized (otherwise a cold self-check) to verify the finished output — not to redo the task. Any FAIL gets fixed and re-checked before delivery.

Why use Double Check on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mrtooher/fable-mode/tree/main/double-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Double Check?

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 Double Check?

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

Is the Double Check AI skill free?

It is published on GitHub by mrtooher. Check the repository for licensing terms. 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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