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Verify Before Claim

Community
Mark393295827
verify-before-claim

Use when an agent is about to claim completion, correctness, safety, publication, deployment, or any consequential external fact.

Overview

PublisherMark393295827
Repositorythird-brain-v7-skills
Skill nameverify-before-claim
Stars
141
Forks
22
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 Mark393295827 on GitHub. Read the source before you install it.

Installation

Install the Verify Before Claim 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/Mark393295827/third-brain-v7-skills/tree/main/skills/verify-before-claim
  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/Mark393295827/third-brain-v7-skills.git /tmp/third-brain-v7-skills
mkdir -p .claude/skills
cp -r /tmp/third-brain-v7-skills/skills/verify-before-claim .claude/skills/verify-before-claim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Before Claim 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 Verify Before Claim 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 Verify Before Claim 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.

Verify Before Claim

<skill_contract> One falsifiable claim, its artifact or system, risk, expected result, permissions, and available checks. A scoped claim decision with fresh evidence, residual risk, approval, and rollback status. The cheapest direct check after the final material change supports the exact allowed wording. <non_goals>Producing the artifact, inferring success from effort, or widening a claim beyond checked evidence.</non_goals>

No evidence, no claim. Match the check to the exact claim, use fresh evidence, and keep execution authority separate from approval authority for consequential actions.

Usage Template

Provide: proposed claim, artifact or system, risk level, available checks, expected result, permissions, and rollback path.

Workflow

  1. Rewrite the proposed statement as one falsifiable claim.
  2. Classify risk: low, material, or consequential.
  3. Select the cheapest check that directly tests the claim.
  4. Record expected signal, time boundary, and acceptable evidence.

<unknowns_gate>

If the artifact, expected behavior, or verification method is missing, return NEEDS_INPUT. If only indirect evidence exists, return INSUFFICIENT_EVIDENCE or narrow the claim; never fill the gap with confidence language.

</unknowns_gate>

Run the selected check after the final material change. Examples: targeted test, lint, build, link check, read-after-write, diff inspection, source comparison, dashboard query, or deployment health check. Capture command/query, timestamp, scope, exit status, and key output.

Treat runtime termination as control evidence, not task evidence. A normalized complete signal still needs the acceptance check; a tool request needs a host execution receipt; truncation needs a checkpoint; refusal/error/unknown cannot support completion. A claimed NO_OP needs a fresh eligibility query, an output-count check, and proof that no prohibited side effect occurred.

For material or consequential claims, obtain independent verification from a separate check, reviewer, or evidence source. Require human approval before irreversible publication, deployment, spending, deletion, credential use, or policy change. Confirm the rollback path before execution.

For Graph claims, verify static contract integrity, every required node and join receipt, terminal acceptance, budgets, permission/compensation state, and checkpoint identity. Passing nodes do not prove the end-to-end graph.

Compare observed versus expected result. Decide supported, partially_supported, unsupported, or blocked. Check scope: passing one test cannot prove the full suite; a successful write cannot prove link integrity. State residual risk and evidence age.

<retry_policy>

max_attempts: 2. Retry only after diagnosing the failure and changing input, tool, scope, or strategy. Stop on repeated signature or NO_PROGRESS; never rerun an unchanged check to manufacture confidence.

</retry_policy>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus claim, risk, check specification, expected/observed results, approval receipt, and rollback readiness. Append verification events so evidence age remains visible.

</state_contract>

Failure Protocol

  • NEEDS_INPUT: the claim or expected result is ambiguous; ask one discriminating probe.
  • INSUFFICIENT_EVIDENCE: no direct check supports the requested scope; narrow or withhold the claim.
  • BLOCKED_PERMISSION: approval or access is absent; do not perform the action.
  • VERIFY_FAILED: observed evidence contradicts the claim; report failure and recovery.
  • NO_PROGRESS: the same verification signature fails twice; stop and escalate.
  • BUDGET_STOP: verification budget is exhausted; preserve evidence and do not claim completion. max_attempts: 2.

Output Contract

Return status, result (claim decision and allowed wording), evidence (fresh receipts), unknowns (including residual risk), and next_action (repair, approval, rollback, or stop).

Edge Cases

  • Tests passed before a final edit: evidence is stale; rerun the relevant checks after the edit.
  • A scheduled agent reports success but its normalized termination was truncation: withhold completion and verify from the checkpoint.
  • A maintenance agent emits nothing: call it a verified NO_OP only when the eligibility and side-effect checks both pass.
  • Every Graph node is green but the reduce join lacks one declared input: withhold graph completion and return VERIFY_FAILED.
  • An authoritative page is unavailable: report INSUFFICIENT_EVIDENCE; do not substitute an uncited recollection for the external fact.

Success Metrics

  • Every material completion claim has a fresh, scope-matched receipt.
  • Consequential actions have independent verification, approval, and rollback readiness.
  • Failed checks change the claim or execution state immediately.

Quality Gates

  • Claim, expected result, and check scope match.
  • Evidence was produced after the last material change.
  • Termination and no-op claims have direct host/runtime receipts.
  • Partial checks lead to partial wording.
  • Approval and rollback controls match risk.

</skill_contract>

Frequently asked questions

What does the Verify Before Claim AI skill do?

Use when an agent is about to claim completion, correctness, safety, publication, deployment, or any consequential external fact.

Why use Verify Before Claim on TypingMind?

Because you install it once and use it with any model. Verify Before Claim 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 Verify Before Claim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mark393295827/third-brain-v7-skills/tree/main/skills/verify-before-claim. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify Before Claim?

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 Verify Before Claim?

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

Is the Verify Before Claim AI skill free?

Yes. It is published on GitHub by Mark393295827 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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