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Verify

Community
xiaolai
verify

Ask Claude Code to verify that a list of reported issues has been fixed. Continues the same Claude session from a prior audit if session_id is available, so Claude has full context of what it flagged. Returns FIXED / NOT FIXED / PARTIAL / REGRESSED per issue.

Overview

Publisherxiaolai
Repositorycc-suite
Skill nameverify
Stars
56
Forks
21
Bundled files
1
LicenseISC
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Verify 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/xiaolai/cc-suite/tree/main/skills/cc-suite/verify
  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/xiaolai/cc-suite.git /tmp/cc-suite
mkdir -p .claude/skills
cp -r /tmp/cc-suite/skills/cc-suite/verify .claude/skills/verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify 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 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 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

Ask Claude Code to check whether previously reported audit findings have been resolved. Claude re-reads the files at the reported locations and renders a verdict per issue. Codex provides the issue list; Claude provides the verdict.

When to Use

  • After Codex has applied fixes from a $audit run
  • To confirm a manual fix was applied at the correct location
  • As the final step of a $audit-fix cycle

Arguments

ArgumentEffect
session_idClaude session from the prior $audit — reuses full audit context
issue listPaste or reference the findings to verify (file:line + description)

Call Pattern

Option A: Continue the audit session (preferred)

If {audit_session_id} is available from a prior $audit or $audit-fix run:

mcp__claude-code__claude_code_reply:
  session_id: {audit_session_id}
  prompt: |
    The following issues from your audit have been addressed. Verify each one.

    ISSUES TO VERIFY:
    {paste findings in "file:line | severity | issue description" format}

    For each issue report one of:
    - FIXED — issue resolved fully, no new problems introduced
    - NOT FIXED — issue still present at the reported location (explain why)
    - PARTIAL — partially addressed; describe what remains
    - REGRESSED — fix introduced a new problem (describe it)

    Read the files at the reported locations. Do not assume a fix is correct
    without reading the current code.

Option B: Fresh verification session

When no prior session is available:

mcp__claude-code__claude_code:
  prompt: |
    Verify whether the following issues have been fixed.

    ISSUES TO VERIFY:
    {paste findings in "file:line | severity | issue description" format}

    For each issue:
    1. Read the file at the reported location
    2. Check if the issue is resolved
    3. Report: FIXED / NOT FIXED / PARTIAL / REGRESSED

    For NOT FIXED and PARTIAL: explain what is still wrong.
    For REGRESSED: describe the new problem introduced.

    Apply independent judgment — do not accept surface-level changes that
    mask the underlying issue.
  cwd: {project working directory}
  effort: high
  permissionMode: plan

Save the returned session_id as {verify_session_id}.

Output Format

File:LineSeverityIssueVerdictNotes
...High...FIXED—
...High...NOT FIXEDcondition still inverted
...Medium...PARTIALnull check added but empty-string case missing

Result: N fixed, N not fixed, N partial, N regressed

Failure Handling

A Claude-backed call that does not return a usable result is terminal for this skill. Stop and report; do not improvise a way to keep going.

This covers every non-success outcome: a runtime or transport error, a timeout, a rate limit, an empty or truncated result, and a result that does not match the shape this skill asked for. Absence of an error is not success — a call that returns nothing has failed, however it exited.

Never report a failed call as a result. It is not CLEAN, not "no findings", not a completed verification, not an empty plan. A verification that did not run is UNVERIFIED, and saying so is the entire value of the step.

Do not recover on your own initiative. Unless the user asks for it: do not retry the call, do not open a replacement session, do not switch to another Claude execution path (a different MCP tool, a different skill, a direct invocation), and do not fall back to doing the work yourself in place of the delegate. A failed claude_code_reply in particular must not silently become a new claude_code session — that discards the conversation the reply was meant to continue, and the original work may still be running.

Preserve the session. Report any session_id the call returned, even on failure, so the user can resume that exact session rather than start over.

Say what may still be running. A timeout or transport error does not establish that the work on the other side stopped. Say so explicitly, so the user does not start a second attempt that overlaps the first.

Then stop according to what the call could have changed:

Call classOn failure
Read-only (claude-plan, claude-review, audit, verify, advisor *_consult)Report the operation as blocked, with the error and any session_id. Nothing was changed.
Write-capable (claude-debug, claude-implement)Stop before any further delegation and state explicitly whether partial workspace changes may exist — the delegate may have written files before failing. Recommend the user inspect git status / git diff.
Cycle (audit-fix)Stop the current cycle. Retain the existing findings and their fix status; do not discard them and do not mark unattempted findings as resolved. Report which finding the cycle stopped on.

Notes

  • Always prefer Option A when a session is available — Claude retains full audit context and can make more precise verdicts
  • If all issues are FIXED, report success and suggest committing
  • If any are NOT FIXED or REGRESSED, feed those back into $audit-fix or fix manually

Example Invocations

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 Verify AI skill do?

Ask Claude Code to verify that a list of reported issues has been fixed. Continues the same Claude session from a prior audit if session_id is available, so Claude has full context of what it flagged. Returns FIXED / NOT FIXED / PARTIAL / REGRESSED per issue.

Why use Verify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xiaolai/cc-suite/tree/main/skills/cc-suite/verify. 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 Verify?

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?

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

Is the Verify AI skill free?

Yes. It is published on GitHub by xiaolai under the ISC 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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