Agent skill

Dingo Verify

by MigoXLab in MigoXLab/dingo

A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.

Apache-2.0Auto-check: notesData & Analytics

Install Dingo Verify

skills CLI
$ npx skills add MigoXLab/dingo --skill dingo-verify -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install MigoXLab/dingo dingo-verify --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/dingo-verify .claude/skills/dingo-verify && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dingo-verify
GitHub stars
757
Token cost
~741 tokens
SKILL.md length
271 words
Files
3 (incl. scripts, references)
Skills in repo
2
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.

  • Works in 3 steps: Dingo is installed: python -c "from… → OPENAI_API_KEY is set (required) → TAVILY_API_KEY is set (optional, enables…
  • The user wants to fact-check an article
  • SKILL.md covers Prerequisites, Usage, Presenting Results and Performance Notes, plus 1 more section
  • Runs Python scripts from its folder; calls pip and python; needs OPENAI_API_KEY and TAVILY_API_KEY

What it does

Dingo Verify is an agent skill from MigoXLab/dingo. Use when the user wants to fact-check an article or verify factual claims in a document. Triggers on: "fact-check", "verify article", "check facts", "文章事实核查", "验证文章". Runs Dingo's ArticleFactChecker via SDK to extract and verify all factual claims with web search evidence.

Its SKILL.md is about 740 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/advanced-config.md` and `scripts/fact_check.py`).

It sits in Data & Analytics, covering Fact-checking and source verification, Web search and Data cleaning. It works with OpenAI, DeepSeek and Qwen. The repository describes itself as: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. The licence is Apache-2.0.

When your agent uses it

  • The user wants to fact-check an article
  • Verify factual claims in a document

Example prompts

  • “fact-check”
  • “verify article”
  • “check facts”
  • “/dingo-verify”

Requirements

  • Python 3
  • A credential in OPENAI_API_KEY
  • A credential in TAVILY_API_KEY
  • Pre-approved tools (allowed-tools): Bash, Read, Glob

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Dingo is installed: python -c "from dingo.config import InputArgs; print('OK')"
  2. OPENAI_API_KEY is set (required)
  3. TAVILY_API_KEY is set (optional, enables web search verification)

What it can do on your machine

Read from SKILL.md and the folder at commit 816aaae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY
    • TAVILY_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dingo Verify loads about 741 tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 271 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~741
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Glob

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from MigoXLab/dingo at commit 816aaae, republished under its Apache-2.0 licence (© MigoXLab). 271 words, ~741 tokens.

Download SKILL.mdSave it as .claude/skills/dingo-verify/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dingo-verify
description
Use when the user wants to fact-check an article or verify factual claims in a document. Triggers on: "fact-check", "verify article", "check facts", "文章事实核查", "验证文章". Runs Dingo's ArticleFactChecker via SDK to extract and verify all factual claims with web search evidence.
allowed-tools
Bash, Read, Glob
argument-hint
article-path

Dingo Article Fact-Checker

Verify factual claims in articles using Dingo's ArticleFactChecker agent.

Prerequisites

Before running, verify:

  1. Dingo is installed: python -c "from dingo.config import InputArgs; print('OK')"
  2. OPENAI_API_KEY is set (required)
  3. TAVILY_API_KEY is set (optional, enables web search verification)

If prerequisites fail, help the user fix them:

  • Missing dingo: pip install -e . (from project root) or pip install dingo-python
  • Missing LangChain: pip install -r requirements/agent.txt
  • Missing API key: export OPENAI_API_KEY='your-key'

Usage

Run the fact-check script with the article path:

bash
python ${CLAUDE_SKILL_DIR}/scripts/fact_check.py $ARGUMENTS
Optional arguments
  • --model MODEL: Override LLM model (default: env OPENAI_MODEL or gpt-5.4-mini)
  • --max-claims N: Max claims to extract (default: 50, reduce for faster runs)
  • --max-concurrent N: Parallel verification slots (default: 5)
Supported file formats
  • .md, .txt: Markdown/plaintext articles (auto-wrapped for Dingo)
  • .jsonl: JSONL with {"content": "..."} per line
  • .json: JSON array format

Presenting Results

The script outputs JSON to stdout. Parse it and present to the user:

Success output

Present a formatted report with these sections:

  1. Summary: total claims, accuracy score, false/unverifiable counts
  2. False Claims Table: if any false claims found, show claim vs truth vs evidence
  3. All Claims Overview: list all claims with their verdicts (TRUE/FALSE/UNVERIFIABLE)
  4. Output Path: where the full Dingo report is saved

Example presentation:

## Article Fact-Check Report

**Accuracy**: 73.3% (11/15 claims verified true)
- Verified True: 11
- Verified False: 2
- Unverifiable: 2

### False Claims Found

| # | Article Claimed | Actual Truth | Evidence |
|---|----------------|-------------|----------|
| 1 | "released in Nov 2024" | Released Dec 5, 2024 | Official announcement |

### Full Report
Saved to: outputs/20260318_143022_abc123/
Error output

If the script exits with code 1, it prints error JSON to stderr. Read the error and hint fields and help the user resolve the issue.

Performance Notes

  • Single article: typically 2-5 minutes depending on claim count and model speed
  • Progress is printed to stderr during execution
  • For faster runs: use --max-claims 10 --model gpt-5.4-mini

Advanced Configuration

For model selection, claim types, and tuning options, see: references/advanced-config.md

© MigoXLab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in .claude/skills/dingo-verify of MigoXLab/dingo.

  • SKILL.md
  • references/advanced-config.md
  • scripts/fact_check.py

Open the folder on GitHubat commit 816aaae

Compare with similar skills

Dingo Verify next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dingo Verify compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dingo Verify this skillMigoXLab/dingo757—~741Automated safety check: NotesApache-2.0
Researchzhongkaifu/TensorSharp557—~2.3kAutomated safety check: WarnBSD-3-Clause
Claude Maintain ModelsKiln-AI/Kiln5.2k—~15kAutomated safety check: NotesCustom licence
Fact Checkerdaymade/claude-code-skills1.4k2 repos~2.1kAutomated safety check: PassMIT
Deep Reviewdyad-sh/dyad22k—~1.4kAutomated safety check: PassCustom licence
Remember Learningsdyad-sh/dyad22k—~1.1kAutomated safety check: PassCustom licence

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More from MigoXLab/dingo

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    MigoXLab/dingo

    A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.

    757 GitHub stars~833 tokensUpdated 10 days ago
    Auto-check passed

Questions about Dingo Verify

What does Dingo Verify do?

A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document. Dingo Verify is an agent skill from MigoXLab/dingo. Use when the user wants to fact-check an article or verify factual claims in a document.

When should I use Dingo Verify?

Dingo Verify fits situations like: the user wants to fact-check an article; verify factual claims in a document.

How do I install Dingo Verify in Claude Code?

Run `npx skills add MigoXLab/dingo --skill dingo-verify -a claude-code`. Or copy the skill folder (.claude/skills/dingo-verify in MigoXLab/dingo) into .claude/skills/dingo-verify in your project. Claude Code loads it when a task matches its description.

How do I install Dingo Verify in Codex?

Run `npx skills add MigoXLab/dingo --skill dingo-verify -a codex`. Or copy the skill folder (.claude/skills/dingo-verify in MigoXLab/dingo) into .agents/skills/dingo-verify in your project. Codex loads it when a task matches its description.

Can I use Dingo Verify in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MigoXLab/dingo --skill dingo-verify -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dingo-verify, .gemini/skills/dingo-verify, .github/skills/dingo-verify and .opencode/skills/dingo-verify in your project.

What does Dingo Verify need to run?

Going by SKILL.md and its folder, Dingo Verify needs Python for the scripts in its folder, the command-line tools its instructions call (pip and python) and credentials named OPENAI_API_KEY and TAVILY_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY; A credential in TAVILY_API_KEY. Its frontmatter pre-approves these tools: Bash, Read, Glob.

Does Dingo Verify access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dingo Verify safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Dingo Verify use?

Dingo Verify is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dingo Verify use?

About 741 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1k tokens, read only when the agent opens those files.

What are the alternatives to Dingo Verify?

Skills that share tags, products or a category with Dingo Verify: Research (zhongkaifu/TensorSharp, 557 stars), Claude Maintain Models (Kiln-AI/Kiln, 5.2k stars), Fact Checker (daymade/claude-code-skills, 1.4k stars) and Deep Review (dyad-sh/dyad, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dingo Verify?

MigoXLab (a GitHub organization) maintains it in MigoXLab/dingo, which has 757 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on September 28, 2026.

Source: MigoXLab/dingo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.