Research
zhongkaifu/TensorSharp
A skill your agent uses for web searches and current information lookups, finding sources, fact-checking, researching questions, comparing sources, or summarising web pages.
A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.
$ npx skills add MigoXLab/dingo --skill dingo-verify -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MigoXLab/dingo dingo-verify --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .claude/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verifyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add MigoXLab/dingo --skill dingo-verify -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MigoXLab/dingo dingo-verify --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/dingo-verify .agents/skills/dingo-verify && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .agents/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MigoXLab/dingo --skill dingo-verify -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MigoXLab/dingo dingo-verify --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/dingo-verify .cursor/skills/dingo-verify && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .cursor/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/MigoXLab/dingo.git --path .claude/skills/dingo-verify--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add MigoXLab/dingo --skill dingo-verify -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MigoXLab/dingo dingo-verify --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/dingo-verify .gemini/skills/dingo-verify && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .gemini/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install MigoXLab/dingo dingo-verifyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add MigoXLab/dingo --skill dingo-verify -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/dingo-verify .github/skills/dingo-verify && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .github/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MigoXLab/dingo --skill dingo-verify -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MigoXLab/dingo dingo-verify --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MigoXLab/dingo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/dingo-verify .opencode/skills/dingo-verify && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dingo-verify" agent skill from https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify into .opencode/skills/dingo-verify/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dingo-verify", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
dingo-verifyA 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. 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 816aaae. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
BashReadGlobFrom allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pippythonFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names these keys or tokens, usually read from environment variables:
OPENAI_API_KEYTAVILY_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash, Read, GlobAutomated 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.
The full file from MigoXLab/dingo at commit 816aaae, republished under its Apache-2.0 licence (© MigoXLab). 271 words, ~741 tokens.
.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.Verify factual claims in articles using Dingo's ArticleFactChecker agent.
Before running, verify:
python -c "from dingo.config import InputArgs; print('OK')"OPENAI_API_KEY is set (required)TAVILY_API_KEY is set (optional, enables web search verification)If prerequisites fail, help the user fix them:
pip install -e . (from project root) or pip install dingo-pythonpip install -r requirements/agent.txtexport OPENAI_API_KEY='your-key'Run the fact-check script with the article path:
python ${CLAUDE_SKILL_DIR}/scripts/fact_check.py $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).md, .txt: Markdown/plaintext articles (auto-wrapped for Dingo).jsonl: JSONL with {"content": "..."} per line.json: JSON array formatThe script outputs JSON to stdout. Parse it and present to the user:
Present a formatted report with these sections:
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/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.
--max-claims 10 --model gpt-5.4-miniFor 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
SKILL.md and 2 other files (scripts, references) in .claude/skills/dingo-verify of MigoXLab/dingo.
Open the folder on GitHubat commit 816aaae
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dingo Verify this skillMigoXLab/dingo | 757 | — | ~741 | Automated safety check: Notes | Apache-2.0 | |
| Researchzhongkaifu/TensorSharp | 557 | — | ~2.3k | Automated safety check: Warn | BSD-3-Clause | |
| Claude Maintain ModelsKiln-AI/Kiln | 5.2k | — | ~15k | Automated safety check: Notes | Custom licence | |
| Fact Checkerdaymade/claude-code-skills | 1.4k | 2 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Deep Reviewdyad-sh/dyad | 22k | — | ~1.4k | Automated safety check: Pass | Custom licence | |
| Remember Learningsdyad-sh/dyad | 22k | — | ~1.1k | Automated safety check: Pass | Custom licence |
zhongkaifu/TensorSharp
A skill your agent uses for web searches and current information lookups, finding sources, fact-checking, researching questions, comparing sources, or summarising web pages.
Kiln-AI/Kiln
Add new AI models to Kiln's mlmodellist.py and produce a Discord announcement.
daymade/claude-code-skills
Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation.
dyad-sh/dyad
Deep multi-agent code review run locally — a fleet of parallel finder agents reviews the diff from independent angles, then adversarial verifier agents reproduce each finding before it is reported.
dyad-sh/dyad
Review the current session for errors, issues, snags, and hard-won knowledge, then update the rules/ files (or AGENTS.md if no suitable rule file exists) with actionable learnings.
liangdabiao/GEO-Content-Optimizer-Skill
完整的 GEO(生成式引擎优化)服务流水线:给一个产品官网 URL 和介绍材料, 做站点诊断与 AI 答案采样、生成带验收标准的执行工单、产出可直接部署的资产 (llms.txt / JSON-LD / 定义块 / FAQ / 内容大纲与初稿)、自动验收工单是否闭环、 并打包成可直接发给客户的交付物。可按周期复跑,做长期 GEO 运营与月报。
MigoXLab/dingo
A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.
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.
Dingo Verify fits situations like: the user wants to fact-check an article; verify factual claims in a document.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.