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AC geo-fact-checker

GEO-focused fact-checking and evidence collection assistant for written content. Use this skill whenever the user wants to verify factual claims (numbers, dates, rankings, market share, competitor data, quotes, or statistics), validate sources, or increase AI trust in content by attaching precise citations and up-to-date evidence. Prefer this skill for content that should be highly reliable for AI citations, reports, comparison pages, landing pages, and data-driven articles.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 6 files body ≈ 5 303 tokens Open the sourcegithub.com↗ analyzed 3 d ago

GEO-focused fact-checking and evidence collection assistant for written content.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, progress reporting

AnalyzerData and analyticsResearchtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5303 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 63/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Steps. 176 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 70Failures and branches. 4 branches
  • 70Execution cost. Instruction body is 5303 tokens
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill
  • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 479: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 176 items
  • +3Output format is stated explicitly
  • +4Has examples (0 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.