SKILLEMALL.ai

BF cross-material-consistency-auditor

This skill should be used when two or more materials on the same topic or event need to be compared for cross-material consistency before publication. It identifies mismatched numbers, product names, fact wording, terminology, source attributions, structural promises, and cross-platform expression drift across articles, press releases, slide decks, web pages, white papers, and social posts. It produces a diff matrix with severity ratings and recommended unified wording, without modifying originals. Trigger keywords: 口径审计, 跨材料核对, 多平台一致性, 口径不一致, consistency audit, cross-material check, PR vs 稿, 初稿 vs 通稿, multi-platform review, fact drift.

ClawHub Agent Skills author: haiyangchen v1.0.4 MIT-0 9 files body ≈ 1 326 tokens Open the sourceclawhub.ai analyzed 2 d ago

This skill should be used when two or more materials on the same topic or event need to be compared for cross-material consistency before publication.

As a process F 53/100 · Will not run — References files that are not bundled: scripts/product-terms.txt

AnalyzerAI and agentsWriting and documentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: scripts/product-terms.txt
Tools and files w 18
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • 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: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: This skill should be used when two or more materials on the same t… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning missing-ref reference to a missing file: scripts/product-terms.txt
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "not_for"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: scripts/product-terms.txt
  • 0Tools and files. 1 referenced file(s) missing: scripts/product-terms.txt
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (cross-material-consistency-auditor) differs from the folder (cross-material-consistency-auditor-skill)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 56 steps
  • 100Execution cost. Instruction body is 1326 tokens

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
  • +4Description does not say when NOT to use the skill (false activations)
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 644: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 56 items
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This skill is a read-only publication consistency auditor that compares user-provided materials and writes audit outputs without hidden network, credential, or persistence behavior.
LLM: benign (high) · VirusTotal: · 31 Aug 2026