SKILLEMALL.ai

AC doc-consistency

Check a long document against itself — dangling cross-references, numbering gaps and duplicates, TOC-vs-body mismatch, missing standard sections, terminology drift, leftover placeholders (TODO/TBD/XXX), stale years, and paragraphs lost in a merge. Reads .docx / .md / .txt and emits a located findings list plus a re-check command you can put in a contract. Runs fully offline with zero dependencies: no network, no upload, no model calls, so the document never leaves the machine. Use when the user asks to proofread, QA, sanity-check, or verify the internal consistency of a textbook, manuscript, bid or RFP response, manual, thesis, or contract set; or asks for a cross-reference audit, numbering audit, continuity check, or editorial consistency check; or says 长文档一致性 / 交叉引用 / 编号跳号 / 重号 / 术语不统一 / 合稿丢段 / 目录对不上 / 标书自检 / 稿件体检 / 定稿前检查 / 教材编号核对.

ClawHub Agent Skills author: dongsheng123132 v1.1.0 MIT-0 16 files body ≈ 572 tokens Open the sourceclawhub.ai analyzed 2 d ago

Check a long document against itself — dangling cross-references, numbering gaps and duplicates, TOC-vs-body mismatch, missing standard sections, terminology…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ReferenceWordWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 572 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3Description length 845: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

    External checks

    ClawHub: clean
    This skill is a local document-consistency checker that reads user-selected documents and reports issues without hidden network, credential, or persistence behavior.
    LLM: benign (high) · VirusTotal: · 10 Aug 2026