SKILLEMALL.ai

AB chapter-lead-writer

Write H2 chapter lead blocks (`sections/S<sec_id>_lead.md`) that preview the chapter's comparison lens and connect its H3 subsections, without adding new facts. **Trigger**: chapter lead writer, section lead writer, H2 lead, lead paragraph, 章节导读, 章节导语. **Use when**: you have H2 chapters with multiple H3 subsections and the draft reads like paragraph islands across subsections. **Skip if**: the outline has no H3 subsections, or `outline/chapter_briefs.jsonl` is missing. **Network**: none. **Guardrail**: no new facts/citations; no headings; no narration templates; use only citation keys present in `citations/ref.bib`.

ClawHub Agent Skills author: WILLOSCAR v1.0.0 MIT-0 26 files body ≈ 1 328 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 72/100 · Nearly there — weak spots: when it triggers, progress reporting

GeneratorResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Tools and files w 18
60
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: 26. 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 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 60 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1328 tokens
    • 100Running it twice. No mutating operations
    • low 13 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 623: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 60 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    This looks like a modest chapter-lead helper on the surface, but the package bundles broader research pipelines and workspace-writing behavior that are not clearly disclosed.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026