SKILLEMALL.ai

AC chapter-briefs

Build per-chapter (H2) writing briefs (NO PROSE) so the final survey reads like a paper (chapter leads + cross-H3 coherence) without inflating the ToC. **Trigger**: chapter briefs, H2 briefs, chapter lead plan, section intent, 章节意图, 章节导读, H2 卡片. **Use when**: `outline/outline.yml` + `outline/subsection_briefs.jsonl` exist and you want thicker chapters (fewer headings, more logic). **Skip if**: the outline is still changing heavily (fix outline/mapping first). **Network**: none. **Guardrail**: NO PROSE; do not invent papers; only reference subsection ids and already-mapped papers.

ClawHub Agent Skills author: WILLOSCAR v1.0.0 MIT-0 19 files body ≈ 1 081 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 57/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
57/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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: 19. 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 57/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 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
    • 85Steps. 51 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1081 tokens
    • low The response is described with custom markup (4 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)
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 586: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 51 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The advertised chapter-brief helper is narrow, but the installed package also contains unrelated routeable research pipelines and workflow-control tooling that need review before use.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026