SKILLEMALL.ai
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AC citation-diversifier

Raise citation diversity/density (NO NEW FACTS): generate an in-scope “citation budget” plan per H3 so drafts stop failing the global unique-citation gate and stop looking under-cited. **Trigger**: cite boost, citation budget, unique citations too low, add more citations, improve reference density, 引用太少, 增加引用, 引用密度. **Use when**: `pipeline-auditor` FAILs due to low unique citations, or you want to increase cite density without changing claims. **Skip if**: you need new papers (fix C1/C2 mapping first), or `citations/ref.bib` / `outline/writer_context_packs.jsonl` is missing. **Network**: none. **Guardrail**: NO NEW FACTS; do not invent citations; only use keys already present in `citations/ref.bib`; keep citations within each H3’s allowed scope (`outline/writer_context_packs.jsonl` / `outline/evidence_bindings.jsonl`).

ClawHub Agent Skills author: WILLOSCAR v1.0.0 MIT-0 19 files body ≈ 1 055 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 63/100 · Has gaps — weak spots: when it triggers, running it twice, progress reporting

AnalyzerResearchtype 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%
87
Run on models
none yet
Process rating
C
63/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
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 63/100

    • 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
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 50 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1055 tokens
    • 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)
    • +3Description length 830: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 50 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: 87.

    External checks

    ClawHub: suspicious
    The visible citation-budget helper is mostly coherent, but the package also installs broad, under-disclosed research pipeline and workflow tooling unrelated to citation diversification.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026