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AD academic-paper

12-agent academic paper writing pipeline on Hermes Agent. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX/PDF output. Uses delegate_task for each agent. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

ClawHub Agent Skills author: Andy Ren v3.1.4 MIT-0 72 files body ≈ 1 298 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWordLaTeXResearchAI and agentstype 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
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
77
Run on models
none yet
Process rating
D
46/100
Unfinished process
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration exfil-secret-in-url shared/cross_model_verification.md:191
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/${ARS_CROSS_MODEL}:generateContent?key=…" \
    placeholder
  • low Exfiltration net-credential-use shared/cross_model_verification.md:191
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/${ARS_CROSS_MODEL}:generateContent?key=…" \
    known service

Files scanned: 72. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1298 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 396: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 4 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (4 of 25)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This is mostly a prompt-only academic writing skill, but it needs review because some optional verification paths can send manuscript or source data to outside model providers and are not clearly gated by user consent.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026