SKILLEMALL.ai

BD academic-pipeline

10-stage end-to-end research-to-publication pipeline orchestrator on Hermes Agent. Stages: planning → deep-research → academic-paper → academic-paper-reviewer → revision → polish → ethics → disclosure → format → deliver. Resume support. Uses delegate_task for each stage. Triggers: full pipeline, end-to-end research, research to paper, publish paper, 完整學術流程, 從研究到發表.

ClawHub Agent Skills author: Andy Ren v1.0.3 MIT-0 41 files body ≈ 868 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
69
Run on models
none yet
Process rating
D
39/100
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: 41. 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 39/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (academic-pipeline) differs from the folder (ars-academic-pipeline)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 11 steps
  • 100Execution cost. Instruction body is 868 tokens
  • 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
  • -417 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 367: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (5 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This appears to be a legitimate academic workflow, but it needs Review because it can persist research/session history and optionally send manuscript material to third-party AI APIs without clear per-run consent.
LLM: suspicious (high) · VirusTotal: · 29 May 2026