SKILLEMALL.ai

AC researchclaw

Automate setup, configuration, execution, monitoring, and troubleshooting of AutoResearchClaw — the 23-stage autonomous research pipeline that generates conference-grade papers. Use when the user mentions ResearchClaw, wants to write a research paper autonomously, needs to set up or debug the pipeline, or says research paper, autonomous research, or paper generation.

ClawHub Agent Skills author: Ahmad Othman Ammar Adi. v0.1.0 MIT-0 12 files · 5 scripts body ≈ 2 803 tokens Open the sourceclawhub.ai analyzed 2 d ago

Automate setup, configuration, execution, monitoring, and troubleshooting of AutoResearchClaw — the 23-stage autonomous research pipeline that generates…

As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, consistency

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Consistency w 8
40
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: 12. 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 64/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 40Consistency. Frontmatter name (researchclaw) differs from the folder (researchclaw-2)
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 60 steps
    • 100Execution cost. Instruction body is 2803 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • -34 of 5 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 369: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    The skill matches its research-automation purpose, but it can run a long autonomous pipeline while skipping human approval gates and using external services or code execution modes.
    LLM: suspicious (high) · 30 Aug 2026