SKILLEMALL.ai

AC openclaw-deep-research

Coordinate deep, source-verifiable research projects in OpenClaw using AgentSkills-compatible folders, local artifact tracking, and explicit evidence ledgers. Use when the user wants a high-quality report or investigation that spans many sources, needs auditable claims, benefits from delegation, or must manage context-window limits by writing project state to local files during an OpenClaw workflow.

ClawHub Agent Skills author: ZZXX-bit v1.0.0 MIT-0 9 files body ≈ 1 576 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerAI and agentsInfrastructureData and analyticstype 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
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
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: 9. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (openclaw-deep-research) differs from the folder (multi-agent-deep-research)
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 58 steps
    • 100Execution cost. Instruction body is 1576 tokens
    • 100Progress reporting. Reports progress

    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
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 402: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 58 items
    • +4Reference files are cited in the instructions (5 of 5)
    • +1License stated

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

    External checks

    ClawHub: clean
    This is a structured research workflow skill that saves research ledgers in the user’s workspace and shows no hidden execution, credential handling, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026