SKILLEMALL.ai

AB deep-research

use for adaptive deep research, broad but accurate information gathering, literature review, github and project due diligence, source graph investigation, cited reports, claim verification, or decisions that require current sources, cross-checking, counterevidence, and synthesis across web pages, academic papers, official docs, repositories, datasets, local files, and conflicting perspectives. do not use for simple lookups answerable from one or two obvious sources.

ClawHub Agent Skills author: BlackC4T v1.0.3 MIT-0 12 files body ≈ 2 148 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerGitHubData and analyticsResearchInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
98
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
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 65/100

    • 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 (deep-research) differs from the folder (b143kc47-deep-research)
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Execution cost. Instruction body is 2148 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 470: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 98.

    External checks

    ClawHub: clean
    This is a disclosed deep-research skill that uses a local Python ledger to track research work, with no evidence of hidden access, exfiltration, or unsafe automation.
    LLM: benign (high) · VirusTotal: · 29 May 2026