SKILLEMALL.ai

AB pm-workbench

Use when product work needs clearer framing, prioritization, or communication: clarifying a vague request, evaluating whether a feature is worth doing, comparing options, prioritizing requests, drafting a lightweight spec, building a roadmap, defining metrics, preparing an executive summary, reviewing outcomes, or making product-leadership/founder trade-offs. Best when the user needs a practical recommendation or reusable output, not just frameworks. Do not use for raw data crunching, deep project tracking, legal/compliance review, generic marketing copy, or pure UI copy.

ClawHub Agent Skills author: Bobbie v1.2.0 MIT-0 80 files body ≈ 3 611 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ProcedureData and analyticsWriting and documentsOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Tools and files w 18
60
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: 80. 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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Failures and branches. 14 branches
    • 100Steps. 183 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3611 tokens
    • 100Running it twice. Mutating operations check current state
    • low 18 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
    • +4No input/output examples
    • -48 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 578: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 183 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This is a product-management prompt/workflow skill with no evidence of data exfiltration, destructive behavior, credential use, or hidden execution.
    LLM: benign (high) · VirusTotal: · 30 May 2026