SKILLEMALL.ai

AC technical-spec-design

Transforms product requirements into structured technical specifications. Auto-triggers when requirements are unclear, multiple implementation approaches exist, or component-level/architecture design is needed. Auto-trigger conditions: - User asks "how to implement a feature" - Requests design of technical specs / architecture / APIs / components - Mentions "multiple implementation approaches, need comparison" - Provides PRD / requirements description, wants technical specs - Requirements contain uncertainty or ambiguity NOT applicable for: - Simple bug fixes - Simple features with clear implementation path - Pure coding tasks (no design decisions)

ClawHub Agent Skills author: wjszxli v1.0.1 MIT-0 11 files body ≈ 1 638 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 11. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 62 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1638 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 656: enough signal without eating the budget
    • +4Structure: 36 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (2 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This skill is a disclosed technical-specification helper with optional local template scripts and no evidence of hidden data access, persistence, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026