SKILLEMALL.ai

AC skill-compiler

Use when you need to compile any prompt OR multi-source content (PDF/video/URL/image/doc) into a production-grade, reusable AI Skill. Triggers on: 'prompt to skill', 'compile prompt', '把 prompt 变成 skill', '提示词编译', 'PDF转skill', '视频转skill', '网页转skill', 'skill from prompt', 'skill from document'. Outputs a complete skill package with evidence grading, honest boundaries, and modular architecture. Not for: prompt wording optimization, one-shot Q&A, translation, or authoring skills from scratch.

ClawHub Agent Skills author: qomob v1.2.2 MIT-0 22 files body ≈ 2 156 tokens Open the sourceclawhub.ai analyzed 2 d ago

Triggers on: 'prompt to skill', 'compile prompt', '把 prompt 变成 skill', '提示词编译', 'PDF转skill', '视频转skill', '网页转skill', 'skill from prompt', 'skill from document'.

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 1. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (skill-compiler) differs from the folder (skillcompiler)
    • 60Tools and files. Uses tools (bash, git) that frontmatter does not declare
    • 100Steps. 45 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 2156 tokens
    • 100Running it twice. No mutating operations

    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
    • +3Output format is not stated: the model decides each time
    • -224 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 494: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (13 of 14)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This is a documented skill-building workflow with some quality and scoping caveats, but no evidence of hidden, destructive, or deceptive behavior.
    LLM: benign (high) · VirusTotal: · 12 Jul 2026