SKILLEMALL.ai

AB dev-workflow

Orchestrate coding agents (Claude Code, Codex, etc.) to implement coding tasks through a structured workflow. Use when the user gives a coding requirement, feature request, bug fix, or GitHub issue to implement. Includes requirement analysis, document generation (requirement doc + verification doc), agent dispatch, monitoring, verification, and delivery. NOT for simple one-line fixes or reading code. Triggers on coding tasks, feature requests, bug reports, GitHub issues, or "implement/build/fix this".

ClawHub Agent Skills author: lgYanami v1.2.0 MIT-0 4 files body ≈ 2 276 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 73/100 · Nearly there — weak spots: result and completion, consistency, running it twice

ProcedureGitHubSoftware 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
B
73/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
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: 4. 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 73/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 8 mutating operations with no state check
    • 40Consistency. Frontmatter name (dev-workflow) differs from the folder (agent-dev-workflow)
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 60 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 2276 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 506: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: suspicious
    This is a coherent coding-agent workflow, but it defaults to highly privileged agent execution and automatic project file changes that users should review carefully.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026