SKILLEMALL.ai

AC coding-agent

Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.

ClawHub Agent Skills author: utromaya-code v1.0.0 MIT-0 2 files body ≈ 2 598 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerDiscordSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Consistency w 8
40
the three weakest of ten parameters · all ten

The same skill appears in 3 more places: ClawHub, ClawHub, ClawHub

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: 2. 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 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (coding-agent) differs from the folder (coding-delegate-agent)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 100Steps. 25 steps
    • 100Execution cost. Instruction body is 2598 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • 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

    • +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 648: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This instruction-only skill is coherent for delegating coding work, but it normalizes high-autonomy agents that can change code, install dependencies, push branches, and post to GitHub with limited review gates.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026