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AC ralph-mode

Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw.

ClawHub Agent Skills author: richginsberg v1.2.0 5 files · 1 script body ≈ 3 609 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 5. 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 12 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 85 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3609 tokens
    • 100Progress reporting. Reports progress
    • low 16 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • -42 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
    • +3Description length 313: enough signal without eating the budget
    • +4Structure: 46 headings
    • +3Step-by-step instructions: 85 items
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

    External checks

    ClawHub: suspicious
    This skill is not malicious, but it should be reviewed because it can drive autonomous code changes and recommends very high sub-agent parallelism that could consume substantial resources.
    LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026