SKILLEMALL.ai
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AD openclaw-autonomous

Autonomous programming mode for openclaw.ai. Use this skill whenever a user requests any code change, feature addition, refactor, bug fix, or project task — especially large or multi-part requests. This skill eliminates unnecessary clarifying questions, prevents false completion claims, enforces self-verification via code reading, maintains honest working state to prevent the AI from building on hallucinated progress, and preserves the project constitution throughout execution. Trigger this skill any time the user asks Claude to build, fix, edit, or extend anything in the codebase.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 695 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Autonomous programming mode for openclaw.ai. Use this skill whenever a user requests any code change, feature addition, refactor, bug fix, or project task —…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerSoftware developmenttype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
49/100
Unfinished process
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 49/100

    • 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
    • 40Consistency. Frontmatter name (openclaw-autonomous) differs from the folder (just-keep-working)
    • 60Tools and files. Uses tools (read, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 28 steps
    • 100Execution cost. Instruction body is 2695 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 588: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.