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AC openclaw

OpenClaw is the high-autonomy agent mode. It proactively decides, plans, and executes tasks with deep context fusion, including a full sweep of historical memory files (`MEMORY.md` and `memory/*.md`) when privacy rules allow. Use this skill whenever the user wants something handled autonomously, says things like 'mach mal', 'just do it', 'handle it', 'erledige das', 'kümmer dich drum', 'mach das selbst', or invokes `/autopilot`. Also use it when a task needs end-to-end execution without hand-holding, when the user wants proactive follow-through, or when they want the agent to decide what to do next.

ClawHub Agent Skills author: Hendrik Schmitz v9.0.1 MIT-0 14 files body ≈ 4 722 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

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

ProcedureAI and agentsInfrastructuretype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 14. 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

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 40Consistency. Frontmatter name (openclaw) differs from the folder (openclaw-autopilot-v9)
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4722 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 17 steps
    • 100Progress reporting. Reports progress
    • low 13 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
    • -230 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 606: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (17 code blocks)
    • +4Reference files are cited in the instructions (12 of 12)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    This instruction-only Autopilot skill is not malware, but it grants very broad autonomous access, scheduling, memory, and persistence powers that users should review carefully.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026