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AA context-product-manager

Turn rough product, feature, or repo-change requests into a PM-grade plan plus agent-ready execution context. Use whenever the user has a vague idea, wants a PRD/spec/phase plan, needs repo-aware context for Codex/Antigravity/OpenClaw, wants a large request cut into a clear MVP or phased plan, or asks for a coding-agent handoff that must preserve scope, constraints, and acceptance criteria. Prefer this skill over generic brainstorming or direct coding when the work needs product framing before or during implementation.

ClawHub Agent Skills author: openclawzhu-AI v0.1.0 MIT-0 6 files body ≈ 1 963 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 94/100 · Runs to the end — weak spots: running it twice

ProcedureSoftware developmentAI and agentstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
A
94/100
Runs to the end
Running it twice w 4
30
Inputs and preconditions w 11
70
Tools and files w 18
100
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 94/100

    • 30Running it twice. 3 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 102 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1963 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • medium 4 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 524: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 102 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is an instruction-only planning skill that reads relevant project files to produce product plans and coding-agent handoffs, with no install scripts or hidden execution behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026