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AC repo-kanban-pm

Install and enforce a lightweight product-management workflow inside a code repo: feature-as-kanban boards, ROADMAP status tracking, branch/PR conventions, and an optional daily OpenClaw cron PM review. Use this skill when (1) starting work in a NEW repository/project that does not yet have a ROADMAP+feature-KANBAN system, (2) you are delegated to spin up a new project and want multi-agent coordination from day one, or (3) you are asked to fix/restructure an existing repo and introduce an organized backlog/feature tracking system. Also use when adding a daily PM audit loop (cron) to keep code + docs + PRs in sync.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 2 scripts body ≈ 557 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Install and enforce a lightweight product-management workflow inside a code repo: feature-as-kanban boards, ROADMAP status tracking, branch/PR conventions…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureOperations and projectsSoftware 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
C
53/100
Has gaps
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: 3. 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 53/100

    • 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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 557 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 621: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (2 code blocks)
    • +3All 2 scripts are documented

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