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Route TODO checklists to the right storage + TaskList conversational discipline. Topics — session (/wip), file (fix_plan.md), issue (GitHub Issues), completion-report, fix-plan-sync, priority-prefix, media-separation (tracking vs recording vs knowledge). Use when: "TODO management", "checklist", "todowrite", "fix_plan cleanup", "register as issue", "completion report", "task priority", "media-separation".

ClawHub Agent Skills author: es6kr v0.9.1 MIT-0 13 files · 1 script body ≈ 837 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

TemplateGitHubPersonal productivityData and analyticstype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
D
43/100
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token priority-prefix.md:26
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - `P{n}-issue{num}-{desc}` — e.g. `P2-i…ion`
      quoted

    Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "depends-on"

    Process rating: all ten parameters 43/100

    • 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
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 837 tokens

    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

    • +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
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 408: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This task-management skill is mostly coherent, but it includes broad persistent task authority and unsafe local scripts that can delete or alter files beyond what users would reasonably expect.
    LLM: suspicious (high) · 10 Sept 2026