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AB project-task-manager

Project Task Manager: AI-powered task planning: generate hierarchical task trees from objectives, decompose tasks, track progress, visualize status. Persistent across sessions. Use when an agent needs project task manager, ai task generation, automatic task breakdown, project decomposition, objective to tasks, decompose, task, level of detail through AgentPMT-hosted remote tool calls. Discovery terms: project task manager, ai task generation, automatic task breakdown, project decomposition.

ClawHub Agent Skills author: AgentPMT v1.0.0 MIT-0 3 files body ≈ 3 547 tokens Open the sourceclawhub.ai↗ analyzed 31 h ago

Project Task Manager: AI-powered task planning: generate hierarchical task trees from objectives, decompose tasks, track progress, visualize status.

As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

GeneratorAI 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 73/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 79 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3547 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 495: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 79 items
    • +3Output format is stated explicitly
    • +4Has examples (12 code blocks)

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

    External checks

    ClawHub: clean
    This is a disclosed remote project-planning skill that persists task data, with no evidence of hidden execution, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 10 Jun 2026