TaskMaster - AI Cost Optimizer
v1.0.0Project manager and task delegation system. Use when you need to break down complex work into smaller tasks, assign appropriate AI models based on complexity, spawn sub-agents for parallel execution, track progress, and manage token budgets. Ideal for research projects, multi-step workflows, or when you want to delegate routine tasks to cheaper models while handling complex coordination yourself.
⭐ 8· 2.8k·11 current·11 all-time
bySonnyW@jlwrow
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (task delegation, model selection, sub-agents, token tracking) match the included files and code. The Python code implements complexity analysis, model selection, spawn-command generation, and local cost logging consistent with the stated functionality. Use of Anthropic model identifiers is coherent with cost-optimization claims.
Instruction Scope
SKILL.md and code stick to orchestration, model selection, spawn command generation, and cost tracking; they do not instruct reading unrelated system files or requesting unrelated credentials. However, several integration functions are stubs or designed for manual invocation (e.g., execute_task prints spawn commands and returns instructions rather than calling sessions_spawn directly; track_session_cost/session_status parsing is incomplete/truncated). Also the skill writes/updates a local JSON cost log (taskmaster-costs.json) which may contain task metadata; review whether that file could store sensitive task text before use.
Install Mechanism
No install spec and no external downloads. The skill is instruction+code only and depends only on Python standard capabilities. There are no URLs, installers, or extracted archives that would execute arbitrary remote code during install.
Credentials
The package declares no required environment variables, no credentials, and no config paths. The code expects OpenClaw platform functions (sessions_spawn, session_status) for integration but does not request unrelated secrets or cloud credentials.
Persistence & Privilege
always:false (no forced inclusion). The skill writes a local cost log (taskmaster-costs.json) and returns spawn commands that include 'cleanup': 'keep' which may retain session artifacts until cleaned. The skill does not request system-wide privileges or alter other skills' config, but consider that saved logs may contain task descriptions or outputs.
Assessment
This skill appears internally coherent and implements what it claims: model triage, sub-agent spawn command generation, and local token-cost tracking. Before installing or running it, consider: 1) Integration is partially manual/stubbed — execute_task prints spawn commands rather than automatically calling sessions_spawn, and track_session_cost is not fully implemented; expect to supply platform session calls or finish integration. 2) Cost/log file (taskmaster-costs.json) will be created/updated locally and may contain task descriptions, token counts, or outputs — treat that as potentially sensitive and store it securely or audit what gets written. 3) The spawn command uses cleanup: "keep" (retains session artifacts); decide whether you want sessions retained. 4) The skill references Anthropic model names — using it will incur model/billing costs when you actually spawn sessions. 5) Because the skill can generate and suggest automated sub-agent work, review and control actual execution (sessions_spawn/session_status) on your OpenClaw instance to avoid unintended automated runs. If you want higher assurance, review the full delegate_task.py and openclaw_integration.py to confirm there are no hidden network calls or logging of full task outputs to external endpoints.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.