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spec-task
by
LeoNardo-LB
· GitHub ↗
· v1.2.1
· MIT-0
172
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Install in OpenClaw
/install spec-task
Description
结构化任务管理与生命周期强制执行。以下场景必须使用：(1) 任何被 coordinator 通过 sessions_spawn 派发的任务 (2) 可拆解步骤≥3 的复杂任务 (3) 工作区已存在 spec-task/ 目录时的后续任务 (4) 用户显式要求使用 spec-task (5) prependCont...
Usage Guidance
This skill is coherent for enforcing a spec-driven task workflow and is instruction-only (no external downloads or credential requests). Before installing: (1) confirm you are comfortable with the skill creating and modifying files under your agent workspace (spec-task/<task>/, outputs/, memory/*), (2) ensure no sensitive secrets or PII will be written to those locations, (3) be aware child agents dispatched by a coordinator will be required to follow this workflow (may interfere with other agent behaviors), and (4) review and, if needed, adjust config.yaml (timeouts, auto_archive, per_agent_archive) to match your environment. A minor oddity: docs mention legacy .py script names even though no Python scripts are included—this appears to be a documentation remnant only.
Capability Analysis
Type: OpenClaw Skill
Name: spec-task
Version: 1.2.1
The spec-task skill bundle is a structured task management framework designed to enforce a rigorous lifecycle (briefing, planning, execution, and verification) for AI agents. It manages task states via local YAML and Markdown files (e.g., status.yaml, checklist.md) and provides tools for logging and archiving project history. There is no evidence of data exfiltration, unauthorized command execution, or malicious intent; the prescriptive instructions in SKILL.md and schema.yaml are solely focused on ensuring agent compliance with the defined organizational workflow.
Capability Assessment
Purpose & Capability
Name and description (structured task management, lifecycle enforcement) match the provided artifacts: SKILL.md, schemas, templates, and config all define a spec-driven task workflow. There are no unrelated required env vars, binaries, or install steps.
Instruction Scope
The runtime instructions require reading and writing workspace files (spec-task/<task>/status.yaml, brief/spec/plan/checklist, outputs/, memory/task-history, etc.) and mandate a strict workflow for child agents. That file I/O and workspace mutation are expected for a task-management skill, but you should be aware it will create and update files in the agent workspace and agent memory. There are no instructions to call external network endpoints or access unrelated system config.
Install Mechanism
Instruction-only skill with no install spec and no code files to fetch or execute. This is the lowest-risk install profile.
Credentials
The skill declares no required environment variables, credentials, or config paths. The config.yaml controls local behavior (timeouts, archive rules). Requested accesses (workspace, writing memory archives) are proportional to a task-management capability.
Persistence & Privilege
always:false (normal). The skill expects to write archival history and lessons into agent memory and workspace (memory/task-history, memory/task-lessons, outputs/). This is typical for a task-tracking skill but means artifacts and potentially sensitive content could be stored in the agent workspace/memory—review storage policies if that is a concern. The skill also enforces use by spawned child agents per its instructions; that is a behavioral constraint rather than an elevated system privilege.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install spec-task - After installation, invoke the skill by name or use
/spec-task - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.2.1
The skill has been deprecated due to unstable factors and can be used in the form of a plugin with the same name instead.
v1.2.0
**Summary:**
Introduced a historical recall feature and made configuration/usage scope clearer.
- Added `task_recall.py` script for retrieving relevant task history and lessons before starting new tasks.
- Updated documentation to require running recall before task creation to promote reuse of prior work and avoid repeated mistakes.
- Adjusted config and file paths: now use `spec-task/` as the standard task and config directory instead of `spec-tasks/`; updated related documentation and examples.
- Added a metadata section specifying Python3 as a runtime requirement.
- Slightly reduced test coverage notice from 117 to 115 case references.
- Clarified that the skill is for tasks with 5 or more steps and is intended for complex/structurable tasks.
v1.0.0
Spec-task 1.0.0 – Initial Release
- Introduces a spec-driven agent task management system using a structured workflow: brief → spec → plan → checklist → verify → archive.
- All task state managed by scripts and stored in files (status.yaml); agents focus on generating creative content.
- Fully automates config initialization and deep merge from project and skill defaults.
- Provides robust scripts for task creation, state transitions, checklist management, verification, archiving, and recovery.
- Enables traceable revisions, resumable checkpoints, configurable failure policies, and persistent project/task histories.
- All core operations documented, with user-friendly command references and flexible error handling workflows.
Metadata
Frequently Asked Questions
What is spec-task?
结构化任务管理与生命周期强制执行。以下场景必须使用：(1) 任何被 coordinator 通过 sessions_spawn 派发的任务 (2) 可拆解步骤≥3 的复杂任务 (3) 工作区已存在 spec-task/ 目录时的后续任务 (4) 用户显式要求使用 spec-task (5) prependCont... It is an AI Agent Skill for Claude Code / OpenClaw, with 172 downloads so far.
How do I install spec-task?
Run "/install spec-task" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is spec-task free?
Yes, spec-task is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does spec-task support?
spec-task is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created spec-task?
It is built and maintained by LeoNardo-LB (@leonardo-lb); the current version is v1.2.1.
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