openclaw-mission-control

Coordinate AI agent teams via a Kanban task board with local JSON storage. Enables multi-agent workflows with a Team Lead assigning work and Worker Agents executing tasks via heartbeat polling. Perfect for building AI agent command centers.

3.41x

Quality

58%

Does it follow best practices?

Impact

99%

3.41x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./data/skills-md/0xindiebruh/openclaw-mission-control-skill/openclaw-mission-control/SKILL.md

Quality

Discovery

40%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description establishes a clear and distinctive niche for multi-agent coordination via Kanban boards, but suffers from missing explicit trigger guidance and relies on marketing language ('Perfect for building AI agent command centers') instead of actionable selection criteria. The technical specificity is moderate but would benefit from more concrete action verbs.

Suggestions

Add an explicit 'Use when...' clause with trigger terms like 'coordinate agents', 'multi-agent workflow', 'task assignment', 'agent team management'

Replace marketing fluff ('Perfect for building AI agent command centers') with concrete trigger scenarios (e.g., 'Use when managing tasks across multiple AI agents or implementing agent-to-agent coordination')

List specific concrete actions like 'create tasks', 'assign work to agents', 'track task status', 'poll for updates' to improve specificity

Dimension	Reasoning	Score
Specificity	Names the domain (AI agent teams, Kanban task board) and some actions (assigning work, executing tasks, heartbeat polling), but lacks comprehensive concrete actions like 'create tasks', 'update status', 'query board state'.	2 / 3
Completeness	Describes what it does but completely lacks a 'Use when...' clause or explicit trigger guidance. The phrase 'Perfect for building AI agent command centers' is marketing fluff rather than actionable trigger criteria.	1 / 3
Trigger Term Quality	Includes some relevant terms like 'Kanban', 'task board', 'multi-agent', 'Team Lead', 'Worker Agents', but uses technical jargon ('heartbeat polling') and misses natural user phrases like 'agent coordination', 'task management', 'assign tasks to agents'.	2 / 3
Distinctiveness Conflict Risk	The combination of 'AI agent teams', 'Kanban task board', 'Team Lead/Worker Agents', and 'local JSON storage' creates a very specific niche that is unlikely to conflict with other skills.	3 / 3
	Total	8 / 12 Passed

Implementation

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured, highly actionable skill for coordinating multi-agent workflows. Its main strength is the comprehensive, executable examples covering the full task lifecycle. The primary weakness is length—the content would benefit from splitting detailed API references and configuration examples into separate files to improve token efficiency.

Suggestions

Extract the API Reference tables into a separate REFERENCE.md file and link to it

Move the detailed agent configuration examples (TypeScript config, OpenClaw config) to a SETUP.md file

Consolidate redundant curl examples—the heartbeat section and worker operations section show nearly identical patterns

Dimension	Reasoning	Score
Conciseness	The skill is comprehensive but includes some redundancy (e.g., curl examples repeated in multiple sections, full agent config shown twice). The content could be tightened by consolidating API examples and removing duplicate patterns.	2 / 3
Actionability	Excellent actionability with fully executable curl commands, complete TypeScript configuration examples, and copy-paste ready bash commands. Every operation has concrete, working code.	3 / 3
Workflow Clarity	Clear task lifecycle diagram with explicit state transitions. The multi-agent workflow example at the end provides a complete numbered sequence with validation (Lead reviews and approves). Heartbeat polling creates natural feedback loops.	3 / 3
Progressive Disclosure	Content is well-organized with clear sections, but the skill is monolithic at ~400 lines. The API reference tables and detailed configuration could be split into separate reference files. Only external references are to GitHub repo.	2 / 3
	Total	10 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: NeverSight/skills_feed
Commit: 5342bca

Reviewed: about 2 hours ago

Table of Contents

Discovery Implementation Validation

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