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

71%

Does it follow best practices?

Impact

99%

3.41x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

Quality

Content

71%Weight 40%Scale 1-5

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

The body is highly actionable with concrete, copy-paste-ready commands and a clear lifecycle, but it is padded by repetition and inlines material (full API reference, full config) that would fit better in separate reference files. Adding verification steps and trimming duplicated curl examples would improve it.

Suggestions

Move the full API reference tables and the complete AGENT_CONFIG example into separate reference files (e.g. REFERENCE.md, CONFIG.md) and link to them from the body, keeping only key examples inline.

Remove the duplicated curl commands — the heartbeat steps in Quick Start step 5 overlap with the Worker Agent Operations section and the Example workflow re-shows commands already demonstrated.

Add explicit verification checkpoints between steps (e.g. after `npm run dev` confirm the board loads at localhost:8080; after `/api/seed` confirm agents were created) to strengthen the workflow.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with concrete commands, but it repeats content (the heartbeat steps in Quick Start step 5 overlap heavily with Worker Agent Operations, and the Example workflow re-states curl commands already shown) and inlines a large config block that could be trimmed.	3 / 5
Actionability	Fully executable, copy-paste-ready curl commands with exact endpoints, JSON payloads, and a complete API reference table; the common cases (create, pick, log, complete, approve) are all covered with concrete examples.	5 / 5
Workflow Clarity	A clear numbered sequence (Quick Start 1-5) plus a task lifecycle diagram and cleanly separated Team Lead vs Worker operations, but it lacks explicit verification checkpoints (e.g. confirm the server is up, verify a task was created/picked).	4 / 5
Progressive Disclosure	Section headers give reasonable structure, but with no bundle files present the entire API reference, full agent config, and all examples are inlined in a ~470-line SKILL.md rather than split into one-level-deep reference files.	3 / 5
	Total	15 / 20 Passed

Description

71%Weight 40%Scale 1-5

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 is specific and concrete with strong action verbs and a distinct niche, but it lacks an explicit 'Use when...' trigger clause, leaving the 'when' only weakly implied. Adding explicit trigger guidance would lift completeness.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when coordinating multiple AI agents, setting up a multi-agent workflow, or building an agent command center.'

Include natural synonyms users might say (e.g. 'task board', 'agent orchestration', 'team of agents') to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Coordinate AI agent teams via a Kanban task board with local JSON storage', 'Team Lead assigning work', 'Worker Agents executing tasks via heartbeat polling' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	The 'what' is clear (coordinate agent teams via Kanban), but there is no explicit 'Use when...' clause; 'Perfect for building AI agent command centers' is only a weakly implied trigger, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Good keyword coverage with natural terms like 'AI agent teams', 'Kanban', 'multi-agent workflows', and 'command centers', but missing common synonyms or variations a user might phrase differently.	4 / 5
Distinctiveness Conflict Risk	The niche is fairly distinct ('Kanban task board', 'heartbeat polling', 'multi-agent workflows') with minimal conflict risk, though it could overlap slightly with general task-management or agent-orchestration skills.	4 / 5
	Total	16 / 20 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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: NeverSight/skills_feed
Path: data/skills-md/0xindiebruh/openclaw-mission-control-skill/openclaw-mission-control/SKILL.md
Commit: 2f57aec

Reviewed: about 22 hours ago

Table of Contents

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