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.
67
49%
Does it follow best practices?
Impact
99%
3.41xAverage score across 3 eval scenarios
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.mdQuality
Discovery
57%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 around multi-agent Kanban task coordination with JSON storage, which helps differentiate it from other skills. However, it lacks an explicit 'Use when...' clause, relies on some marketing language ('Perfect for building AI agent command centers'), and could be more specific about the concrete operations it supports.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to coordinate multiple AI agents, manage a Kanban board, or set up task assignment workflows.'
Replace the marketing phrase 'Perfect for building AI agent command centers' with concrete actions like 'create tasks, assign tasks to agents, update task status, poll for new assignments, and track task completion.'
Include natural trigger term variations such as 'task management', 'agent orchestration', 'workflow coordination', or 'task queue'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI agent teams, Kanban task board) and some actions (assigning work, executing tasks, heartbeat polling), but the actions are described at a high level rather than listing specific concrete operations like 'create tasks', 'update task status', 'query board state'. | 2 / 3 |
Completeness | The 'what' is partially covered (Kanban task board with JSON storage, multi-agent workflows), but there is no explicit 'Use when...' clause or equivalent trigger guidance. The phrase 'Perfect for building AI agent command centers' is vague marketing language rather than explicit trigger conditions. | 2 / 3 |
Trigger Term Quality | Includes some relevant terms like 'Kanban', 'task board', 'multi-agent', 'Team Lead', 'Worker Agents', but misses common user-facing terms like 'task management', 'workflow orchestration', 'agent coordination', 'JSON task file'. The phrase 'AI agent command centers' is marketing fluff rather than a natural trigger term. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of Kanban task board, local JSON storage, multi-agent workflows with Team Lead/Worker Agent roles, and heartbeat polling creates a very distinct niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 9 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable guidance with complete curl commands and configuration examples, which is its primary strength. However, it is severely bloated — repeating API operations across multiple sections (heartbeat, team lead ops, worker ops, API table, example workflow) and including excessive inline content that should be split into referenced files. It also lacks validation checkpoints for a multi-step setup process involving server startup, database seeding, and multi-agent configuration.
Suggestions
Reduce redundancy by removing duplicate API examples — keep the API reference table and one concise example workflow, removing the separate Team Lead Operations and Worker Agent Operations sections that repeat the same endpoints.
Extract the full agent config, heartbeat template, and API reference table into separate referenced files (e.g., CONFIG.md, HEARTBEAT_TEMPLATE.md, API_REFERENCE.md) and keep SKILL.md as a concise overview.
Add validation checkpoints: verify server is running after npm run dev, confirm seed response, check that agent heartbeat polling returns expected data before proceeding.
Trim the agent config example to 2 agents instead of 6 — Claude can extrapolate the pattern.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines. It includes a full agent config example with 6 agents when 2 would suffice, repeats API endpoints in multiple sections (heartbeat, team lead operations, worker operations, API reference table, and example workflow), and includes information like recommended team structures and tips that add little value for Claude. | 1 / 3 |
Actionability | Every operation includes fully executable curl commands with proper headers and JSON payloads. The setup steps include concrete bash commands, the config files are complete and copy-paste ready, and the API reference table covers all endpoints. | 3 / 3 |
Workflow Clarity | The task lifecycle diagram is clear and the example workflow shows a good end-to-end sequence. However, there are no validation checkpoints — no verification that the server started successfully, no check that seed worked, no error handling guidance if curl commands fail or tasks are in unexpected states. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with no references to external files. The full API reference, agent configuration examples, heartbeat templates, team lead operations, worker operations, comments system, and configuration details are all inline. The API reference table, detailed config examples, and heartbeat template should be in separate referenced files. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
100%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
f772de4
- Reviewed
Table of Contents
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