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

49%

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

Content

42%

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

The skill excels at actionability with fully executable curl commands and concrete configuration examples, but suffers significantly from verbosity — the same API endpoints are documented 3-4 times across different sections. The lack of any validation/error-handling steps in a multi-agent coordination workflow is a notable gap, and the entire content should be restructured across multiple files rather than presented as one massive document.

Suggestions

Eliminate redundancy by keeping API details in a single reference table and having other sections reference it, or split the API reference into a separate REFERENCE.md file.

Trim the agent config example to 2-3 agents instead of 6 — Claude can extrapolate the pattern.

Add validation checkpoints: verify server is running after npm run dev, check seed response, verify agent heartbeat connectivity before proceeding.

Split into multiple files: HEARTBEAT_TEMPLATE.md for the heartbeat setup, API_REFERENCE.md for the endpoint table, and EXAMPLES.md for the multi-agent workflow walkthrough.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~300+ lines. It repeats API endpoints multiple times (heartbeat section, team lead operations, worker agent operations, API reference table, and example workflow all show the same curl commands). The agent config example is unnecessarily long with 6 agents when 2 would suffice. The recommended team structure table duplicates information already in the config example.	1 / 3
Actionability	Every operation includes fully executable curl commands with proper headers and JSON payloads. The setup instructions are copy-paste ready with concrete bash commands, config files, and directory creation steps.	3 / 3
Workflow Clarity	The task lifecycle diagram is clear and the end-to-end example workflow in 5 steps is well-sequenced. However, there are no validation checkpoints — no verification that the server started correctly, no check that seeding succeeded, no error handling guidance if a task pick fails or an agent heartbeat doesn't connect.	2 / 3
Progressive Disclosure	Everything is crammed into a single monolithic file with no references to supporting documents. The API reference table, full agent config, heartbeat template, and detailed examples could all be split into separate files. No bundle files exist to support progressive disclosure.	1 / 3
	Total	7 / 12 Passed

Description

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 identifies a clear and distinctive niche (multi-agent Kanban coordination with JSON storage) but lacks an explicit 'Use when...' clause, which weakens its completeness. The final sentence ('Perfect for building AI agent command centers') reads as marketing fluff rather than actionable trigger guidance. The specificity is moderate—it names the architecture but could enumerate more concrete operations.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to orchestrate multiple AI agents, manage tasks on a Kanban board, or set up agent team workflows.'

Replace the marketing phrase 'Perfect for building AI agent command centers' with natural trigger terms users might say, such as 'task management', 'agent orchestration', 'multi-agent coordination', or 'workflow automation'.

List more concrete actions the skill supports, e.g., 'create/move/assign tasks, track agent status, manage task queues, read/write local JSON task files'.

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 somewhat high-level and not fully concrete—e.g., what specific operations can be performed on the board? 'Coordinate', 'assigning', and 'executing' are moderately specific but not as granular as listing discrete operations.	2 / 3
Completeness	The 'what' is reasonably covered (coordinate agent teams via Kanban board with JSON storage, Team Lead assigns, Workers execute). However, there is no explicit 'Use when...' clause or equivalent trigger guidance—'Perfect for building AI agent command centers' is a marketing claim, not an explicit trigger condition. Per rubric guidelines, missing 'Use when' caps completeness at 2.	2 / 3
Trigger Term Quality	Includes some relevant keywords like 'Kanban', 'task board', 'multi-agent', 'Team Lead', 'Worker Agents', 'JSON storage', and 'heartbeat polling'. However, it misses common natural user terms like 'task management', 'agent orchestration', 'workflow automation', or simpler phrases a user might say. Some terms like 'AI agent command centers' are more marketing than natural triggers.	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. This is unlikely to conflict with other skills given its highly specific architecture and use case.	3 / 3
	Total	9 / 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: c7b7905

Reviewed: about 4 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.