agent-worker-specialist
Agent skill for worker-specialist - invoke with $agent-worker-specialist
44
13%
Does it follow best practices?
Impact
98%
10.88xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-worker-specialist/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely weak description that fails on every dimension. It provides no information about what the skill does, when it should be used, or what domain it covers. It reads as a placeholder or auto-generated stub rather than a functional skill description.
Suggestions
Replace the entire description with concrete actions the skill performs, e.g., 'Delegates tasks to specialized worker agents for parallel processing of X, Y, Z.'
Add an explicit 'Use when...' clause that describes the trigger conditions and user scenarios where this skill should be selected.
Include natural language keywords and terms that users would actually say when they need this skill's capabilities, rather than internal naming conventions like 'worker-specialist'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for worker-specialist' is entirely abstract with no indication of what the skill actually does. | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is answered. The description only states the invocation command without explaining the skill's purpose or trigger conditions. | 1 / 3 |
Trigger Term Quality | There are no natural keywords a user would say. 'worker-specialist' is internal jargon, and 'invoke with $agent-worker-specialist' is a technical invocation command, not a user-facing trigger term. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so generic that it provides no distinguishing information. 'Worker-specialist' could refer to virtually any domain, making it impossible to differentiate from other skills. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is overly verbose, repeating the same memory coordination pattern across 8+ nearly identical code blocks when a single template with a table of variations would suffice. While it provides concrete MCP tool call structures, the code has syntax inconsistencies and lacks error handling or validation steps. The monolithic structure with no external references wastes token budget on repetitive examples that could be factored out.
Suggestions
Consolidate the repetitive mcp__claude-flow__memory_usage blocks into a single template with a table or list showing the key/value variations for each worker type and status update.
Add explicit validation steps: verify memory writes succeeded, handle MCP call failures, and include a feedback loop for retrying failed task steps.
Split specialized worker types (code, analysis, testing) into separate reference files and link to them from a concise overview section.
Fix the inconsistent JavaScript syntax in the dependency management section—either use pure MCP tool call notation or proper async/await, not a mix of both.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with repetitive JSON blocks that all follow the same pattern (memory_usage store calls). The skill could be condensed to a single template with variations noted, rather than repeating nearly identical code blocks 8+ times. Much of the content (work patterns, quality standards, integration points) is generic agent behavior that Claude can infer. | 1 / 3 |
Actionability | The code blocks show specific MCP tool calls with concrete JSON structures, which is useful. However, the JavaScript is not truly executable—it mixes `await` with raw MCP call syntax inconsistently, uses placeholder values like `[ID]` and `[feature]` without explaining how to resolve them, and the dependency management example uses invalid syntax (mixing async/await with raw tool calls). | 2 / 3 |
Workflow Clarity | The sequential execution pattern lists clear steps (receive, verify, execute, report, deliver), and the task execution protocol has a logical start/progress/complete flow. However, there are no explicit validation checkpoints—no verification that stored memory was actually persisted, no error handling for failed MCP calls, and no feedback loop for when task execution itself fails. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of text with no references to external files and no bundle files to support it. All specialized worker types, patterns, and metrics are inlined despite being clearly separable. The content would benefit greatly from splitting worker types into separate reference files. | 1 / 3 |
Total | 6 / 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
- ruvnet/claude-flow
- Commit
9d4a9ea
- Reviewed
Table of Contents
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