agent-topology-optimizer
Agent skill for topology-optimizer - invoke with $agent-topology-optimizer
33
0%
Does it follow best practices?
Impact
92%
1.58xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-topology-optimizer/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 minimal description that essentially only provides the skill's name and invocation command. It completely lacks any explanation of what the skill does, what actions it performs, or when it should be used. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.
Suggestions
Add concrete capability descriptions such as 'Performs structural topology optimization using finite element analysis, generates optimized material distributions, and produces mesh outputs' or whatever the skill actually does.
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about topology optimization, structural optimization, material layout optimization, FEA-based design, or load path analysis.'
Remove the invocation instruction ('invoke with $agent-topology-optimizer') from the description field, as this is operational metadata rather than a description of capabilities and triggers.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. 'Agent skill for topology-optimizer' is entirely vague—it doesn't describe what the skill does, only names itself. There are no listed capabilities. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command, with no explanation of functionality or usage triggers. | 1 / 3 |
Trigger Term Quality | The only potentially relevant term is 'topology-optimizer', which is a technical/internal name rather than a natural keyword a user would say. There are no natural language trigger terms like 'optimize topology', 'structural optimization', 'FEA', etc. | 1 / 3 |
Distinctiveness Conflict Risk | While 'topology-optimizer' is a specific name, the description is so vague that Claude cannot determine when to select it versus any other skill. The lack of any functional description makes it impossible to distinguish from other optimization or engineering skills. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an architectural design document masquerading as an actionable skill. It presents hundreds of lines of non-executable pseudocode with undefined dependencies, explains concepts Claude already understands (genetic algorithms, simulated annealing, graph partitioning), and provides no clear workflow for actually performing topology optimization. The CLI commands and MCP integrations reference tools without documenting their availability, installation, or expected behavior.
Suggestions
Replace the illustrative class definitions with a concise, step-by-step workflow: 1) Analyze current topology, 2) Generate recommendations, 3) Validate the plan, 4) Apply changes, 5) Monitor results—with explicit validation checkpoints.
Make the CLI commands actionable by documenting prerequisites, expected inputs/outputs, and providing a concrete example with sample output.
Remove the extensive algorithm implementations (genetic algorithm, simulated annealing, graph partitioning) since Claude already knows these concepts—instead, specify which commands or MCP calls invoke them and what parameters to tune.
Split advanced content (algorithm details, neural network integration, performance metrics) into separate referenced files, keeping SKILL.md as a concise overview with clear navigation links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~600+ lines. The code is largely pseudocode/illustrative class definitions that Claude cannot execute. It explains concepts like genetic algorithms, simulated annealing, and graph partitioning at length—things Claude already knows. Most code instantiates undefined classes (e.g., `new HierarchicalTopology()`, `new LatencyAnalyzer()`) providing no real implementation value. | 1 / 3 |
Actionability | None of the code is executable—every class references undefined dependencies and constructors. The CLI commands (e.g., `npx claude-flow topology-optimize`) are listed without explaining prerequisites, installation, or expected outputs. The MCP integration references functions like `mcp.swarm_status()` and `mcp.topology_optimize()` without documenting their actual API or availability. | 1 / 3 |
Workflow Clarity | There is no clear step-by-step workflow for performing topology optimization. The content presents architectural concepts and class structures but never sequences them into an actionable process with validation checkpoints. No feedback loops or error recovery steps are defined despite the complexity and potentially destructive nature of topology reconfiguration. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files and no bundle files to support it. All content—from basic topology types to genetic algorithms to simulated annealing—is dumped inline with no organization into separate reference documents. The sheer volume of non-executable code makes navigation difficult. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (813 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- ruvnet/claude-flow
- Commit
d29d87f
- Reviewed
Table of Contents
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