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 fails on all dimensions. It provides no information about what the skill does, when to use it, or what user requests should trigger it. It reads as a placeholder rather than a functional skill description.
Suggestions
Add concrete actions describing what topology-optimizer does (e.g., 'Performs structural topology optimization to determine optimal material distribution within a design space, minimizing weight while meeting stress constraints').
Add an explicit 'Use when...' clause with natural trigger terms (e.g., 'Use when the user asks about topology optimization, structural optimization, material layout, weight minimization, or finite element-based design optimization').
Remove the invocation instruction ('invoke with $agent-topology-optimizer') from the description, as this is operational detail that doesn't help Claude decide when to select the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for topology-optimizer' is entirely vague—it doesn't describe what the skill actually does, only names itself. | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is answered. The description only states it's an agent skill and how to invoke it, providing no functional or contextual information. | 1 / 3 |
Trigger Term Quality | The only keyword is 'topology-optimizer', which is a technical tool name rather than a natural term a user would say. There are no natural language trigger terms like 'optimize topology', 'structural optimization', 'material distribution', etc. | 1 / 3 |
Distinctiveness Conflict Risk | While the name 'topology-optimizer' is somewhat niche, the description is so vague that Claude would have no basis to distinguish when to select this skill versus any other. There are no distinguishing triggers or capability descriptions. | 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 extremely verbose, non-actionable collection of speculative JavaScript class stubs that describe a theoretical topology optimization system. None of the code is executable, no clear workflow is provided, and the content massively over-explains concepts without providing any concrete, usable guidance. It reads more like a design document or wishlist than an actionable skill.
Suggestions
Replace speculative class stubs with actual executable code or concrete CLI commands that perform real topology optimization tasks, with specific inputs and expected outputs.
Define a clear step-by-step workflow: e.g., 1) Analyze current topology, 2) Generate recommendations, 3) Validate recommendations, 4) Apply changes, 5) Verify improvement — with explicit validation checkpoints.
Reduce content by 80%+ — remove all undefined placeholder classes and methods, and focus on the minimal set of concrete actions the agent should take.
Split advanced topics (genetic algorithms, simulated annealing, neural network integration) into separate referenced files rather than inlining hundreds of lines of non-functional code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~600+ lines. The content is filled with speculative, non-executable class definitions that explain concepts Claude already understands (genetic algorithms, simulated annealing, graph partitioning). Most code is aspirational pseudocode dressed up as real implementations, with placeholder methods like `this.calculateAverageLatency()` that have no actual logic. | 1 / 3 |
Actionability | None of the code is executable — every class references undefined dependencies (HierarchicalTopology, MeshTopology, LatencyAnalyzer, etc.), and methods call other undefined methods. The bash commands reference `npx claude-flow` subcommands that may not exist. There is no concrete, copy-paste-ready guidance for actually performing topology optimization. | 1 / 3 |
Workflow Clarity | There is no clear multi-step workflow or sequence of operations. The content presents disconnected class definitions and code blocks without explaining when or how to use them, in what order, or with what validation checkpoints. No feedback loops or error recovery steps are defined. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of code blocks with no references to external files and no clear hierarchy. Everything is dumped inline — genetic algorithms, simulated annealing, neural network integration, MCP hooks, placement strategies — with no organization that helps a reader navigate to what they need. | 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/ruflo
- Commit
0f7c750
- Reviewed
Table of Contents
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