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, generates mesh-based designs, and computes stress/strain analysis').
Add an explicit 'Use when...' clause with natural trigger terms (e.g., 'Use when the user asks about topology optimization, structural optimization, material layout, finite element design, or lightweight structure generation').
Remove the invocation instruction ('invoke with $agent-topology-optimizer') from the description—this is implementation detail, not selection criteria—and replace it with capability and context information.
| 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 extensive but entirely non-actionable document that presents speculative, non-executable JavaScript class definitions for a topology optimization system. It reads like a design document or API wishlist rather than a skill that teaches Claude how to perform specific tasks. The massive code volume provides no real guidance since none of it can be executed, and it lacks any workflow, validation steps, or concrete instructions.
Suggestions
Replace speculative class definitions with actual executable commands or real API calls that Claude can use to perform topology optimization tasks
Add a concise quick-start section (under 20 lines) showing the most common topology optimization workflow with concrete, runnable steps
Define a clear multi-step workflow with validation checkpoints, e.g.: analyze current topology → identify bottlenecks → select optimization strategy → apply changes → verify improvement
Remove or move the advanced algorithm implementations (genetic algorithm, simulated annealing) to separate reference files and keep only actionable summaries in the main skill
| 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 illustrative pseudocode dressed up as real code, with placeholder methods like `this.calculateAverageLatency()` that have no implementation. | 1 / 3 |
Actionability | None of the code is executable — every class references undefined dependencies (HierarchicalTopology, MeshTopology, LatencyAnalyzer, etc.) and methods that are never implemented. The bash commands reference `npx claude-flow` subcommands without explaining if they exist or how to set them up. The entire skill describes rather than instructs. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequence of steps for performing topology optimization. The content presents disconnected code blocks without explaining when or how to use them, has no validation checkpoints, and no error recovery guidance. The MCP integration section mixes conceptual code with no clear operational sequence. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of code blocks with no references to external files and no clear hierarchy. All content is inline regardless of complexity, with no separation between quick-start material and advanced algorithms. The 'Integration Points' section is a brief bullet list that could have linked to detailed docs but doesn't. | 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
398f7c2
- Reviewed
Table of Contents
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.