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 description is essentially a placeholder that provides no useful information about the skill's capabilities, domain, or appropriate usage context. It only names the tool and its invocation command, which is insufficient for Claude to make informed skill selection decisions. This is among the weakest possible descriptions.
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, FEA-based design, or minimizing material usage in engineering designs').
Remove the invocation instruction ('invoke with $agent-topology-optimizer') from the description and replace it with functional content—invocation details belong elsewhere, not in the selection-critical description field.
| 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 actually does, only that it exists. | 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, with 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 tool name 'topology-optimizer' is somewhat specific, the description is so vague that Claude would have no basis for distinguishing when to use this skill versus any other. It provides no domain context or use-case boundaries. | 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 consisting of speculative, non-executable JavaScript class definitions for a topology optimization system. None of the code can be run, no concrete steps are provided, and the content reads like an aspirational architecture document rather than a skill that teaches Claude how to perform a specific task. It violates every dimension of the rubric: it's verbose, abstract, unstructured, and lacks any workflow or validation guidance.
Suggestions
Replace speculative class definitions with actual executable code or concrete CLI commands that Claude can run, showing real inputs and expected outputs.
Define a clear step-by-step workflow: e.g., 1) Analyze current topology, 2) Run optimization command, 3) Validate results, 4) Apply changes — with explicit validation checkpoints.
Cut the content by 80%+ — remove all placeholder classes and undefined method calls. Focus on the 2-3 most important operations with complete, working examples.
Add progressive disclosure by creating a concise overview in SKILL.md and moving detailed algorithm implementations to separate reference files.
| 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 call unimplemented functions. The bash commands at the end reference `npx claude-flow` subcommands without explaining if they exist or how to set them up. This is architectural fantasy, not actionable guidance. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequence of steps to follow. The content presents disconnected class definitions and concepts without explaining when or how to use them, in what order, or with what validation checkpoints. No feedback loops or error recovery are described. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of code blocks with no references to external files, no clear navigation structure, and no separation between overview and detail. Everything is dumped inline with no hierarchy or signposting for discovery. | 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
01070ed
- Reviewed
Table of Contents
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