agent-adaptive-coordinator
Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator
40
6%
Does it follow best practices?
Impact
100%
1.51xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-adaptive-coordinator/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 fails on every dimension because it only states the invocation syntax without describing what the skill does or when it should be selected. Claude would have no basis for choosing this skill over any other.
Suggestions
Add concrete actions describing what 'adaptive-coordinator' actually does (e.g., 'Orchestrates multi-step workflows by dynamically assigning tasks to sub-agents based on complexity').
Add an explicit 'Use when...' clause with natural trigger terms that describe the situations where this skill should be selected (e.g., 'Use when the user needs to coordinate multiple tasks, manage parallel workflows, or delegate subtasks').
Replace the generic 'agent skill' framing with domain-specific language that distinguishes this from other agent or coordination skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for adaptive-coordinator' is entirely abstract and gives 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, providing no functional or contextual information. | 1 / 3 |
Trigger Term Quality | The only keyword is 'adaptive-coordinator', which is technical jargon and not something a user would naturally say. There are no natural trigger terms that would help Claude match user requests to this skill. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it could conflict with any coordination, orchestration, or agent-related skill. There are no distinct triggers or domain-specific terms to differentiate it. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
12%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, conceptual document that reads more like a design specification or whitepaper than an actionable skill for Claude. The Python code is entirely pseudocode with unimplemented methods, the MCP commands lack verifiable context, and the document is padded with generic ML/systems concepts Claude already knows. The content would benefit enormously from being reduced to ~50 lines of concrete, executable guidance with clear workflows.
Suggestions
Reduce content to under 100 lines by removing all conceptual Python pseudocode classes and replacing with actual executable commands or concrete decision rules Claude can follow
Remove generic best practices, KPI definitions, and ML concept explanations that Claude already knows—focus only on project-specific configuration and commands
Add a clear, numbered step-by-step workflow for the primary use case (e.g., 'When to switch topology and how') with explicit validation commands after each step
Split detailed reference material (topology decision matrix, rollback protocols) into separate bundle files and reference them from a concise overview in SKILL.md
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~350+ lines. Contains extensive Python class definitions that are conceptual pseudocode (not executable), lengthy YAML decision matrices, ASCII art diagrams, and explanations of ML concepts Claude already knows. The KPIs section, best practices, and much of the 'architecture' content are generic padding that don't provide actionable value. | 1 / 3 |
Actionability | Despite containing many code blocks, nearly all Python code is pseudocode with undefined methods (e.g., self.collect_performance_metrics(), self.initialize_ml_model()). The bash MCP commands appear concrete but reference tools/APIs whose existence and exact syntax are unverifiable. The skill describes concepts rather than providing executable, copy-paste-ready instructions. | 1 / 3 |
Workflow Clarity | The 'Topology Transition Protocols' section provides a phased migration process with rollback mechanisms, and the switching conditions are clearly enumerated. However, validation checkpoints are vague ('Validation of improved performance'), there's no concrete feedback loop with specific commands to verify success, and the overall workflow for actually using this coordinator is unclear amidst the conceptual content. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. All content—from architecture diagrams to Python classes to best practices—is inlined in a single massive document. There's no separation of overview from detailed reference material, and no bundle files exist to support progressive disclosure. | 1 / 3 |
Total | 5 / 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/ruflo
- Commit
844f68d
- Reviewed
Table of Contents
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