agent-agent
Agent skill for agent - invoke with $agent-agent
37
6%
Does it follow best practices?
Impact
93%
4.65xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-agent/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely poor skill description that provides essentially no useful information. It fails on every dimension: it describes no concrete actions, includes no natural trigger terms, answers neither what the skill does nor when to use it, and is completely indistinguishable from any other agent-related skill.
Suggestions
Replace the entire description with concrete actions the skill performs (e.g., 'Spawns sub-agents to handle parallel tasks, delegates work across multiple contexts, and coordinates agent responses').
Add an explicit 'Use when...' clause with natural trigger terms that describe the situations where this skill should be selected.
Include specific keywords and file types or task types that distinguish this skill from other potentially similar skills in the collection.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for agent' is entirely vague and abstract, providing no information about what the skill actually does. | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is answered. There is no 'Use when...' clause and no description of functionality. | 1 / 3 |
Trigger Term Quality | The only keyword is 'agent', which is overly generic and not a natural term a user would say when needing a specific capability. The invocation syntax '$agent-agent' is not a natural trigger term. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic and the term 'agent' could conflict with virtually any agent-related skill. There is nothing to distinguish this from other skills. | 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 extremely bloated and largely non-actionable. It reads more like a marketing whitepaper or architectural design document than a practical skill file. The extensive code examples are pseudo-executable at best, referencing numerous undefined functions and classes, making them unusable as concrete guidance. The content would benefit enormously from being reduced to ~50 lines of core workflow with references to separate files for advanced topics.
Suggestions
Reduce the main skill to under 100 lines: a brief description, the list of MCP tools, one concrete executable example, and links to separate files for advanced workflows (multi-agent, learning, gaming AI).
Make code examples truly executable by either providing complete self-contained snippets or removing the pretense of being runnable code and instead providing clear step-by-step instructions for how to invoke the MCP tools.
Remove marketing language and concept explanations (what GOAP is, what A* is, what behavior trees are) - Claude already knows these concepts.
Split advanced topics (multi-agent coordination, learning from execution, gaming AI integration, utility planning) into separate referenced files to enable progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at 500+ lines. Massive amounts of code that are not executable (references undefined helper functions like `buildConsensusMatrix`, `generatePreferenceVector`, `stateKey`, etc.). Explains concepts Claude already knows (what GOAP is, what A* search is, what behavior trees are). The marketing-style language ('cutting edge of AI-driven objective achievement') wastes tokens. | 1 / 3 |
Actionability | Despite the volume of code, almost none of it is executable. Functions reference undefined helpers, classes extend undefined base classes (GOAPAgent), and MCP tool calls use inconsistent naming (underscores vs hyphens). The code is elaborate pseudocode dressed up as real code - it cannot be copy-pasted and run. | 1 / 3 |
Workflow Clarity | The numbered workflow steps (1-5) provide a sequence, but there are no validation checkpoints, no error recovery feedback loops in the main workflow, and the steps blend into increasingly complex code blocks without clear 'verify before proceeding' gates. The error handling section exists but is separate from the workflow. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. Everything is inlined - the gaming AI section, multi-agent coordination, advanced configuration, learning systems - all crammed into one massive document. No clear separation between quick-start essentials and advanced/reference material. | 1 / 3 |
Total | 5 / 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 (821 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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