Content
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, largely non-actionable document that reads more like a marketing whitepaper or academic concept paper than a practical skill guide. It contains hundreds of lines of pseudocode masquerading as executable examples, explains numerous concepts Claude already understands (A*, PageRank, behavior trees, GOAP), and provides no validation steps or progressive disclosure structure. The core useful content—which MCP tools to call and in what order—is buried under layers of unnecessary abstraction.
Suggestions
Reduce content by 80%+: Remove explanations of well-known algorithms (A*, PageRank, behavior trees, GOAP concepts) and focus only on the specific MCP tool calls, their parameters, and expected outputs.
Make code examples truly executable: Ensure all referenced functions and classes are defined or clearly noted as user-provided, use consistent MCP tool naming conventions, and show actual expected return values from tool calls.
Split into progressive disclosure structure: Create a concise SKILL.md overview (under 100 lines) with references to separate files for advanced patterns, multi-agent coordination, and usage examples.
Add validation checkpoints to the workflow: After each major step (graph construction, optimization, plan execution), include explicit verification steps showing how to validate the output before proceeding.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at 500+ lines. Explains concepts Claude already knows (GOAP, A*, behavior trees, PageRank, utility theory). Massive amounts of illustrative pseudocode that isn't truly executable. The marketing-style descriptions ('cutting edge of AI-driven objective achievement') and emoji headers add no value. Could be reduced to ~20% of its size. | 1 / 3 |
Actionability | Despite the volume of code, almost none of it is executable. Functions reference undefined helpers (buildConsensusMatrix, generatePreferenceVector, canTransition, stateKey, etc.), 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 implementations, not copy-paste ready guidance. | 1 / 3 |
Workflow Clarity | There is a numbered workflow (steps 1-5) showing a logical progression from state modeling through graph construction, prioritization, temporal planning, and A* search. However, there are no validation checkpoints, no error recovery feedback loops in the main workflow, and the steps don't clearly indicate when to verify outputs before proceeding. The OODA loop in DynamicPlanner is conceptual rather than practically sequenced. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files and no bundle files to support it. Everything is inlined in a single massive document with no clear separation between overview and detailed reference material. The content would benefit enormously from splitting into separate files for examples, API reference, and advanced patterns. | 1 / 3 |
Total | 5 / 12 Passed |