Content
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill suffers significantly from boilerplate bloat — roughly 60-70% of the content is generic template text that adds no NOS-specific value and wastes context window tokens. The core NOS assessment workflow (Selection, Comparability, Exposure evaluation) is present but lacks concrete examples, expected JSON schemas, and validation checkpoints. The skill would be dramatically improved by stripping all generic sections and focusing on the domain-specific NOS criteria, example inputs/outputs, and the actual assessment workflow.
Suggestions
Remove all generic boilerplate sections (When to Use, When Not to Use, Required Inputs, Recommended Workflow, Output Contract, Validation and Safety Rules, Failure Handling) — these describe behaviors Claude already knows and waste ~60% of the token budget.
Add a concrete example showing a sample NOS evaluation: input study excerpt → expected JSON output with scores for each domain, so Claude knows exactly what the deliverable looks like.
Define the expected JSON schema for the format_nos_table.py input explicitly, so the handoff between evaluation and formatting is unambiguous.
Add a validation checkpoint after the JSON aggregation step (Step 3) to verify all required NOS fields are populated before running the formatting script.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. The skill contains multiple redundant sections (e.g., 'When to Use', 'When Not to Use', 'Required Inputs', 'Recommended Workflow', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling') that are generic boilerplate not specific to NOS assessment. The actual NOS-specific content (the Usage and Detailed Workflow sections) is buried among filler. Concepts like 'validate inputs before execution' and 'don't fabricate results' are things Claude already knows. | 1 / 3 |
Actionability | The NOS-specific workflow steps (Selection, Comparability, Exposure evaluation) provide some concrete guidance, and there are executable bash commands for the scripts. However, the actual NOS criteria are deferred to a reference file, the JSON schema for output is never shown, and the detailed workflow steps lack concrete examples of what a proper evaluation looks like. The format_nos_table.py command is shown but the expected JSON input structure is missing. | 2 / 3 |
Workflow Clarity | The detailed workflow has a clear 4-step sequence (Selection → Comparability → Exposure → Generate Summary Table), which is good. However, there are no validation checkpoints between steps, no feedback loops for error recovery, and the workflow is diluted by a separate generic 'Recommended Workflow' section that competes with the actual NOS-specific workflow. No example of what valid intermediate output looks like at each step. | 2 / 3 |
Progressive Disclosure | There is a reference to `references/nos_criteria_prompts.md` for detailed criteria, which is appropriate progressive disclosure. However, no bundle files were provided to verify these exist, the 'Key Features' section vaguely mentions 'Reference material available in references/' without specifics, and the overall document structure is poorly organized with redundant sections that could be consolidated or removed entirely. | 2 / 3 |
Total | 7 / 12 Passed |