Content
81%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An unusually strong operational skill: the multi-step workflow is clearly sequenced with genuine validation feedback loops, and hard-won operational knowledge (costs, gotchas, measured prevalence) fills every section. The gaps are minor: a few editorial sentences could be trimmed, the Python extraction is a labeled sketch rather than complete code, and the workflow fan-out design notes are not backed by a bundled script or schema.
Suggestions
Trim the editorial aphorisms (e.g. "Ten spec-verified sources beat 200 guesses", "Small endpoint, large unlock") to pure instruction, and frame the dated spec-URL table as periodically refreshed rather than a fixed "worked on 2026-07-26" list.
Provide the long-tail workflow as a bundled script or a concrete output-schema skeleton in scripts/ or references/ instead of prose design notes, and complete the Python extraction sketch ("Sketch") into a runnable script that handles the one- and two-segment nesting cases it describes.
Ship the referenced scripts/dump_source_inventory.py (currently missing from the bundle, making the link dangling) and consider moving the theme-statistics list and vendor spec table into a one-level-deep reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense and competence-assuming throughout (no explanation of OpenAPI, GraphQL introspection, or basic tooling), but includes editorial aphorisms ("Ten spec-verified sources beat 200 guesses", "Small endpoint, large unlock", "A gap only matters in proportion to who hits it") and a dated spec table ("Specs that worked on 2026-07-26") that is time-sensitive without a refresh/deprecated framing. Minor trims possible; no padding. | 4 / 5 |
Actionability | Mostly copy-paste-ready: the inventory dump command, the SQL adoption-ranking query, the curl bulk URL check, and the TODO grep are all executable, but the Python extraction is explicitly labeled a "Sketch" and the long-tail workflow — the core of the method — is conveyed as design notes rather than a runnable script or output schema. | 4 / 5 |
Workflow Clarity | A clear six-step sequence (dump inventory → rank by adoption → diff against spec → judge gaps → find cross-source patterns → record) with explicit validation checkpoints and feedback loops throughout: strip stdout warnings, "Bulk-check the cited doc_urls", "Spot-check a handful of findings end to end", "Make 'could not verify' an explicit, blessed answer", and "Confirm column names against system.information_schema.columns first". The batch fan-out includes output validation, so the batch-operation cap does not apply. | 5 / 5 |
Progressive Disclosure | Well-sectioned single file with clear headers and a Related-skills map, but the bundle contains no references/, scripts/, or assets/ directories, so the one cited bundle path (scripts/dump_source_inventory.py) is dangling, and long inline material (the 8-vendor spec table, the theme statistics) could live in reference files. | 4 / 5 |
Total | 17 / 20 Passed |