Content
92%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.
A well-structured, highly actionable read-only data skill: sequenced steps, prerequisite validation, an error-recovery table, and an appropriately thin main file pointing to one real, one-level-deep reference. The only slack is minor redundancy in provenance statements and a host-specific probe note that could be trimmed or cut.
Suggestions
State the data provenance and daily-refresh cadence once (e.g., in the intro) and drop the repetition in Step 4's 'Mention that the dataset refreshes daily' and the Algolia/GitHub-Actions detail, which is already in the reference file.
Trim or generalize the opening host-probe note ('If your host has not already executed the shell probes below... (for example, in Codex)') to a single sentence about verifying curl/jq, removing environment-specific instructions from the main file.
The 'If jq is unavailable, you can still fetch raw JSON... and parse it inline with Python' aside duplicates the fallback path; either compress it to one clause or move it next to the install instructions as a single fallback note.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dominated by executable commands and lookup tables with little concept explanation, matching the 'efficient; minor instances of over-explanation that could be trimmed' anchor. It falls short of lean-per-token because the dataset provenance is repeated ('sourced from YC's Algolia search index and updated daily' in the intro, refreshed-daily again in Step 4), and the host-specific shell-probe note about 'if your host has not already executed the commands... (for example, in Codex)' is environment noise rather than task content. | 4 / 5 |
Actionability | Everything needed to execute is concrete and copy-paste ready: the base URL, complete curl|jq one-liners ('curl -s https://yc-oss.github.io/api/companies/top.json | jq .'), a routing table from user intent to endpoint, a jq filter cookbook, and discovery commands against meta.json. This matches the 'fully executable; specific examples cover the common cases' anchor, not the level below with minor gaps. | 5 / 5 |
Workflow Clarity | Five clearly sequenced steps with explicit validation (the prerequisite probe reporting CURL_OK/JQ_OK) and a genuine feedback loop in Step 5 (error → cause → fix table: 404 → check meta.json; empty array → broaden the search). As a read-only skill there are no destructive/batch operations, so the validation cap does not apply; this fits the 'explicit validation steps; feedback loops for error recovery' anchor better than level 4's 'minor validation gaps'. | 5 / 5 |
Progressive Disclosure | The SKILL.md body is a concise overview (routing table, base pattern, key rules) with a single, clearly signaled, one-level-deep reference — references/api_reference.md exists and contains the field schema, full endpoint list, and research workflow examples without nesting further files. The inline 'when to read it' guidance ('when you need the exact company field schema, valid batch/industry/tag names...') matches the 'clear overview with well-signaled one-level-deep references' anchor; it exceeds level 4 because there is no mislaid content or unclear signaling. | 5 / 5 |
Total | 19 / 20 Passed |