Content
78%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 workflow skill: executable fetch code, intent-based routing, concrete interpretation thresholds, and proper use of a one-level reference file for API details. The main gaps are the absence of an inline data-availability check in the main script and prose-only specification of the derived metrics.
Suggestions
Add a data-availability guard to the Step 2 script (check each DataFrame for None/empty before building tables) instead of leaving it only in references/api_reference.md, creating an explicit feedback loop for stocks with no analyst coverage.
Show the core derived metrics — range width as a % of consensus, revision ratio Up/(Up+Down), and YoY growth — as short code snippets rather than prose descriptions, so the analysis sections are fully executable.
Trim the opening paragraph that restates the frontmatter description, since it duplicates information already available to Claude at trigger time.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient — routing tables, concrete thresholds, and a compact fetch script — but the opening paragraph restates the frontmatter description, and the 'How to read it' bullets border on explanation Claude could derive. Not 5: these minor trims remain; not 3: there is no genuine padding. | 4 / 5 |
Actionability | The Step 2 fetch script is copy-paste ready and thresholds are specific (15% range, 5 analysts, 0.7/0.3 ratios), but derived metrics like range width % and the revision ratio are described in prose rather than code, and the empty-data guard lives only in the reference file. Clearly above the pseudocode/hint level of 3, short of 5's 'specific examples cover the common cases'. | 4 / 5 |
Workflow Clarity | Five clearly sequenced steps (install probe → fetch → route by intent → build analysis → synthesize) with an environment check up front. Minor validation gap: the body never instructs Claude to check for empty/None DataFrames before building tables — that check appears only in references/api_reference.md. No destructive or batch operations, so the cap of 3 does not apply; missing an explicit in-body feedback loop keeps it below 5. | 4 / 5 |
Progressive Disclosure | The workflow lives in the body while bulk API detail is correctly split into a single one-level-deep reference, clearly signaled in a dedicated 'Reference Files' section with its purpose ('exact return formats or edge case handling'); references/api_reference.md exists and matches that description. | 5 / 5 |
Total | 17 / 20 Passed |