Content
86%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.
The body is a well-structured, highly actionable workflow with excellent progressive disclosure and mostly lean token usage. Its only weaknesses are minor: some redundancy with the frontmatter description in the intro, and a lack of explicit validation steps for failed or empty data fetches.
Suggestions
Trim the redundant opening paragraph ("Generates a post-earnings analysis using Yahoo Finance data via yfinance. Covers the actual vs estimated numbers, surprise magnitude, stock price reaction, and financial context — a complete picture of what happened.") since it repeats the frontmatter description almost verbatim.
Add an explicit validation checkpoint for failed or empty data fetches — e.g., what to do if the ticker is invalid or get_earnings_dates() returns None — mirroring the existing YFINANCE_NOT_INSTALLED check style.
Tighten the host-shell-probe preamble in the introduction to a single sentence, keeping only the instruction to run the ! wrappers and report unavailable shell access.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Efficient overall — API-specific knowledge (column names, index semantics, timezone handling, reaction-window logic) is non-obvious and earns its tokens, and bulk API detail is delegated to the reference file. Minor trims possible: the opening paragraph "Generates a post-earnings analysis ... a complete picture of what happened" restates the frontmatter description, and the host-shell-probe preamble is wordy — fitting the score-4 'minor instances of over-explanation' anchor rather than the lean score-5 anchor. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready Python throughout: the install snippet, the data-gathering script with exact method names, and the complete earnings_reaction function with real pandas logic, plus explicit handling of the pending-reaction case. Placeholders like ticker = yf.Ticker("AAPL") # replace with actual ticker are parameterization with clear instruction, not pseudocode, covering the common cases per the score-5 anchor. | 5 / 5 |
Workflow Clarity | Five clearly sequenced steps (ensure library, gather data, find report and measure reaction, build recap, respond) with several explicit checkpoints: the YFINANCE_NOT_INSTALLED branch, the "If reaction_pct is None ... say the regular-session reaction is still pending" handling, and filtering None prior moves. It stays at 4 rather than 5 because validation of failed or empty fetches is missing (e.g., invalid ticker or get_earnings_dates() returning None); the destructive/batch cap does not apply since this is read-only analysis. | 4 / 5 |
Progressive Disclosure | Clean split: the workflow and key code stay inline while bulk API detail lives in one clearly-signaled, one-level-deep reference — "references/api_reference.md — Detailed yfinance API reference ..." (verified to exist and match) — with explicit guidance on when to read it ("when you need exact method signatures or to handle edge cases"). This matches the score-5 anchor of a clear overview with well-signaled one-level-deep references. | 5 / 5 |
Total | 18 / 20 Passed |