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.
A well-structured operational skill: executable commands for every input mode, a clear four-step workflow with per-state handoff rules, and proper offloading of methodology detail to real, clearly-signaled reference files. The remaining gaps are minor — some duplicated catalyst enumeration and no explicit error/verification checkpoint after running the analyzer.
Suggestions
Remove the duplicated catalyst enumeration in 'When to Use' (it repeats the description and the Mode A example) and keep a single pointer to references/catalyst_quality.md for the taxonomy.
Add a brief verification checkpoint after Step 2, e.g. confirm the report JSON was written and fields are populated before scoring candidates in Step 3, plus one line on what to do when the analyzer errors or returns no candidates.
State what the analyzer requires when no offline OHLCV or FMP key is available (which fields degrade) so Step 3 review does not depend on silently missing enrichment data.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient and assumes domain knowledge (no explanation of what an EP or OHLCV is), with every section carrying operational content. Minor trimmable redundancy remains: the catalyst taxonomy is enumerated in 'When to Use' ('The user provides earnings, guidance, M&A, FDA, analyst, contract, product, short-squeeze, or theme/news events') and again in the Mode A example, duplicating the description. Fits 'Efficient; minor instances of over-explanation that could be trimmed' rather than the 5 anchor's 'every token earns its place'. | 4 / 5 |
Actionability | Quotes: three complete copy-paste bash invocations of 'scripts/analyze_ep.py' covering all input modes (--events-json/--prices-json, --earnings-json, --momentum-json), the FMP enrichment command with 'export FMP_API_KEY=your_key', a concrete JSON input example, named output files with timestamps, and an explicit state taxonomy for review. Fully executable and covering the common cases — matches the top anchor; not 4 because there are no gaps in the guidance needed to run the skill. | 5 / 5 |
Workflow Clarity | Quotes: a clear four-step sequence 'Step 1: Prepare Candidate Inputs' → 'Step 2: Run the Analyzer' → 'Step 3: Review the Output' → 'Step 4: Handoff Rules', with per-state decision rules ('DELAYED_EP_WATCH: Do not chase Day 1; monitor for a controlled pullback'). However, checkpoints are implicit — there is no explicit step to verify the analyzer ran successfully or to handle a failed/empty report — matching 'Clear sequence with most checkpoints present; minor validation gaps' rather than the 5 anchor's explicit validate/fix/retry loop. This is a read-only analysis skill, so the destructive/batch cap does not apply. | 4 / 5 |
Progressive Disclosure | The body is a lean overview and the Resources section clearly signals each one-level-deep bundle file with its purpose: 'references/ep_methodology.md — Stockbee EP interpretation and setup taxonomy', 'references/catalyst_quality.md — catalyst classification and quality scoring', 'references/handoff_rules.md — downstream workflow handoffs and review rules'. All three referenced files exist in the bundle alongside scripts/analyze_ep.py and its tests, and no detail content is wrongly inlined. Matches 'Clear overview with well-signaled one-level-deep references; content appropriately split'. | 5 / 5 |
Total | 18 / 20 Passed |