stockbee-20pct-study

Build and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves.

Quality

86%

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Securityby

Low

Low-risk findings worth noting

Quality

Content

80%Weight 40%Scale 1-5

Reviews 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 skill body with copy-paste commands and clean progressive disclosure into reference files. The main gap is the absence of explicit validation/feedback checkpoints in the batch scan and backfill workflow steps.

Suggestions

Add an explicit validation checkpoint after Step 1 (scan), e.g. 'Confirm events_detected > 0 and review the skipped reasons in scan metadata before running enrich.'

Insert a verify step in Step 5 (backfill) before marking outcomes, e.g. check the survivorship-bias flag is present and spot-check a sample of records against the source OHLCV.

Add a short feedback loop to Step 3 (update-outcomes): re-run on the same state file after fixing any missing-bar flags to confirm outcome maturity counts increase.

Dimension	Reasoning	Score
Conciseness	Mostly efficient: the body is dominated by copy-paste commands and tight section prose, with only minor trims possible (e.g. the boundary sentence 'It does not generate buy/sell signals...' restates the methodology reference).	4 / 5
Actionability	Every workflow step ships a complete, copy-paste-ready CLI invocation with all flags (e.g. the scan command with --fmp-universe, --lookback-days, --min-abs-return-pct, --state-file), covering the common cases.	5 / 5
Workflow Clarity	Steps 1–5 are clearly sequenced with concrete commands, but scan/backfill are batch operations and the workflow lacks explicit validate→fix→retry checkpoints (e.g. confirm events detected before enriching, verify state integrity), which caps this dimension at 3.	3 / 5
Progressive Disclosure	SKILL.md is a concise overview that points one level deep to five real, well-signaled reference files (methodology, event_schema, catalyst_taxonomy, scoring_system, cohort_mining_rules) plus the script and asset templates, with a clear Resources section for navigation.	5 / 5
	Total	17 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that clearly states capabilities and gives explicit 'Use when...' trigger guidance with natural phrasing. Third-person imperative voice is used correctly throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns' — giving comprehensive coverage of what the skill does.	5 / 5
Completeness	Explicitly answers both 'what' (build/maintain the study via scanning, classifying, updating, summarizing) and 'when' ('Use when the user asks to run a daily 20% study, backfill... , find recurring edge patterns, or build a model book...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Captures natural phrases a user would say ('run a daily 20% study', 'backfill historical 20% movers', 'edge patterns', 'model book of explosive market moves') but omits some synonyms/variants; above the midpoint but not fully comprehensive.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche ('Stockbee-style daily 20% mover study for US equities') with distinct, specialized triggers and minimal overlap risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: tradermonty/claude-trading-skills
Path: skills/stockbee-20pct-study/SKILL.md
Commit: 51c7907

Reviewed: ~7 hours ago

Table of Contents

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