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stockbee-episodic-pivot-analyzer

Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.

76

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

87%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, actionable skill body with copy-paste commands, real reference files, and clear sequencing. The main gap is the absence of an explicit verification/feedback checkpoint for a batch operation, which limits workflow clarity.

Suggestions

Add an explicit verification checkpoint after running the analyzer — e.g., confirm the output JSON parses and each candidate has required fields (state, catalyst score, risk) — with a fix-and-rerun loop before any handoff.

For batch runs, document how to detect and handle per-candidate failures (missing catalyst, no price data) so a single bad input does not silently drop a candidate.

Note required input sanity checks up front (e.g., events JSON must include event_date and catalyst_type) to gate Step 2 and make the review checkpoint in Step 3 a true validation step.

DimensionReasoningScore

Conciseness

Lean, instructional body with no re-explanation of concepts Claude already knows; tokens go to commands, JSON schemas, output fields, and decision rules rather than background, matching the 'every token earns its place' anchor.

3 / 3

Actionability

Provides copy-paste-ready bash invocations with real flags ('python3 .../analyze_ep.py --events-json ... --prices-json ... --output-dir reports/'), example catalyst JSON, concrete output fields, and state-based handoff rules.

3 / 3

Workflow Clarity

Four clearly sequenced steps (Prepare Inputs, Run Analyzer, Review Output, Handoff Rules) are present, but for a batch candidate-screening operation there is no explicit validate/verify-and-retry feedback loop, which the rubric caps at 2 rather than 3.

2 / 3

Progressive Disclosure

The body is an overview that signals one-level-deep references to references/ep_methodology.md, references/catalyst_quality.md, and references/handoff_rules.md, all verified to exist as real files, alongside a script and tests directory.

3 / 3

Total

11

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12

Passed

Description

100%

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, third-person description that states concrete capabilities, lists natural trigger terms in an explicit 'Use when' clause, and occupies a distinctive niche. It is information-dense rather than padded, though the two long enumerations push toward the verbose end of acceptable.

DimensionReasoningScore

Specificity

Names concrete actions and specific scoring factors: 'Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals...' and 'Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.'

3 / 3

Completeness

Clearly answers both halves: what it does (analyze and score EP candidates by catalyst quality plus price/volume) and when to use it via an explicit 'Use when' clause, so it is not the level below where 'when' is only implied.

3 / 3

Trigger Term Quality

The explicit 'Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring' covers natural phrasings a target user would say.

3 / 3

Distinctiveness Conflict Risk

A narrow Stockbee/EP/PEAD niche with specialized, domain-specific triggers makes it unlikely to fire for unrelated skills; well above level 2's 'could still overlap.'

3 / 3

Total

12

/

12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
tradermonty/claude-trading-skills
Reviewed

Table of Contents

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