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elliott-wave

Elliott Wave Theory signal engine. Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals. Pure in-house pandas implementation.

52

Quality

66%

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SecuritybySnyk

Passed

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tessl review fix ./agent/src/skills/elliott-wave/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%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 compact, readable overview of the engine's rules and signals, but it functions as a theory summary rather than an operational skill: it has no runnable code or invocation path, and it duplicates (rather than links to) the more detailed reference files sitting unused in the bundle. Restructuring it to point at the references and include a minimal executable example would raise most dimensions at once.

Suggestions

Replace the inline 'Core Rules' and 'Fibonacci Relationships' sections with one-line pointers to references/波浪结构.md and references/Fibonacci浪间关系.md, keeping only the engine's chosen ratios (the last table of the Fibonacci reference) in the body.

Add a minimal executable example — a short pandas snippet or CLI call showing how the engine is invoked on OHLC data and what the returned signal series looks like.

State the operational workflow explicitly (load candles → detect swings → match/validate waves → emit signal) so the pipeline order and the interpretation step are unambiguous.

DimensionReasoningScore

Conciseness

The body re-teaches textbook Elliott Wave material Claude already knows — the impulse/corrective table, the 'Three Iron Rules', and the standard Fibonacci ratios — and this material is duplicated almost verbatim in references/波浪结构.md and references/Fibonacci浪间关系.md, so those tokens are spent twice. It is not 4 because the duplicated theory sections are more than 'minor instances of over-explanation'; it is not 2 because the parameter table, signal logic, and signal convention are genuinely engine-specific and earn their place.

3 / 5

Actionability

Concrete guidance is minimal: the only executable content is 'pip install pandas numpy requests'; there is no code example, function signature, CLI invocation, or script path showing how to actually run the signal engine. This matches 'minimal concrete guidance; high-level hints but missing the specific steps to execute' rather than 3, because there is no pseudocode or partial code either — only descriptions.

2 / 5

Workflow Clarity

The pipeline is implied (Zigzag swing detection → structure matching → Fibonacci validation → signal) and the signal mapping is stated unambiguously ('5-wave advance completed → sell'), but the operational sequence for a user (prepare data → call engine → interpret signal convention) has no explicit steps or validation checkpoints. It is not 2 because signal conditions and parameters are concretely defined; it is not 4 because there is no explicit run/validate sequence at all, only an implicit one.

3 / 5

Progressive Disclosure

The bundle contains references/波浪结构.md and references/Fibonacci浪间关系.md, but the body never links to or mentions either file — instead it inlines condensed copies of their content (iron rules, Fib ratios) — so the references are orphaned and the duplicated content sits in SKILL.md where the rubric says it should be split out. This matches anchor 3 ('references present but not clearly signaled; content that should be separate is inline') exactly; it is not 2 because the body itself is short and well-sectioned, and not 4 because the existing bundle files are completely unnavigable from the body.

3 / 5

Total

11

/

20

Passed

Description

75%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, third-person description that concretely enumerates the engine's capabilities in a distinct niche. Its one real weakness is the total absence of any 'when to use' trigger guidance, which caps completeness and leaves users to infer applicability on their own.

Suggestions

Add an explicit trigger clause, e.g. 'Use when analyzing market swing structure, counting Elliott waves, or checking Fibonacci retracement/extension relationships in OHLC price data.'

Include a few natural synonyms users might say — 'wave count', 'impulse/corrective pattern', 'technical analysis' — to broaden trigger coverage.

Optionally name the expected input format (e.g., OHLCV candle data / pandas DataFrame) so the 'what' also states what it operates on.

DimensionReasoningScore

Specificity

Lists four concrete, distinct capabilities in third person ('Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals'), which comprehensively covers the engine's action set. It does not fall to 4 because there is no meaningful gap in coverage for this domain; the actions are specific and complete rather than having 'minor gaps'.

5 / 5

Completeness

The 'what' is explicit and clear (detect swing points, match wave structures, validate with Fibonacci, generate signals), but there is no 'Use when...' clause or any equivalent trigger guidance for when to invoke the skill. Per the rubric guideline, a missing 'Use when...' clause caps completeness at 3; it is not 2 because the 'what' half is concrete and multi-part, not vague.

3 / 5

Trigger Term Quality

Good natural keyword coverage: 'Elliott Wave Theory', '5-wave impulse', '3-wave corrective', 'Fibonacci', 'swing points', 'trend top', 'correction' are terms a user asking for wave analysis would say. It is not 5 because common variations and synonyms are missing (e.g., 'wave count', 'wave analysis', 'impulse/corrective pattern', 'EW', 'technical analysis'); it is clearly above 3 since far more than 'some' relevant keywords are present.

4 / 5

Distinctiveness Conflict Risk

Elliott Wave counting with Zigzag swing detection and Fibonacci validation is a clear niche with distinct triggers; only a near-duplicate Elliott Wave or wave-counting skill would compete. It is not 4 because the specialized domain ('Elliott Wave Theory', specific wave structures) leaves essentially no minor-overlap risk with general TA skills.

5 / 5

Total

17

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
HKUDS/Vibe-Trading
Reviewed

Table of Contents

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