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candlestick

Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.

62

Quality

78%

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tessl review fix ./agent/src/skills/candlestick/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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 concise, well-organized reference whose pattern tables and scoring rules are efficient and clear. Its main gaps are executability — no pandas code or quantitative detection formulas despite the description promising a vectorized implementation — and an internal consistency problem: the double-candle table lists 6 patterns under a "(5)" header, and the 15-pattern total does not reconcile with what is actually documented (5 + 6 + 4 + 1 = 16, with the trend-confirmation pattern unnamed).

Suggestions

Add an executable pandas snippet showing the vectorized body/shadow computation and at least one pattern's detection expression, so the promised implementation is concrete rather than implied.

Fix the count inconsistencies: the Double-Candle table lists 6 patterns under a "(5)" header, and the "1 trend confirmation" pattern is never named or defined — reconcile these against the stated total of 15.

Tighten the qualitative detection criteria (e.g., define "small body" and "long shadow" numerically in terms of body_pct and shadow_ratio) so pattern rules are unambiguous, and drop the unused `requests` dependency.

DimensionReasoningScore

Conciseness

The body is lean and efficient: tables carry the pattern definitions, the signal logic is two sentences, and parameters are a compact table — every token earns its place with no padding or explanation of concepts Claude already knows. Not 4 because there are no trimmed-able over-explanations at all; the only blemish (unused `requests` dependency) is an accuracy issue, not verbosity.

5 / 5

Actionability

The signal logic ("Bullish patterns score +1, bearish patterns score -1..."), parameters (body_pct 0.1, shadow_ratio 2.0), and signal convention (1/-1/0) are concrete, but the skill promises a "pure pandas vectorized implementation" while providing no code, no formulas for body/shadow computation, and detection criteria that are qualitative prose ("long lower shadow", "small body") rather than executable rules. It sits between pseudocode-level guidance and executable guidance — key implementation details are missing, matching anchor 3 rather than 4.

3 / 5

Workflow Clarity

As a single-purpose compute task, the sequence — detect patterns, sum +1/-1 scores, emit 1/-1/0 — is clear and unambiguous, satisfying the simple-skill exception. Not 5 because the 15th pattern ("1 trend confirmation") is never named or defined, leaving one input to the scoring pipeline unspecified, and the qualitative detection criteria leave ambiguity a validation checkpoint would otherwise catch.

4 / 5

Progressive Disclosure

This is a short (~54 lines), single-purpose skill with no external references needed; the sections (Purpose, Signal Logic, Parameters, Dependencies, Signal Convention) are well-organized and appropriately scoped for a self-contained SKILL.md, which the rubric's simple-skill guideline scores at 5.

5 / 5

Total

17

/

20

Passed

Description

70%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 specific, distinctive description that clearly states what the skill builds and how its output is derived, using appropriately concrete technical vocabulary. Its main weakness is the complete absence of when-to-use guidance, which caps completeness and leaves trigger behavior implicit.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user mentions candlestick patterns, bullish/bearish setups, or wants a pandas-based trading signal from price action."

Include common synonyms users actually say — "technical analysis", "OHLC data", "price action", "trading strategy" — to broaden natural keyword coverage.

Convert part of the noun-phrase stack into explicit actions (e.g. "Detects 15 candlestick patterns in OHLC data and generates a composite long/short signal") to sharpen the capability statement.

DimensionReasoningScore

Specificity

The description lists concrete, specific capabilities — "pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation)" and "generating a composite signal from bullish/bearish pattern scores" — but reads as a stack of noun phrases rather than multiple explicit actions, leaving minor coverage gaps. Not 5 because it does not enumerate several distinct actions (recognition and signal generation are the only two), and not 3 because the implementation detail and output semantics go well beyond naming a domain with 1-2 generic actions.

4 / 5

Completeness

The "what" is clearly and concretely answered (pattern recognition engine with enumerated pattern counts and composite signal output), but there is no "Use when..." clause or any equivalent explicit trigger guidance — the "when" is entirely absent, capping completeness at 3 per the rubric guideline.

3 / 5

Trigger Term Quality

Natural user terms like "candlestick patterns", "bullish/bearish", "pandas", and "signal" are present and well chosen. Not 5 because common synonyms users say are missing — "technical analysis", "price action", "OHLC", "trading strategy" — so coverage is good but not comprehensive.

4 / 5

Distinctiveness Conflict Risk

"Candlestick pattern recognition engine" carves out a clear niche with distinct trigger terms ("candlestick", "bullish/bearish pattern scores") that no generic document- or code-processing skill would match, so conflict risk is minimal. Not 4 because it does not merely overlap with closely related skills — the candlestick-specific vocabulary is unambiguous.

5 / 5

Total

16

/

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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