alphaear-predictor

Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.

Quality

66%

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tessl review fix ./skills/alphaear-predictor/SKILL.md

Quality

Content

57%

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

The skill body is well-organized with real, one-level references and a sensible two-step workflow, but it is undermined by a non-executable code example, an inconsistent script path, and a duplicated heading.

Suggestions

Fix the example import path to 'scripts.kronos_predictor' and use the documented get_base_forecast(df, lookback, pred_len, news_text) call so the snippet is copy-paste executable.

Remove the duplicated '### 1. Forecast Market Trends' heading and the unused DatabaseManager import.

Add a brief validation/feedback checkpoint after the agentic adjustment step (e.g., verify the adjusted forecast has the same number of data points as the base forecast).

Dimension	Reasoning	Score
Conciseness	The body is mostly lean, but a duplicated '### 1. Forecast Market Trends' heading, an unused DatabaseManager import in the example, and minor redundancy mean it could be tightened.	2 / 3
Actionability	Concrete signatures and a code example are present, but the example is not executable: it imports from 'scripts.utils.kronos_predictor' (which does not exist; the real file is 'scripts/kronos_predictor.py') and calls predict() with a horizon argument that does not match the documented get_base_forecast(df, lookback, pred_len, news_text) signature.	2 / 3
Workflow Clarity	A two-step sequence (base forecast then adjustment) is listed, but there are no validation checkpoints and the duplicated heading muddies the structure.	2 / 3
Progressive Disclosure	The body is a clear overview with one-level-deep, well-signaled references to references/PROMPTS.md and scripts/kronos_predictor.py, both of which exist as real bundle files.	3 / 3
	Total	9 / 12 Passed

Description

75%

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

The description is well-structured with an explicit trigger clause and a clear niche, though its action list and trigger-term coverage could be more comprehensive. It is concise and distinct.

Suggestions

Expand the action list with more concrete verbs (e.g., 'generate, adjust, and backtest market forecasts') to lift specificity.

Add common trigger variations users might say, such as 'stock price prediction' or 'trading signals', to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	It names the domain and model ('Market prediction skill using Kronos', 'time-series forecasting', 'news-aware finance market adjustments') with a couple concrete actions, but the action list is not comprehensive enough for a 3.	2 / 3
Completeness	It states what the skill does ('Market prediction skill using Kronos') and provides an explicit 'Use when user needs...' trigger clause, satisfying both what and when.	3 / 3
Trigger Term Quality	Terms like 'market time-series forecasting', 'market prediction', and 'news-aware finance market adjustments' are relevant, but common user variations such as 'stock price prediction' or 'trading' are missing.	2 / 3
Distinctiveness Conflict Risk	The finance market forecasting niche tied to the Kronos model has distinct triggers and is unlikely to conflict with unrelated skills.	3 / 3
	Total	10 / 12 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: RKiding/Awesome-finance-skills
Path: skills/alphaear-predictor/SKILL.md
Commit: 853f09b

Reviewed: about 13 hours ago

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