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weekly-performance-digest

Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.

64

Quality

76%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/weekly-performance-digest/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

85%

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 a concrete runnable command, clear sequenced workflow, and clean separation of detail into real reference and script bundle files. The only minor weakness is the length of the inline JSON output example.

DimensionReasoningScore

Conciseness

The body is lean and domain-specific without explaining concepts Claude already knows, but the inline JSON output schema block is somewhat lengthy; it is mostly efficient yet could be tightened, fitting the level-2 anchor rather than the fully lean level-3.

2 / 3

Actionability

Provides a complete executable command with script path, flags, documented defaults, and named output files — copy-paste ready, matching the fully-executable anchor.

3 / 3

Workflow Clarity

A clearly sequenced Step 1/2/3 workflow with an explicit edge-case note ('An empty week still produces a valid report with exit code 0'); the operation is read-only report generation, so the destructive/batch validation cap does not apply.

3 / 3

Progressive Disclosure

SKILL.md is an overview with well-signaled, one-level-deep references to real bundle files (scripts/generate_weekly_digest.py and references/weekly-digest-metrics.md), and metric formulas are appropriately split into the reference file rather than inlined.

3 / 3

Total

11

/

12

Passed

Description

67%

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, well-scoped description that concretely enumerates the computed metrics and breakdown dimensions. Its main gap is the missing explicit 'Use when…' trigger clause, which leaves the 'when to use' guidance only implied.

Suggestions

Add an explicit 'Use when…' trigger clause naming the situations a user would voice (e.g., 'Use when reviewing a trading week's results, or when asked for win rate, expectancy, or a weekly P&L summary').

Soften or gloss the jargon (MAE/MFE, R-multiple) alongside plain-language equivalents so the description surfaces terms a user would naturally say.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and metrics — 'win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism' — matching the multi-action anchor.

3 / 3

Completeness

Clearly states what the skill does, but 'when' is only implied from 'weekly' with no 'Use when…' clause, which per the guidelines caps completeness at 2.

2 / 3

Trigger Term Quality

Contains relevant natural terms ('weekly performance summary', 'win rate', 'expectancy') but mixes in jargon (MAE/MFE, R-multiple) and lacks explicit trigger phrasing; not the level-3 broad coverage of plain user vocabulary, and not level-1 because core terms are present.

2 / 3

Distinctiveness Conflict Risk

Tightly scoped to 'closed trader-memory-core theses' with domain-specific metrics, giving a clear niche unlikely to trigger for unrelated skills.

3 / 3

Total

10

/

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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