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ito-data-atlas-agent

Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.

70

Quality

86%

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SecuritybySnyk

Low

Low-risk findings worth noting

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, concise architecture-planning skill with concrete lane definitions and a clear output contract. The only meaningful gap is workflow_clarity: the sequence lacks explicit validation/retry checkpoints between drafting and human handoff.

Suggestions

Add an explicit validation checkpoint in the workflow — e.g. after drafting the basket spec, verify data freshness/provenance before presenting it to the human editor.

Clarify the feedback loop when the human editor rejects or requests changes (how research is re-triggered and re-audited).

DimensionReasoningScore

Conciseness

Lean body with no padding and no explanation of concepts Claude already knows; each section (Guardrails, Architecture, Workflow, Skill Chains, Output Contract) earns its place.

3 / 3

Actionability

For an instruction-only architecture skill, guidance is concrete: four named lanes with specific responsibilities, a 5-step workflow, named skill chains, and an explicit output-contract field list.

3 / 3

Workflow Clarity

A clear numbered 5-step sequence exists, but validation checkpoints are only implicit (human approval); there is no explicit verify/retry gate between drafting parameters and handing off.

2 / 3

Progressive Disclosure

No bundle files exist, so all content lives in SKILL.md, but it is well-organized into clearly labeled sections with easy navigation — appropriate for a self-contained skill.

3 / 3

Total

11

/

12

Passed

Description

85%

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 with explicit what/when coverage and a clear niche boundary. The main gap is trigger-term breadth — the use clause names a purpose rather than the natural phrases a user would say.

Suggestions

Add concrete natural-language triggers a user would actually say, e.g. 'Use when designing a prediction-market research agent, planning a basket-discovery workflow, or drafting Itô parameters'.

Broaden trigger terms with common synonyms ('research agent', 'market discovery', 'basket design') to improve recall.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'basket research, market discovery, parameter drafting, and human-in-the-loop editing' — rather than vague language.

3 / 3

Completeness

Explicitly states what it does ('Design background Data Atlas style agents for...') and when to use it ('Use for architecture and workflow planning, not live order execution').

3 / 3

Trigger Term Quality

'Use for architecture and workflow planning' is a usable trigger, but natural user-facing keywords are thin and miss common variations like 'design a research agent' or 'prediction-market basket'.

2 / 3

Distinctiveness Conflict Risk

The Itô/Data-Atlas niche plus a clear negative boundary ('not live order execution') make it unlikely to trigger for the wrong skill.

3 / 3

Total

11

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
affaan-m/ECC
Reviewed

Table of Contents

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