Name: data-engineering-data-driven-feature
Rating: 48 (1 reviews)
Author: administrakt0r

data-engineering-data-driven-feature

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

Quality

51%

Does it follow best practices?

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/data-engineering-data-driven-feature/SKILL.md

The canonical home for this skill is data-engineering-data-driven-feature in administrakt0r/AI-Agents-Safe-Coding-Skills

Quality

Content

52%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.

The body is a well-sequenced, highly actionable orchestration workflow with real validation phases and feedback loops, but it is padded with templated filler and inlines a large prompt catalogue that should be split into reference files. Its only file reference is a dead path, which weakens navigation.

Suggestions

Move the 16 detailed step prompts into a reference file (e.g., references/implementation-playbook.md) and keep SKILL.md as a concise phase overview with clearly signaled one-level-deep links.

Remove the bracketed extended-thinking paragraph and the generic 'Clarify goals... Apply relevant best practices...' instructions that restate what Claude already knows.

Fix or remove the broken 'resources/implementation-playbook.md' reference so navigation points to a file that actually exists in the bundle.

Dimension	Reasoning	Score
Conciseness	Noticeably verbose: the bracketed extended-thinking paragraph and the templated 'Use this skill when'/'Do not use this skill when'/'Instructions' sections add padding that Claude does not need, even though the step prompts themselves are substantive.	2 / 5
Actionability	Each of the 16 steps gives a concrete Task-tool invocation with a full, ready-to-use prompt and expected output, which is mostly executable guidance with only minor gaps from reliance on external subagent types.	4 / 5
Workflow Clarity	Six numbered phases with explicit sequencing and context threading, plus a dedicated Pre-Launch Validation phase, automated-rollback feedback loops, and a Success Criteria checklist provide a clear sequence with most checkpoints present.	4 / 5
Progressive Disclosure	The 16-step prompt catalogue is fully inlined when it clearly belongs in a separate reference file, and the sole reference ('resources/implementation-playbook.md') points to a file that does not exist in any bundle directory.	2 / 5
	Total	12 / 20 Passed

Description

50%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.

The description gives a clear high-level 'what' tied to data-driven feature development and experimentation, but it omits any explicit 'when to use' trigger guidance and relies on methodology-level language rather than concrete, distinguishable actions. It reads as competent but generic and would benefit from explicit trigger phrases.

Suggestions

Add an explicit 'Use when...' clause naming concrete trigger situations (e.g., 'Use when planning a feature that needs A/B testing, metric instrumentation, or rollout validation').

Replace methodology abstractions with concrete, distinguishable actions (e.g., 'design A/B experiments, instrument analytics events, configure gradual rollouts') to raise specificity and distinctiveness.

Include natural trigger synonyms users actually say ('A/B test', 'experiment', 'feature flag', 'instrument metrics') to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Names the domain ('Build features guided by data insights, A/B testing, and continuous measurement') and a few concrete elements, but these are methodology categories rather than a comprehensive list of discrete actions.	3 / 5
Completeness	Clearly states what the skill does but contains no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.	3 / 5
Trigger Term Quality	Includes some relevant natural terms like 'A/B testing' and 'data insights' but is missing common variations or synonyms users might say (e.g., 'experimentation platform', 'feature flags', 'metrics').	3 / 5
Distinctiveness Conflict Risk	The data-driven experimentation niche is somewhat specific, but 'Build features' is broad and could still overlap with general feature-development or analytics skills.	3 / 5
	Total	12 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/data-engineering-data-driven-feature/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

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