Name: data-engineering-data-driven-feature
Rating: 52.8 (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

59%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/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

57%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 content is a well-sequenced, concrete orchestration workflow with specific tooling and config, but it repeats boilerplate across 16 phases, lacks explicit validation checkpoints for risky rollout operations, and references a non-existent bundle file.

Suggestions

Add explicit validation checkpoints with feedback loops between phases (e.g., 'Validate analytics tracking in staging; only proceed to rollout when data quality passes').

Fix or remove the dangling reference to resources/implementation-playbook.md (no such file exists in the bundle), or create it under references/.

Templatize the repeated per-phase 'Use Task tool / Context / Prompt / Output' boilerplate to reduce token overhead.

Dimension	Reasoning	Score
Conciseness	The body avoids explaining concepts Claude already knows and uses tight prompt/context/output triplets, but the near-identical 'Use Task tool with subagent_type=...' boilerplate repeated across 16 phases is tightening-able padding, matching anchor 3.	3 / 5
Actionability	It gives concrete subagent_type values, prompts naming specific tools (Amplitude, LaunchDarkly, Kafka, Snowflake, Datadog), and a concrete config YAML with thresholds, providing mostly executable guidance with minor gaps, fitting anchor 4.	4 / 5
Workflow Clarity	The 6-phase/16-step sequence is clearly ordered with context threading, but explicit validate->fix->retry checkpoints are absent for risky batch/deployment operations (gradual rollout, traffic allocation), so workflow clarity is capped at 3 per the destructive/batch guideline.	3 / 5
Progressive Disclosure	Phase headers give navigable structure, but all 16 steps are inlined in a single monolithic doc and the one referenced file (resources/implementation-playbook.md) does not exist in the bundle, fitting anchor 3.	3 / 5
	Total	13 / 20 Passed

Description

61%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 conveys a clear, fairly specific niche (data-driven feature development with A/B testing) but omits any explicit 'when to use' trigger guidance, capping completeness. It would benefit from adding concrete trigger phrases.

Suggestions

Add a 'Use when...' clause naming concrete trigger phrases (e.g., 'Use when designing features backed by A/B tests, experiment analysis, or rollout decisions').

Replace abstract actions ('analysis, implementation, and experimentation') with more concrete capabilities (e.g., 'design experiments, instrument analytics, run gradual rollouts').

Include natural synonyms users say ('experiment', 'feature flag', 'metrics', 'rollout') to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	The description names the domain ('Build features guided by data insights, A/B testing, and continuous measurement') plus a few actions ('analysis, implementation, and experimentation'), but the actions remain high-level and abstract rather than concrete capabilities, matching anchor 3.	3 / 5
Completeness	It clearly states what the skill does but contains no 'Use when...' clause or equivalent explicit trigger guidance, so per the judging guideline completeness is capped at 3 (anchor 3: clear 'what', missing 'when').	3 / 5
Trigger Term Quality	It includes natural terms a user would say ('data insights', 'A/B testing', 'continuous measurement', 'features') with good coverage, though common synonyms like 'experiment', 'feature flag', 'rollout', and 'metrics' are missing, fitting anchor 4.	4 / 5
Distinctiveness Conflict Risk	The data-driven experimentation niche is mostly distinct with minor overlap risk against general feature-development or A/B-testing skills, matching anchor 4 rather than 5 because 'build features' is still broad.	4 / 5
	Total	14 / 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/skills/data-engineering-data-driven-feature/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

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