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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

66%

Does it follow best practices?

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tessl review fix ./skills/data-engineering-data-driven-feature/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 presents a well-sequenced, genuinely actionable multi-agent workflow with concrete prompts and expected outputs per step. Its weaknesses are padding (meta-commentary, tautological use/don't-use sections, generic instructions), a dangling reference to a nonexistent playbook file, and the absence of explicit validation-failure/rollback recovery loops.

Suggestions

Remove the bracketed '[Extended thinking: ...]' paragraph, the tautological 'Use this skill when'/'Do not use this skill when' sections, and the generic Instructions section — none add information Claude does not already have.

Fix the broken reference: either create 'references/implementation-playbook.md' with the detailed examples it promises, or delete the pointer entirely since the prompt templates already carry the substance.

Add explicit validation feedback loops, e.g. what to do when the Phase 4 analytics validation fails or which alert thresholds trigger the Phase 12 automated rollback, to move the workflow from sequenced to fully checkpointed.

DimensionReasoningScore

Conciseness

The 16 agent prompts are content-dense, but the body includes noticeable padding: the bracketed '[Extended thinking: ...]' meta-commentary paragraph, tautological 'Use this skill when / Do not use' sections ('The task is unrelated to data-driven feature development'), a generic Instructions section ('Clarify goals, constraints, and required inputs'), and a stray trailing '$ARGUMENTS' line. This fits anchor 3 ('mostly efficient but includes some unnecessary explanation') rather than anchor 4's 'minor instances of over-explanation'.

3 / 5

Actionability

Each of the 16 steps specifies an exact subagent_type, a complete copy-paste-ready prompt, and expected outputs, plus a concrete configuration YAML block — concrete, executable guidance for an agent-orchestration skill. It falls short of anchor 5 because there are no worked examples of inputs/outputs and success criteria like 'error rates below 0.1%' are stated without how to measure them.

4 / 5

Workflow Clarity

The six phases with 16 numbered steps form a clear sequence, context handoffs between steps are explicit, and Phase 4 is a dedicated pre-launch validation stage with automated rollback during rollout. It is not anchor 5 because explicit error-recovery loops (what to do when validation fails, alert thresholds for rollback) are missing or only implied.

4 / 5

Progressive Disclosure

The body is well-sectioned, but the sole external reference — 'open resources/implementation-playbook.md' — points to a file that does not exist in the bundle (no references/, scripts/, or assets/ directories are present), and even the path uses 'resources/' rather than a real directory. This fits anchor 3 ('references present but not clearly/signally organized; content that should be separate is inline'): all detail lives inline with one broken pointer.

3 / 5

Total

14

/

20

Passed

Description

53%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 clearly states what the skill does within a distinct niche, but it lacks any explicit 'when to use' trigger guidance and misses several natural trigger-term synonyms. It reads as moderately specific but generic in places ('specialized agents for analysis, implementation, and experimentation' is implementation detail rather than capability).

Suggestions

Add an explicit 'Use when...' clause with concrete triggers, e.g. 'Use when building a feature that should be validated with data, running A/B tests, or instrumenting features with analytics'.

Include natural user phrases and synonyms such as 'analytics', 'experiment', 'metrics', 'hypothesis testing', and 'feature rollout' so the skill triggers on how users actually phrase these requests.

Replace the vague 'specialized agents for analysis, implementation, and experimentation' with concrete capabilities (e.g. 'designs experiments, defines success metrics, instruments event tracking, and analyzes test results').

DimensionReasoningScore

Specificity

The description names the domain ("Build features guided by data insights, A/B testing, and continuous measurement") with 1-2 concrete actions, but does not list several specific capabilities. It fits anchor 3 ('names domain and 1-2 concrete actions') better than anchor 4, which expects a list of multiple specific actions with only minor gaps.

3 / 5

Completeness

The 'what' is reasonably clear (build data-driven features with A/B testing and measurement), but there is no 'Use when...' clause or equivalent explicit trigger guidance, capping completeness at 3 per the judging guidelines. It is not score 2 because the 'what' is clear, not vague.

3 / 5

Trigger Term Quality

Relevant keywords like "data insights", "A/B testing", "features", and "experimentation" are present, but common natural variations users would say are missing ("analytics", "metrics", "experiment", "hypothesis", "feature flags"). This matches anchor 3 ('some relevant keywords but missing common variations or synonyms') rather than anchor 4's 'good keyword coverage'.

3 / 5

Distinctiveness Conflict Risk

The niche (data-driven feature development with experimentation) is mostly distinct with minor overlap risk against closely related skills like general feature-development or analytics skills. It is not score 5 because 'Build features...' could still partially match general feature requests, and not score 3 because the data/experimentation qualifiers carve out a specific domain.

4 / 5

Total

13

/

20

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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

skills/data-engineering-data-driven-feature/SKILL.md

Reviewed

Table of Contents

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