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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

43%

Does it follow best practices?

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

Quality

Content

11%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 hollow shell: generic boilerplate sections, an abstract instruction list, and a phase heading with no steps, pointing exclusively at 16 sub-skill files and a playbook that do not exist in the bundle. A reader following any reference finds nothing, and nothing in the body itself is actionable. The only structural virtue is that it is short and points one level deep.

Suggestions

Add real executable substance to the Instructions: concrete first steps, specific commands or code patterns for instrumentation/experiment setup, and explicit validation checkpoints (e.g. verify analytics events fire before rollout, check experiment power calculations before launch).

Fix the workflow: either describe the full phase sequence (Phase 1 through N) with ordered steps and validation gates, or drop the phase framing entirely — currently 'Phase 1' contains no steps and implies missing phases.

Repair progressive disclosure: ship the referenced ./sub-skills/*.md and resources/implementation-playbook.md files (or remove the dead links), and give each link a one-line description of what it covers so navigation is possible.

Cut the filler: delete the bracketed 'Extended thinking' meta-paragraph and the tautological use/don't-use sections, which consume tokens without adding guidance.

DimensionReasoningScore

Conciseness

The ~100-word bracketed "[Extended thinking: This workflow orchestrates a comprehensive data-driven development process...]" meta-paragraph, the tautological "Use this skill when / Do not use this skill when" sections ("The task is unrelated to data-driven feature development"), and filler bullets like "Apply relevant best practices and validate outcomes" are padded sections adding no information — matching 'Noticeably verbose; several unnecessary explanations or padded sections'. Not 1 because it never explains concepts Claude already knows; not 3 because the majority of the body is filler rather than an occasional stray sentence.

2 / 5

Actionability

The Instructions are entirely abstract — "Clarify goals, constraints, and required inputs", "Apply relevant best practices and validate outcomes", "Provide actionable steps and verification" describe rather than instruct, with no concrete steps, commands, or examples — matching 'Entirely vague or abstract; no concrete code or commands'. The single concrete pointer ("open resources/implementation-playbook.md") targets a file that does not exist in the bundle, so it provides no executable guidance.

1 / 5

Workflow Clarity

"## Phase 1: Data Analysis and Hypothesis Formation" is a header with no steps under it — the phase contains only an unrelated list of 16 sub-skill links, no Phase 2+ exists despite the numbering implying more, and the sole validation mention ("Apply relevant best practices and validate outcomes") is generic. This matches 'Steps missing or incoherent; no sequence; no validation for risky operations'.

1 / 5

Progressive Disclosure

Scored against the actual bundle: the bundle contains only SKILL.md — neither ./sub-skills/ (16 linked files) nor resources/implementation-playbook.md exist, so every reference is broken. The textual structure is one level deep and the body is a thin pointer file (the right shape), but the links go nowhere, are bare numbered titles with no description of content, and sit under a mislabeled phase header — matching 'Minimal structure; ... or references are buried'. Not 1 because the intended navigation is flat and legible rather than deeply nested; not 3 because broken paths mean the references cannot actually be followed.

2 / 5

Total

6

/

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 communicates a clear niche (data-driven, experiment-validated feature development) with a few relevant keywords, but it completely lacks any 'when to use' trigger guidance, which caps completeness. It reads as a mid-tier description: distinct enough to avoid most conflicts, yet neither comprehensive in actions nor rich in natural trigger terms.

Suggestions

Add an explicit 'Use when...' clause with concrete triggers, e.g. 'Use when building features that need A/B tests, analytics instrumentation, experiment design, or post-launch metric analysis.'

Broaden natural trigger terms to include synonyms users actually say: 'experiments', 'analytics', 'metrics', 'hypothesis testing', 'feature rollout'.

Enumerate one or two more concrete capabilities (e.g. 'design statistical experiments' or 'instrument analytics events') to lift specificity toward comprehensive coverage.

DimensionReasoningScore

Specificity

Phrases like "Build features guided by data insights", "A/B testing", and "continuous measurement" name the domain plus 2-3 semi-concrete activities, matching the anchor 'Names domain and 1-2 concrete actions, but not comprehensive'. It falls short of 4 because these are process categories rather than specific user-recognizable operations, and short of comprehensive coverage (no mention of instrumentation, rollout, or metrics analysis).

3 / 5

Completeness

The 'what' is reasonably clear ("Build features guided by data insights, A/B testing, and continuous measurement using specialized agents..."), but there is no 'Use when...' clause or equivalent trigger guidance anywhere — the rubric explicitly caps this at 3 ('Has a clear what but when is missing or only weakly implied'). It is not 2 because the 'what' is not vague, and cannot be 4 without any 'when' element.

3 / 5

Trigger Term Quality

"A/B testing", "data insights", and "continuous measurement" are phrases users would naturally say, but common variations like "experiment", "analytics", "metrics", "hypothesis", or "instrumentation" are absent, matching 'Some relevant keywords but missing common variations or synonyms'. Not 4 because the natural-term coverage is thin for a domain with many everyday synonyms.

3 / 5

Distinctiveness Conflict Risk

The combination "data insights, A/B testing, and continuous measurement" carves out a recognizable experimentation-driven niche, but the opening "Build features" is broad enough to overlap with general feature-development and agent-orchestration skills, matching 'Somewhat specific but could still overlap with similar skills'. Not 4 because generic feature-build requests would plausibly trigger it.

3 / 5

Total

12

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 16 missing

Warning

Total

14

/

16

Passed

Repository

Dokhacgiakhoa/Agent-Skills-4-Vibe-Coding-CLI

Path

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

Reviewed

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