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machine-learning-ops-ml-pipeline

Design and implement a complete ML pipeline for: $ARGUMENTS

Quality

54%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/machine-learning-ops-ml-pipeline/SKILL.md

The canonical home for this skill is machine-learning-ops-ml-pipeline in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

38%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 skill lays out a well-sequenced four-phase multi-agent orchestration with specific tooling per phase, but it is padded with aspirational criteria and restated role lists, contains no executable artifacts, lacks inter-phase validation gates, and its only progressive-disclosure reference is a dangling path. The core orchestration skeleton is sound; the surrounding material needs trimming and the referenced playbook needs to actually exist.

Suggestions

Fix the dangling reference: either create resources/implementation-playbook.md with the detailed examples or remove the pointer, and move the per-phase Task prompts into referenced files so SKILL.md stays a lean overview.

Cut the 'Thinking' role list, 'Success Criteria', and 'Final Deliverables' sections or compress them to a few lines — they restate the phase structure or state aspirational numbers Claude gains no instruction from.

Add explicit validation checkpoints between phases (e.g. 'Review Phase 1 deliverables and confirm quality gates before dispatching Phase 2 agents') to turn the sequence into a gated workflow.

DimensionReasoningScore

Conciseness

Several padded sections add little: the 'Thinking' prose ('The multi-agent approach ensures each aspect is handled by domain experts' plus a role list restating the phases), the aspirational 'Success Criteria' with invented metrics ('99.9% uptime', '< 0.1% data quality issues'), and 'Final Deliverables' which restates the phases. At ~310 lines, much of the tool-name listing (MLflow, Feast, KServe, Prometheus, etc.) is MLOps knowledge Claude already has. Not 3 because multiple entire sections, not just occasional sentences, could be cut.

2 / 5

Actionability

Each Task prompt lists concrete deliverables and specific tools ('MLflow/Weights & Biases integration', 'Terraform modules for cloud resources', 'KServe/Seldon Core'), but nothing is executable: no code, commands, or examples appear, and the '<Task>' blocks with '{phase1.data-engineer.output}' interpolation use an undefined mechanism. Not 4 because there are no copy-paste-ready artifacts and the actual dispatch/output-passing mechanics are unspecified.

3 / 5

Workflow Clarity

Phases 1-4 are clearly sequenced with explicit inter-agent handoffs ('Using data architecture from: {phase1.data-engineer.output}'), but there are no validation checkpoints between phases — no 'verify Phase N output before proceeding' gates. The Success Criteria are aspirational end-state metrics, not in-workflow checkpoints, matching 'sequence present but checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

All ~310 lines are inlined in SKILL.md with content (per-phase prompts, criteria) that clearly belongs in separate files, and the only external reference — 'open resources/implementation-playbook.md' — points to a file that does not exist anywhere in the bundle (no references/, scripts/, assets/, or resources/ directories are present). A broken sole reference plus monolithic inlining matches the anchor for 'content that clearly belongs in separate files is inlined'.

2 / 5

Total

10

/

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 is a bare template: it states a clear domain and two generic actions but omits any 'when to use' guidance, capability enumeration, or synonym coverage. It would benefit most from an explicit trigger clause and a concrete list of pipeline capabilities.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks to build, productionize, or end-to-end orchestrate an ML/MLOps pipeline (data ingestion, training, deployment, monitoring)'.

Enumerate 3-4 concrete capabilities (data pipeline design, feature engineering, model training with experiment tracking, serving/monitoring setup) instead of the generic 'design and implement'.

Include natural synonyms such as MLOps, machine learning pipeline, model deployment, and model serving to improve trigger-term coverage.

DimensionReasoningScore

Specificity

"Design and implement a complete ML pipeline" names the domain and exactly two actions (design, implement), matching the 'names domain and 1-2 concrete actions' anchor. It is not 4 because no specific capabilities are enumerated (data ingestion, training, deployment, monitoring), and not 2 because two actions and a clear domain are explicitly stated.

3 / 5

Completeness

The 'what' is clear ("Design and implement a complete ML pipeline") but there is no 'Use when...' clause or equivalent trigger guidance anywhere in the description, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

"ML pipeline" is a phrase users naturally say, but common variations and synonyms (MLOps, machine learning, model training, model deployment) are absent, matching 'some relevant keywords but missing common variations or synonyms'.

3 / 5

Distinctiveness Conflict Risk

"Complete ML pipeline" carves out a niche, but the phrasing is broad enough to overlap with generic ML development or data-science skills, matching 'somewhat specific but could still overlap with similar skills'. Not 4 because no distinct trigger phrases distinguish it from neighboring ML skills.

3 / 5

Total

12

/

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

plugins/AI-Agents-Safe-Coding-Skills-claude/skills/machine-learning-ops-ml-pipeline/SKILL.md

Reviewed

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