CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

mle-workflow

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Quality

79%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.agents/skills/mle-workflow/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

62%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 has an excellent sequenced workflow with real validation checkpoints and runnable code, but it is noticeably overlong and buries that value under textbook ML guidance and two large ecosystem-mapping tables that should live in reference files. Splitting reference material out and cutting the standard-knowledge prose would raise every dimension at once.

Suggestions

Cut or compress the 'Metric and Mistake Economics' and 'Data and Feature Hypotheses' sections to one-line reminders; the precision/recall/F1 tradeoffs and missing-value/outlier handling guidance restate knowledge Claude already has.

Move the 'Reuse the SWE Surface' table and the 'Ten MLE Task Simulations' table into reference files (e.g., references/swe-surface.md and references/task-simulations.md) and link them from the body, keeping SKILL.md as a lean overview.

Trim the duplicated checklist material: the Review Checklist, Anti-Patterns, and per-step bullet lists repeat the same requirements stated in the Core Workflow sections.

DimensionReasoningScore

Conciseness

The 347-line body restates knowledge Claude already has ("Favor precision when the cost of an incorrect positive decision dominates", "Use F1 only when the precision/recall tradeoff is genuinely balanced") and pads with a 20-row SWE-surface table and 10-row simulation table tied to an external 'ECC' skill ecosystem. Not 3 because there are several such sections, not just isolated over-explanations; not 1 because the text is dense rather than tutorial-style filler.

2 / 5

Actionability

Two complete, executable Python blocks (frozen TrainingConfig with artifact_name hashing; assert_promotion_ready with fail-closed gate checks) plus concrete Iteration Compact and Observation Ledger templates. Not 5 because much of the surrounding guidance is directive prose without code or commands (e.g., the serving and monitoring sections); not 3 because what is present is copy-paste ready.

4 / 5

Workflow Clarity

The Core Workflow is six clearly numbered phases with explicit validation: automated promotion gates that raise on failure, a six-step Error Analysis Loop as a feedback loop, a 12-item Review Checklist, and rollback requirements per deployment. This matches the anchor for clear sequence plus explicit validation, feedback loops, and checklists; neighboring anchors lack one of those three elements.

5 / 5

Progressive Disclosure

Sections are well organized, but the entire skill is inlined in one 347-line file with no reference files at all, and the large SWE-surface and task-simulation tables are exactly the content that belongs in separate reference files. Not 4 because nothing is split out and navigation relies on scrolling one long document; not 2 because section structure is clear and nothing is buried.

3 / 5

Total

14

/

20

Passed

Description

88%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 strong: it names six concrete lifecycle capabilities and pairs them with an explicit, naturally worded trigger clause. Its only weakness is moderate synonym coverage in the trigger phrasing, which could catch a few more ways users ask for production ML work.

DimensionReasoningScore

Specificity

"data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback" lists six concrete capabilities covering the full production ML lifecycle, matching the comprehensive-coverage anchor. Not 4 because no meaningful capability gaps remain in the description's scope.

5 / 5

Completeness

Explicitly answers what ("Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback") and when ("Use when building, reviewing, or hardening ML systems beyond one-off notebooks") with concrete trigger phrases. Not 4 because both halves are explicit, specific, and back-to-back.

5 / 5

Trigger Term Quality

"Use when building, reviewing, or hardening ML systems beyond one-off notebooks" gives good natural phrasing, but common user synonyms like "train a model", "model deployment", or "productionize" are absent. Not 3 because the present terms are natural and varied; not 5 because synonym coverage is incomplete.

4 / 5

Distinctiveness Conflict Risk

"Production machine-learning engineering workflow" carves a clear niche distinct from general data-analysis or DevOps skills, but "building, reviewing, or hardening" overlaps with generic code-review and deployment skill triggers. Not 5 due to that minor overlap risk; not 3 because the ML-production framing is unmistakable.

4 / 5

Total

18

/

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

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository

affaan-m/ECC

Path

.agents/skills/mle-workflow/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.