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training-observability

Inspect local or mounted machine-learning training runs through durable scalar metrics, rendered PNG plots, and saved visual predictions. Use when Codex needs to assess a live or completed run, compare metric trends, inspect prediction overlays, or diagnose training quality without modifying the run or relying on a live Visdom dashboard.

75

Quality

93%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

93%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.

A tight, actionable, read-only observation skill with executable commands and clean sectioning. The only gap is the absence of an explicit error-recovery feedback loop, though that is less critical for a non-destructive observational workflow.

Suggestions

Add an explicit validation checkpoint in the inspect sequence (e.g., 'If reader warnings appear in step 1, resolve or acknowledge them before interpreting metrics') to turn implicit checks into a clear feedback loop.

Show one example of interpreting a metric trend from raw points (e.g., a sample `metrics` output and the step range/value phrasing expected in the report) to make the evidence-reporting guidance concrete.

DimensionReasoningScore

Conciseness

Lean imperative prose with no padding or explanation of concepts Claude already knows; every line (command flags, evidence-reporting rules, control-boundary limits) earns its place, matching the lean-and-efficient anchor.

5 / 5

Actionability

Provides copy-paste-ready shell commands with real flags for each subcommand (summary, metrics, render, artifacts) covering the common cases, satisfying the fully-executable anchor.

5 / 5

Workflow Clarity

Clear numbered 4-step inspect sequence with implicit checkpoints ("reader warnings" in step 1, "inspect... metadata before judging" in step 4), but no explicit validate-then-fix feedback loop; the sequence is clear yet checkpoints are implicit rather than enumerated, fitting just below a 5.

4 / 5

Progressive Disclosure

Under 50 lines with no need for external references and three well-organized sections (Inspect a run, Report evidence, Preserve control boundaries), satisfying the simple-skill exception for a top score on organization alone.

5 / 5

Total

19

/

20

Passed

Description

92%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.

A strong, third-person description that concretely states what the skill does and when to use it with domain-specific triggers. Minor room for improvement in trigger-term naturalness, but overall highly specific and distinctive.

DimensionReasoningScore

Specificity

Lists multiple concrete inspection modalities ("durable scalar metrics, rendered PNG plots, and saved visual predictions") plus four distinct use-case actions (assess, compare trends, inspect overlays, diagnose), giving comprehensive coverage rather than the minor gaps of a 4.

5 / 5

Completeness

Explicitly states both what ("Inspect... training runs through... metrics... plots... predictions") and when ("Use when Codex needs to assess a live or completed run, compare metric trends..."), matching the anchor for clearly answering both with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keywords for the domain ("training runs", "metric trends", "prediction overlays", "training quality", "Visdom dashboard"), but jargon like "durable scalar metrics" and the absence of common synonyms/file extensions keep it just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (machine-learning training observability) with distinct triggers (training runs, Visdom, prediction overlays) and explicit non-goals ("without modifying the run or relying on a live Visdom dashboard"), minimizing overlap with other skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
jolibrain/deepdetect
Reviewed

Table of Contents

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