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evaluation

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

61

Quality

77%

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tessl review fix ./skills/evaluation/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 well-organized instructional body with genuinely actionable specifics (thresholds, sample sizes, stratification, a 9-step sequence) and a clearly signaled one-level-deep reference file. The main costs are cross-section redundancy that inflates token use and a 627-line scripts/evaluator.py in the bundle that SKILL.md never points to.

Suggestions

Collapse the redundancy between Core Concepts, Detailed Topics, Guidelines, and Gotchas — the multi-dimensional-rubric point is stated four times; state it once authoritatively and let Guidelines reference rather than restate.

Reference scripts/evaluator.py from the body with a 'Read when' pointer (as done for metrics.md) so the largest bundle artifact is discoverable, and either link or remove the placeholder 'External resources' entries.

Make the code examples executable by either defining or importing the helper functions they call (load_rubric, assess_dimension, run_validate_repo), or explicitly justify them as illustrative scaffolds.

DimensionReasoningScore

Conciseness

The body is information-dense and directive rather than padded with concepts Claude already knows, but it repeats its own points across sections: "multi-dimensional rubrics instead of single scores" appears in Core Concepts, Detailed Topics, Guidelines #1, and Gotcha #5, and the 8-item Guidelines section largely recapitulates earlier sections. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' — below 4 because the Core Concepts / Detailed Topics / Guidelines overlap is a systematic redundancy, not just minor trimming.

3 / 5

Actionability

Concrete, specific guidance throughout: numeric thresholds (0.7 general / 0.9 high-stakes pass, 0.85/0.70 alert lines), sample sizes (20-30 early, 50+ mature), a 9-step build sequence, a 4-level complexity stratification, and four worked examples in Python and YAML. It stops short of 5 because the code examples call undefined helpers (load_rubric, assess_dimension, run_validate_repo) — scaffolding rather than copy-paste executable — fitting 'mostly executable guidance; concrete code or commands with minor gaps'.

4 / 5

Workflow Clarity

The "Building Evaluation Frameworks" sequence gives a clearly ordered 9 steps with an explicit checkpoint ('Establish baseline metrics before making changes so improvements can be measured against a known reference') and a validation-separation step (deterministic failures before LLM scores). It is not a 5 because the sequence itself lacks explicit error-recovery feedback loops (what to do when a baseline regresses or a stratum underperforms), matching 'clear sequence with most checkpoints present; minor validation gaps'.

4 / 5

Progressive Disclosure

Good structure with well-signaled, one-level-deep references — the body links "[Metrics Reference](./references/metrics.md) - Read when: designing specific evaluation metrics..." and that file exists and matches its described content. Against the actual bundle structure, though, scripts/evaluator.py (627 lines) is never referenced from SKILL.md, leaving a substantial bundle artifact undiscoverable, and the 'External resources' entries are unlinked placeholders — minor organization gaps that hold this below a 5.

4 / 5

Total

15

/

20

Passed

Description

75%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 solid description that clearly names the domain, enumerates seven concrete capability areas, and includes an explicit 'should be used when' trigger clause. The main gap versus top anchor examples is trigger breadth: the when-clause is a single condition and lacks the synonym and phrasing variations users naturally say.

DimensionReasoningScore

Specificity

The description lists several concrete capability areas — "deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement" — which matches the anchor 'lists several specific actions; minor gaps in coverage'. It falls short of a 5 because these are domain nouns rather than fully concrete actions (compare the anchor-5 example's verbs like 'extract', 'fill', 'merge'), and it stays above a 3 because coverage spans far more than 1-2 actions.

4 / 5

Completeness

It explicitly answers both questions: what ("building agent evaluation systems: deterministic checks, ... outcome measurement for agent pipelines") and when ("This skill should be used when building agent evaluation systems"). The when clause is a single generic trigger condition rather than the multiple concrete trigger phrases of the anchor-5 example ("Use when working with PDF files or when the user mentions PDFs, forms..."), so it sits at 4 — noticeably above the midpoint but not a clean 5.

4 / 5

Trigger Term Quality

Natural terms users would say are present — "agent evaluation", "regression suites", "rubrics", "quality gates", "production monitoring", "baseline comparison" — giving good keyword coverage per the score-4 anchor. It misses common synonyms and variations users actually type ("evals", "testing", "benchmarks", "LLM-as-judge" adjacent phrasing), which keeps it below the comprehensive synonym-plus-extension coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

The evaluation niche is distinct — "deterministic checks, regression suites, multi-dimensional rubrics, quality gates" for agent pipelines is unlikely to trigger for unrelated skills. Minor overlap risk remains with closely adjacent skills (testing/regression skills generally, and the sibling advanced-evaluation/context-optimization skills named in the body), and the description itself carries no disambiguating exclusion, matching 'mostly distinct; minor overlap risk' at 4 rather than the clear-niche-minimal-conflict of a 5.

4 / 5

Total

16

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
muratcankoylan/Agent-Skills-for-Context-Engineering
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.