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evaluation

Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time.

Quality

64%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/evaluation/SKILL.md

The canonical home for this skill is evaluation in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

42%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 covers genuinely useful agent-evaluation concepts but is padded with duplicated sections and generic textbook explanation, and its code examples are non-executable pseudocode. Structure exists, yet progressive disclosure fails in practice: no bundle files ship, and the one referenced file is dangling.

Suggestions

Delete the duplicated 'When to Use This Skill', 'When to Use', second '## References', and 'Guidelines' sections, and remove the garbled opening tautology; keep one consolidated version of each.

Replace the pseudocode in Example 1 with a self-contained executable snippet (define the rubric dict, scoring, and weighting inline) and add validation checkpoints to the 8-step workflow (e.g., calibrate rubric agreement on a sample before scaling, establish a baseline before comparing versions).

Move the detailed methodology (rubric design, LLM-as-judge prompts, test set design) into a real references/ file such as references/metrics.md and link it clearly, or drop the dangling 'Metrics Reference' mention entirely.

DimensionReasoningScore

Conciseness

The body duplicates sections ('When to Use This Skill' vs 'When to Use', two consecutive '## References' headers, 'Practical Guidance' vs 'Guidelines'), opens with a garbled tautology ('Use this skill when working with build evaluation frameworks for agent systems'), and explains concepts Claude already knows (LLM-as-judge basics, human evaluation, weighted rubric scoring). This is 'noticeably verbose; several unnecessary explanations or padded sections' — not a 1 because the agent-specific material (non-determinism, end-state evaluation, degradation testing) is genuinely non-obvious.

2 / 5

Actionability

The 8-step framework sequence, rubric dimension list, and complexity stratification are concrete, but Example 1's code calls undefined functions (load_rubric(), assess_dimension(), weighted_average()) and is pseudocode rather than executable, matching 'pseudocode instead of executable code; missing key details'.

3 / 5

Workflow Clarity

'Building Evaluation Frameworks' provides a coherent 1–8 sequence, but there are no validation checkpoints or feedback loops (e.g., calibrating rubric agreement before scaling, verifying a baseline before comparing versions). Fits 'steps listed but validation gaps; checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

Sections are headed and organized, but ~200 lines of methodology are inlined monolithically with no bundle files, and the sole reference ('Metrics Reference - Detailed evaluation metrics and implementation') is plain text pointing to a file that does not exist in the bundle. Fits 'references present but not clearly signaled; content that should be separate is inline'.

3 / 5

Total

11

/

20

Passed

Description

70%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 with an explicit 'Use when...' trigger clause and clear domain focus. Its main weakness is a thin 'what' — one generic action verb — where listing concrete capabilities (rubrics, test sets, LLM-as-judge pipelines, regression tracking) would earn top marks.

DimensionReasoningScore

Specificity

Names the domain (agent system evaluation) and one action ('Build evaluation frameworks') with useful trigger activities (testing, validating, measuring), but does not enumerate concrete capabilities like rubric design, test set construction, or eval pipelines. Fits the anchor 'names domain and 1-2 concrete actions, but not comprehensive' — above a bare domain label (2) but short of several specific actions (4).

3 / 5

Completeness

Both parts are present: a 'what' ('Build evaluation frameworks for agent systems') and an explicit 'Use when...' clause with three concrete triggers. Not 5 because the 'what' is a single generic action rather than multiple concrete capabilities paired with the trigger phrases.

4 / 5

Trigger Term Quality

'testing agent performance systematically', 'validating context engineering choices', and 'measuring improvements over time' are phrases users would naturally say when needing this skill. Missing common variations such as 'benchmark', 'evals', or 'regression testing', so it falls short of the comprehensive-synonym anchor at 5.

4 / 5

Distinctiveness Conflict Risk

'Evaluation frameworks for agent systems' is a clear niche with distinct triggers, with only minor overlap risk against generic testing or benchmarking skills. Not 5 because 'testing agent performance' could also fire for non-agent test-automation requests.

4 / 5

Total

15

/

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/skills/evaluation/SKILL.md

Reviewed

Table of Contents

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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.