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agent-evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

Quality

62%

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tessl review fix ./cli-tool/components/skills/ai-research/agent-evaluation/SKILL.md

The canonical home for this skill is agent-evaluation in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

33%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 reads as a metadata stub for a skill that was never filled in: section scaffolding and a well-formed Sharp Edges table exist, but the substantive guidance (patterns, anti-patterns, solutions) is either one line long or literally empty placeholder comments. An agent loading this skill would learn almost nothing actionable about how to actually test or benchmark an LLM agent.

Suggestions

Expand the three Patterns into concrete, executable guidance — e.g., a worked statistical-testing recipe (N runs, pass-rate threshold, how to detect flakiness via distribution spread) instead of 'Run tests multiple times and analyze result distributions'.

Replace the placeholder Sharp Edges solutions ('// Bridge benchmark and production evaluation') with actual remedies, and give each Anti-Pattern a one-line explanation of why it fails and what to do instead.

Add a sequenced evaluation workflow (define behavioral contract → build test suite → run repeated trials → analyze metrics → monitor in production) with a validation checkpoint, which is essential for this skill's own stated methodology.

DimensionReasoningScore

Conciseness

The body is short but spends tokens on persona narrative Claude does not need ("You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production"), and one sentence is literally truncated mid-thought ("the goal isn't 100% test pass rate—it"). Mostly efficient, but includes unnecessary framing that could be trimmed.

3 / 5

Actionability

Patterns are single-line abstractions with no method, tool, metric, or code ("Run tests multiple times and analyze result distributions"), the Anti-Patterns sections contain only headers ("❌ Single-Run Testing", "❌ Only Happy Path Tests", "❌ Output String Matching") with no explanation, and the Sharp Edges solutions are placeholder comments ("// Bridge benchmark and production evaluation", "// Handle flaky tests in LLM agent evaluation"). High-level hints exist, but the specific steps to execute anything are missing.

2 / 5

Workflow Clarity

There is no sequenced process anywhere in the body — no steps for designing tests, running repeated trials, analyzing distributions, or setting reliability thresholds, and no validation checkpoints despite the skill itself advocating "Statistical Test Evaluation" and regression testing. Steps are missing rather than merely gappy, so it matches the lowest anchor rather than anchor 2 (which requires a rough sequence).

1 / 5

Progressive Disclosure

The body is short (<60 lines) with clearly labeled sections (Capabilities, Requirements, Patterns, Anti-Patterns, Sharp Edges, Related Skills) and no nested references, so structure is good for a lean skill. It falls short of 5 because the Anti-Patterns sections are empty stubs and a methodology this deep would benefit from one-level-deep reference files; it is above 3 because what is present is well-organized and easy to navigate.

4 / 5

Total

10

/

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.

The description is solid: it names a clear niche, lists several concrete capability areas, and provides an explicit trigger list a user's phrasing would naturally match. Its main flaws are cosmetic and precision-related — a run-on sentence, an unsourced "less than 50%" statistic that functions as fluff, and a keyword-list trigger clause that omits natural variations.

Suggestions

Remove the editorializing statistic ("where even top agents achieve less than 50% on real-world benchmarks") — it pads the description without stating a capability, and fix the run-on by starting a clean sentence at "Use when".

Expand the trigger list with natural variations users would say, such as "evaluate agents", "agent benchmarks", "agent monitoring", or "assess agent reliability".

Add the concrete actions the body advertises but the description omits (regression testing, benchmark design) to move specificity toward comprehensive coverage.

DimensionReasoningScore

Specificity

The description lists several concrete actions — "behavioral testing, capability assessment, reliability metrics, and production monitoring" — matching anchor 4 (several specific actions, minor gaps). It is not 5 because the actions are somewhat generic gerunds, coverage the body itself advertises (regression testing, benchmark design) is absent, and the "less than 50% on real-world benchmarks" clause is a marketing hook rather than a capability.

4 / 5

Completeness

Both parts are present: a clear "what" ("Testing and benchmarking LLM agents including behavioral testing...") and an explicit "Use when:" clause with concrete trigger terms. It falls short of anchor 5 because the when is a bare comma-separated keyword list rather than natural trigger phrasing, the editorial statistic jammed mid-sentence muddies the what, and the missing punctuation between "benchmarks" and "Use when" leaves the sentence run together.

4 / 5

Trigger Term Quality

The "Use when" list covers natural phrases users would actually say: "agent testing, agent evaluation, benchmark agents, agent reliability, test agent". It is not 5 because common variations like "evaluate agents", "agent monitoring", "agent quality", or "LLM benchmark" are missing and "test agent" is an awkward fragment.

4 / 5

Distinctiveness Conflict Risk

The niche is clear (LLM agent testing/evaluation) with fairly distinct triggers, so overlap risk is minor — matching anchor 4. It is not 5 because terms like "test agent" and "agent reliability" could collide with general software-testing or multi-agent-orchestration skills.

4 / 5

Total

16

/

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

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

davila7/claude-code-templates

Path

cli-tool/components/skills/ai-research/agent-evaluation/SKILL.md

Reviewed

Table of Contents

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