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anthropic-evaluations

This skill should be used when the user asks to "create evals", "evaluate an agent", "build evaluation suite", or mentions agent testing, graders, or benchmarks. Also suggest when building coding agents, conversational agents, or research agents that need quality assurance.

78

1.50x
Quality

73%

Does it follow best practices?

Impact

98%

1.50x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/toolkit/skills/anthropic-evaluations/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%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-disciplined index-style skill body: lean tables, a mandatory reference-reading directive, and exemplary one-level-deep progressive disclosure with all referenced files present. Its weaknesses are the absence of any inline workflow sequence (delegated entirely to roadmap.md) and body-level guidance that points but does not show concrete first steps.

Suggestions

Inline a brief quick-start sequence (a compressed version of roadmap steps 0-8, e.g., "1. Define tasks and success criteria → 2. Choose grader types → 3. Pick pass@k or pass^k → 4. Set up harness") so the body carries a minimal workflow skeleton.

Reduce duplication between the Grader Types quick table and grader-types.md by trimming the body table to the three type names with one-line 'best for' notes plus the pointer.

Add one concrete anchor from the templates in the body (e.g., a 3-line minimal task definition from coding-agent-eval.yaml) so the body demonstrates the target output format without opening a reference.

DimensionReasoningScore

Conciseness

The body is lean: dense tables instead of prose, a worked pass@k math example, a short YAML snippet, and no padding explaining concepts Claude already knows. It is not 5 because the Grader Types quick table and Capability/Regression table partially duplicate content in the referenced files and could be trimmed to pointers.

4 / 5

Actionability

Concrete elements exist (the pass@k/pass^k worked example, the tracked_metrics YAML, the imperative file-reading directive with labeled links), but the body itself contains no commands or steps for actually building an eval — all construction guidance is deferred to references. This matches anchor 3 (some concrete guidance but incomplete); it is not 4 because there is no executable code or command sequence in the body, and not 2 because the navigation and concept guidance is specific and concrete.

3 / 5

Workflow Clarity

No sequenced steps or checkpoints appear in the body; the workflow is delegated to roadmap.md ("Steps 0-8 for building evals from scratch"), which is clearly signaled but not summarized inline. Anchor 3 fits: the immediate action (read the references) is unambiguous and the workflow's location is clear, but the body has no sequence of its own. Not 4 because no steps are listed in the body at all; not 2 because the workflow is explicitly and correctly routed to a bundled, existing file rather than missing.

3 / 5

Progressive Disclosure

The body is a clean overview hub: a MUST directive to read references, eight one-level-deep reference links all verified to exist in references/, grouped under labeled sections (YAML Templates, Annotated Examples) each with a one-line description of contents. This matches anchor 5 (clear overview, well-signaled one-level-deep references, easy navigation) with no nesting or buried references.

5 / 5

Total

15

/

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 strong trigger-focused description with explicit, naturally phrased 'when' guidance and a clear niche. Its main weakness is that it never explicitly states what the skill itself does, leaving the capability side implied by trigger phrases.

Suggestions

Open with an explicit capability statement, e.g., "Builds evaluation suites for AI agents: designing grader types (code, model, human), selecting metrics (pass@k, pass^k), and assembling task templates."

Add natural trigger synonyms users might say, such as "evaluation", "assess agent quality", or "score agent responses", to broaden keyword coverage.

Tighten the broad "Also suggest when building coding agents..." clause to evaluation-specific contexts (e.g., "when building agents that need quality assurance or eval harnesses") to reduce overlap with general agent-building skills.

DimensionReasoningScore

Specificity

The description names the domain (agent evaluations) and trigger actions like "create evals" and "build evaluation suite", but never states the skill's own concrete capabilities (grader design, metrics, templates); anchor 3 fits "names domain and 1-2 concrete actions, but not comprehensive." It is not 4 because the skill's specific actions are absent, and not 2 because multiple concrete domain actions are named.

3 / 5

Completeness

The 'when' is explicit with quoted user phrases ("when the user asks to..."), but the 'what' is only implied through those triggers — the description never concretely states what the skill does. This matches anchor 4 (both present, one could be more explicit); it is not 5 because 'what' is never explicitly stated, and not 3 because the 'when' clause is fully explicit rather than weakly implied.

4 / 5

Trigger Term Quality

Good natural-phrase coverage: "create evals", "evaluate an agent", "build evaluation suite", "agent testing", "graders", "benchmarks", plus agent-type variants. It is not 5 because common synonyms such as "evaluation", "assess", or "score agent quality" are missing; not 3 because coverage goes well beyond a couple of generic keywords.

4 / 5

Distinctiveness Conflict Risk

Agent evaluation is a clear niche with distinct triggers (evals, graders, benchmarks), but "Also suggest when building coding agents, conversational agents, or research agents that need quality assurance" invites overlap with general agent-building skills. This is anchor 4 (mostly distinct, minor overlap risk); not 5 due to that broad proactive trigger, not 3 because the core triggers are unmistakably about evaluation.

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

allowed_tools_field

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

Warning

Total

15

/

16

Passed

Repository

dwmkerr/claude-toolkit

Path

plugins/toolkit/skills/anthropic-evaluations/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.