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agentic-development

Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)

48

Quality

61%

Does it follow best practices?

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tessl review fix ./skills/agentic-development/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

45%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 is a monolithic 850-line agent-development encyclopedia: broadly knowledgeable but padded with concepts Claude already knows, mixed-quality code (solid Pydantic AI examples alongside non-runnable pseudocode), and zero progressive disclosure — everything lives inline with no reference files. The Explore-Plan-Execute-Verify workflow and its verification loops are the strongest element, but conciseness and file structure need the most work.

Suggestions

Split framework-specific deep dives (Pydantic AI patterns, guardrails, testing, the Anthropic skills pattern) into one-level-deep reference files (e.g. references/pydantic-ai.md, references/guardrails.md) and keep SKILL.md as a concise overview with well-signaled links.

Cut or compress sections that restate general knowledge Claude already has (the agent architecture ASCII diagram, generic multi-agent pattern descriptions, thinking-trigger folklore) to reclaim token budget.

Make code examples executable or explicitly justify the pseudocode: define the invented helpers (createAgent, executeTool, llmCall) or replace the pseudo-APIs with real, runnable SDK calls, and move time-sensitive model/version tables into a clearly dated or deprecated section.

DimensionReasoningScore

Conciseness

The ~850-line body re-explains concepts Claude already knows (the three-component agent diagram, generic multi-agent/guardrail/memory patterns, "thinking triggers" like "think hard"/"ultrathink") and embeds time-sensitive model/version tables ("gpt-5-mini, o4-mini", "gemini-3", "claude-sonnet-4-6") with no deprecated/old-patterns framing. Several sections are padded or redundant with Claude's existing knowledge, matching anchor 2; it avoids anchor 1 because much of the bulk is code rather than pure prose padding.

2 / 5

Actionability

The Pydantic AI snippets are close to executable with real imports, but a large share of TypeScript examples are pseudocode against invented or undefined APIs ("createAgent({...})", "executeTool", "llmCall", "webSearchTool", "ResearchQuerySchema" are never defined), matching anchor 3 (pseudocode instead of executable code, missing key details). It is not 4 because many snippets will not run as written.

3 / 5

Workflow Clarity

The Explore-Plan-Execute-Verify pattern is a clearly sequenced four-phase workflow with an explicit verify step, a self-correct feedback loop, and failure escalation, matching anchor 4 (clear sequence with most checkpoints present). It falls short of anchor 5 because the checkpoints are abstract template code (e.g. "await verify(step, result)") rather than concrete validation commands.

4 / 5

Progressive Disclosure

There are no bundle files and no references at all; framework-specific deep dives (Pydantic AI patterns, guardrails, testing, the skills pattern) are all inlined into a single ~850-line file, matching anchor 2 (content that clearly belongs in separate files is inlined). It is not 3 because there are zero references to signal, and not 1 because section headers do provide real within-file navigation.

2 / 5

Total

11

/

20

Passed

Description

61%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 concise and names a concrete, distinctive niche (two specific agent frameworks by language), but it is a bare capability statement: a single action verb with no "use when" trigger guidance in the description field itself. Adding an explicit trigger clause and one or two more concrete actions would lift completeness and specificity.

Suggestions

Append a trigger clause to the description, e.g. "Use when building AI agents, tool-using LLM systems, multi-agent workflows, or agentic pipelines" (the frontmatter's separate when-to-use field does not count for description-field evaluation).

Enumerate one or two more concrete actions beyond "Build", such as "design tools with schemas and risk levels" or "add guardrails, memory, and evaluation tests", to improve specificity and completeness.

Include natural synonyms users would say ("agentic", "tool use", "multi-agent", "LLM agents") to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

"Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)" names the domain and one concrete action (build) with two specific frameworks, but offers no other concrete actions like tool design, testing, or guardrails. This matches anchor 3 (domain plus 1-2 concrete actions); it is not 4 because only a single verb is enumerated.

3 / 5

Completeness

The description clearly answers "what" (build agents with two named frameworks) but contains no "Use when..." clause or equivalent trigger guidance within the description field itself, which caps completeness at 3 per the rubric guideline. It is not 4 because the "when" is entirely absent rather than merely imprecise.

3 / 5

Trigger Term Quality

Terms like "AI agents", "Pydantic AI", "Claude SDK", "Python", and "Node.js" are natural phrases users would say when needing this skill, giving good keyword coverage. It falls short of anchor 5 because common variations such as "agentic workflows", "tool use", "multi-agent", or "LLM agents" are missing.

4 / 5

Distinctiveness Conflict Risk

Naming two specific frameworks paired with languages carves a mostly distinct niche that would not fire for unrelated skills. Minor overlap risk remains with other AI/agent development skills since "Build AI agents" is broad, keeping it at anchor 4 rather than 5.

4 / 5

Total

14

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (856 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

alinaqi/maggy

Path

skills/agentic-development/SKILL.md

Reviewed

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