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ainativedev/aidevcon-2026-ldn

AI Native DevCon 2026 London — all conference sessions as interactive skills

71

Quality

89%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Overview
Quality
Evals
Security
Files

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured, highly actionable skill that provides clear workflows for multiple task types grounded in specific bundle files. Its main strengths are the concrete step-by-step procedures, explicit handling of edge cases (garbled transcription, unreliable speaker attribution, out-of-scope questions), and clear validation checkpoints. Minor weaknesses include some redundancy across task sections and the inability to verify bundle file references since no bundle files were provided.

Suggestions

Consider consolidating repeated references to the standard lookup procedure across task sections to reduce redundancy — each task section could simply say 'Follow the standard lookup procedure, then...' rather than partially restating lookup steps.

DimensionReasoningScore

Conciseness

The content is reasonably efficient but includes some redundancy — the standard lookup procedure is repeated conceptually across multiple task sections (e.g., 'Apply the speaker's approach' and 'Factual Q&A' both reference the same lookup steps). Some phrasing could be tightened, but it generally respects Claude's intelligence and doesn't over-explain basic concepts.

2 / 3

Actionability

Each task section provides concrete, step-by-step procedures with specific file references (outline.md, transcript.md, quotes.md), exact lookup sequences, clear output expectations (verbatim quotes with line ranges, bracketed annotations for garbled text, explicit verdicts per dimension), and specific artifact structures (Actor, Preconditions, Main success scenario, etc.). The guidance is highly specific and directly executable.

3 / 3

Workflow Clarity

Multi-step processes are clearly sequenced with numbered steps across all task types. The standard lookup procedure has a clear pipeline with validation (step 5: explicitly say when content isn't found; step 6: handle unreliable attribution; step 7: handle garbled text). The audit workflow has an ordered checklist with per-dimension verdicts. The artifact drafting workflow includes a marking step for content not from the talk, serving as a verification checkpoint.

3 / 3

Progressive Disclosure

The skill references bundle files (outline.md, transcript.md, quotes.md) and explains their roles, which is good structure. However, no bundle files were provided, making it impossible to verify the references work. The SKILL.md itself is moderately long (~100 lines of substantive content) with some sections that could potentially be split out, but the organization with clear headers and a shared lookup procedure is reasonable for this complexity level.

2 / 3

Total

10

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong, well-crafted description that clearly defines what the skill does (answer questions, explain concepts, retrieve insights, draft artifacts, audit setups) and when to use it (with an extensive explicit trigger list). The description is highly distinctive due to its grounding in a specific speaker's talk and named methodology, and it includes rich natural trigger terms covering the talk's key topics.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'answers questions about', 'explains concepts from', 'retrieves verbatim insights', 'drafting artifacts per his methodology', and 'auditing setups against his AI Unified Process'. These are concrete, distinguishable capabilities.

3 / 3

Completeness

Clearly answers both 'what' (answers questions, explains concepts, retrieves insights, drafts artifacts, audits setups) and 'when' with an explicit 'Use when...' clause listing numerous specific trigger scenarios.

3 / 3

Trigger Term Quality

Excellent coverage of natural trigger terms users would say: 'Simon Martinelli's talk', 'AI Unified Process', 'system use cases as specs', 'user stories', 'self-contained systems vs microservices', 'MCP servers', 'guardrails', 'AI-assisted ERP modernization', 'drift management', 'spec-driven approach'. These are specific phrases a user would naturally use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive — anchored to a specific person's talk ('Simon Martinelli'), a named methodology ('AI Unified Process'), and very specific domain concepts ('spec-driven development', 'self-contained systems vs microservices'). Extremely unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

81%

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

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

Total

9

/

11

Passed

Reviewed

Table of Contents