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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-crafted knowledge-retrieval skill with strong actionability and workflow clarity. Each use case has clear, numbered steps with explicit guardrails around attribution, verbatim quoting, and honesty about gaps. The main weaknesses are moderate repetition across the six help sections (the same 'read outline.md → read transcript.md → quote verbatim' pattern appears in nearly every section) and the inability to verify the referenced bundle files (outline.md, transcript.md, quotes.md).

Suggestions

Consolidate the repeated 'read outline.md → read transcript.md → quote verbatim → cite line ranges' pattern into a single 'Standard lookup procedure' section referenced by the individual workflows, reducing token cost.

Ensure bundle files (outline.md, transcript.md, quotes.md) are included in the bundle so the progressive disclosure structure is complete and verifiable.

DimensionReasoningScore

Conciseness

The content is moderately efficient — it provides structured, non-trivial instructions for handling a specific talk's content. However, there's some repetition across the six 'How to help' sections (e.g., the 'read outline.md then transcript.md' pattern is repeated in nearly every section), and some instructions could be consolidated. The grounding rules are tight, but the help sections are somewhat verbose.

2 / 3

Actionability

Each section provides concrete, step-by-step instructions with specific actions (read outline.md, locate section, quote verbatim, cite line ranges, check quotes.md first). The guidance is precise about what to do, what not to do, and how to handle edge cases (garbled terms, missing information, strained connections). The referenced resources (gh.io/sk, gh.io/scg, gh.io/taskflows) are specific and named.

3 / 3

Workflow Clarity

Each workflow section has a clear numbered sequence with explicit validation checkpoints — e.g., 'If the answer isn't in the transcript, say so explicitly', 'If the framework genuinely doesn't fit, say so', 'Any parts you add beyond Joseph's prescription, mark clearly'. The audit workflow includes a systematic walk-through of all five areas with clear verdicts. Error handling and boundary conditions are well-addressed throughout.

3 / 3

Progressive Disclosure

The skill references outline.md, transcript.md, and quotes.md as supporting files, which is good progressive disclosure in principle. However, no bundle files were provided, so we can't verify these exist. The SKILL.md itself is fairly long (~100 lines of instruction) with six detailed sections that share significant structural overlap — some consolidation or a summary table pointing to detailed sections could improve navigation.

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 skill description that clearly defines when it should be triggered and covers a comprehensive set of specific topics from the talk. The explicit 'Use when' clause, combined with highly specific trigger terms including the speaker's name, talk title, and unique concepts, makes it both complete and distinctive. The only minor note is that it could slightly benefit from a brief 'what it does' statement before the trigger clause (e.g., 'Provides information about...'), but the current format is effective.

DimensionReasoningScore

Specificity

The description lists numerous specific concrete topics and actions: writing safer code, MCP servers, skills, agentic workflows, dual-LLM/LLM-jury, AI-assisted fuzzing, supply chain decisions, hallucinations and non-determinism in security review, and specific resources with URLs.

3 / 3

Completeness

The description explicitly opens with 'Use when the user asks about...' providing a clear trigger clause, and the extensive list of topics thoroughly covers what the skill handles. Both 'what' and 'when' are clearly addressed.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms a user might say: the speaker's name, the talk title, specific concepts like 'shift left', 'start left', 'AI-assisted fuzzing', 'hallucinations', 'MCP servers', 'GitHub Security Lab', and specific URLs (gh.io/scg, gh.io/sk, gh.io/taskflows). These are highly natural keywords someone familiar with the talk would use.

3 / 3

Distinctiveness Conflict Risk

This is extremely niche — tied to a specific person's specific talk with unique concepts like 'the 1-to-100 security-to-developer gap', 'dual-LLM / LLM-jury', and specific GitHub Security Lab URLs. It is highly 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

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