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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?

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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 skill for a talk-reference use case. Its greatest strengths are highly actionable workflows with clear sequencing, explicit validation steps (say when info isn't available, mark non-talk additions), and strong grounding rules that prevent hallucination. The main weakness is moderate repetition across workflow sections — the 'read outline.md → read transcript.md → quote verbatim' pattern appears in nearly every section and could be factored into a shared preamble to save tokens.

Suggestions

Factor the repeated 'read outline.md → find section → read transcript.md → quote verbatim' pattern into a single shared procedure referenced by each workflow section, reducing token repetition across the six use-case blocks.

Provide the bundle files (outline.md, transcript.md, quotes.md) or at minimum a file listing so the progressive disclosure references can be verified and the skill is self-contained.

DimensionReasoningScore

Conciseness

The content is reasonably well-structured but includes some repetitive instructions (e.g., the 'read outline.md then transcript.md' pattern is repeated nearly identically across every section). The grounding rules and multiple workflow sections could be tightened. However, most content is genuinely instructive rather than explaining things Claude already knows.

2 / 3

Actionability

The skill provides highly concrete, step-by-step guidance for each use case: specific file paths to check (outline.md, transcript.md, quotes.md), exact ordering of operations, specific folder names to match (inputs/, sources/, wiki/), explicit labeling requirements for added content, and clear rules about quoting vs paraphrasing. Each workflow section is a concrete playbook.

3 / 3

Workflow Clarity

Each workflow section has a clear numbered sequence with explicit validation checkpoints — e.g., 'check quotes.md first before searching transcript.md', 'if the framework doesn't fit, say so', 'mark anything added beyond Emma's prescription', 'if the answer isn't in the transcript, say so'. The grounding rules establish clear guardrails, and the audit workflow explicitly requires walking through every component in order with verdicts.

3 / 3

Progressive Disclosure

The skill references supporting files (outline.md, transcript.md, quotes.md) appropriately and signals when to use each. However, no bundle files are provided, making it impossible to verify these references resolve correctly. The SKILL.md itself is moderately long with six workflow sections that could potentially be split, but the inline length is reasonable for the complexity of the task.

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 well-crafted description that excels across all dimensions. It opens with an explicit 'Use when' clause, lists highly specific concepts and named entities that serve as natural trigger terms, and is so narrowly scoped to a particular talk and speaker that conflict risk is negligible. The only minor note is that it doesn't describe what the skill *does* (e.g., 'Answers questions about...') — it only specifies when to use it — but the 'Use when the user asks about' framing implicitly covers the 'what'.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete concepts: four components (live ingestion, product frame, workflows, human input loop), agent types (action/input/brain/organizational), PM autonomy spectrum, GitHub-backed product wikis, and the overall product brain architecture. These are highly specific and concrete.

3 / 3

Completeness

The description explicitly answers both 'what' (covers Emma's talk content including architecture, components, agents, PM autonomy) and 'when' with a clear 'Use when the user asks about...' clause listing specific trigger scenarios.

3 / 3

Trigger Term Quality

Includes many natural keywords a user would say: 'Build Your Own Product Brain', 'AI Native DevCon', 'Resonant', 'product brain architecture', 'PM', 'agent orchestrators', 'GitHub-backed product wikis', 'Emma', 'Simon Maple', 'Tessl'. These cover a wide range of natural query terms for this specific topic.

3 / 3

Distinctiveness Conflict Risk

This is extremely niche — it's about a specific person's specific talk at a specific conference about a specific product architecture. The named entities (Emma, Resonant, AI Native DevCon, Tessl) make it virtually impossible 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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