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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 for answering questions about a specific conference talk. Its greatest strengths are the concrete, step-by-step workflows for each use case and the rigorous grounding rules that prevent hallucination. Its main weakness is moderate repetition of procedural instructions (especially the 'read outline.md then transcript.md' and 'quote verbatim' patterns) across sections, which inflates token cost without adding new information.

Suggestions

Consolidate the repeated procedural steps (read outline.md → find section → read transcript.md → quote verbatim → cite line numbers) into a single 'Standard lookup procedure' section referenced by all use cases, reducing redundancy by ~30%.

Since bundle files are referenced but not provided for evaluation, ensure outline.md, transcript.md, and quotes.md exist and are correctly pathed relative to SKILL.md.

DimensionReasoningScore

Conciseness

The content is reasonably efficient for a complex, multi-use-case skill, but there is noticeable repetition — the instruction to 'quote verbatim from transcript.md' appears in nearly every section, and some procedural steps (read outline.md, then transcript.md) are repeated 5+ times when they could be stated once as a universal rule. The grounding rules section is tight, but the 'How to help' sections could be consolidated.

2 / 3

Actionability

The skill provides highly concrete, specific guidance for each use case: exact steps for applying frameworks, specific selection criteria for methodologies with quoted decision heuristics, named artifacts with their transcript locations, explicit verdicts to produce (covered/partial/missing), and clear instructions on what to mark as added content. The guidance is directly executable by Claude without ambiguity.

3 / 3

Workflow Clarity

Each use case has a clearly numbered sequence of steps with explicit validation checkpoints — e.g., 'If the framework genuinely doesn't fit, say so', 'Don't skip ones that seem weak', 'If the answer genuinely isn't in the transcript, say so explicitly', and 'Mark anything you add beyond her explicit prescription'. The grounding rules establish clear guardrails, and the workflows include error-handling/boundary conditions throughout.

3 / 3

Progressive Disclosure

The skill references external files (outline.md, transcript.md, quotes.md) with clear navigation instructions, which is good progressive disclosure. However, no bundle files were provided, so we cannot verify these references exist. The SKILL.md itself is fairly long and could benefit from splitting some of the repeated procedural patterns into a shared reference, but the section organization by use case is logical and navigable.

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 for a knowledge-retrieval skill about a specific conference talk. It excels at specificity by naming the speaker, talk, conference, and numerous subtopics, and includes an explicit 'Use when' clause with comprehensive trigger scenarios. The only minor concern is its length, but the detail is justified given the niche topic and the need to capture diverse query patterns.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete topics: three named methodologies (pseudo-greenfield, strangler fig pattern, branch by abstraction), specific case studies (AG Grid upgrade, Nearform), specific concepts (code as a city metaphor, value-vs-complexity mirror exercise), and specific speaker/talk details.

3 / 3

Completeness

The description clearly answers both 'what' (knowledge about Katie Roberts's specific talk and its topics) and 'when' with an explicit 'Use when' clause listing numerous trigger scenarios including questions about the talk, its methodologies, case studies, and applying the approach.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms a user would say: the speaker's name, talk title, conference name, 'brownfield vs greenfield', 'legacy codebases', 'strangler fig pattern', 'branch by abstraction', 'AI agents going rogue', specific case study names. These are highly specific and natural query terms.

3 / 3

Distinctiveness Conflict Risk

Extremely distinctive — it's scoped to a specific speaker, a specific talk at a specific conference, with named methodologies and case studies. This is unlikely to conflict with any other skill unless there were multiple skills about the same talk.

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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