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talk-thomas-ai-native-engineering

Use when the user asks about Ian Thomas's talk "AI Native Engineering" (Meta / Reality Labs / Horizon Experiences) — including questions about Meta's AI4P (AI For Productivity) programme, the 6-dimension / 5-level AI maturity model and self-assessment workshop, how Horizon rolled out AI tooling across 500+ engineers, engineering excellence as an adoption vehicle, anti-test-slop, autonomous code mods, the DRS risk-scoring tool, the Horizon MCP server, vanity metrics vs real productivity, or applying Thomas's ground-up-plus-top-down adoption playbook to their own org.

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

81%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.

A well-structured reference-skill body: five clearly sequenced workflows with explicit grounding rules, per-branch fallbacks, and sound one-level-deep routing to companion files. The main deductions are repeated quoting rules across sections, a missing citation-format example, and a companion-file layout that is referenced but never inventoried.

Suggestions

State the excerpt/quoting rule once in the grounding rules and reference it from the workflow sections instead of restating 'safe excerpts' in each one — this would tighten conciseness without losing the constraint.

Add a one-line example of the expected citation format (e.g. how a transcript line-range citation should look) so the 'cite by line range' instruction is executable rather than implied.

Open with a short inventory of the companion bundle (outline.md — section map; transcript.md — full talk; quote.md — vetted highlights) and resolve the level-name uncertainty on line 48 by either confirming the five level names or instructing the reader to treat them as unverified.

DimensionReasoningScore

Conciseness

The body is dense procedural guidance with no padding and no explanations of concepts Claude already knows, but the quoting rule ('quote short, non-sensitive excerpts', 'check quote.md first', 'safe excerpts') is restated in nearly every workflow section, and line 48 carries self-hedging meta-commentary ('the rest are inferred from "zero-index based list"...'). This fits anchor 4 — efficient with minor trimmable redundancy — rather than anchor 5's 'every token earns its place'.

4 / 5

Actionability

Guidance is concrete and executable for an instruction-only skill: precise lookup chains ('outline.md → "Named frameworks / concepts" → "AI Maturity Model"'), file-level routing ('read outline.md to locate the relevant section, then read that section of transcript.md'), explicit fallback behavior ('say "the talk doesn't address this"'), and a concrete workshop cadence ('repeat every 3–4 weeks'). It falls short of anchor 5 because the demanded line-range citation format is never shown by example, and the level names are left partially uncertain rather than given as a definitive list.

4 / 5

Workflow Clarity

Five distinct workflows, each a clearly numbered sequence with explicit verification checkpoints and error-recovery feedback loops: 'If a claim isn't in transcript.md, say "the talk doesn't address this"', 'If the framework genuinely doesn't fit the user's situation, say so', 'If the user hasn't described their state for a dimension, ask before scoring', and 'If the answer genuinely isn't in the transcript, say so explicitly'. These per-branch fallbacks match the anchor-5 pattern of explicit validation with feedback loops; no destructive or batch operations exist, so the cap does not apply.

5 / 5

Progressive Disclosure

The body acts as an index that routes to three companion files (outline.md, transcript.md, quote.md), each one level deep and each with a stated purpose ('quote.md contains pre-extracted safe highlights... check quote.md first'). However, no bundle inventory is given — the referenced files are bare sibling paths with no upfront listing of what each contains or where they live, and none ship alongside SKILL.md in this copy, leaving navigation partially unverifiable. This places it at anchor 4 (good structure, minor organization gaps) rather than anchor 5's fully easy navigation.

4 / 5

Total

17

/

20

Passed

Description

86%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.

A strong, highly specific description for a single-talk reference skill: explicit and comprehensive trigger phrasing, rich natural keywords, and near-zero conflict risk. Its only weakness is that the skill's core capability is implied through topic enumeration rather than stated directly, leaving 'what' fractionally less explicit than 'when'.

DimensionReasoningScore

Specificity

Enumerates several concrete capabilities — 'questions about Meta's AI4P (AI For Productivity) programme, the 6-dimension / 5-level AI maturity model and self-assessment workshop', 'anti-test-slop, autonomous code mods, the DRS risk-scoring tool, the Horizon MCP server', and 'applying Thomas's ground-up-plus-top-down adoption playbook to their own org'. It stops short of anchor 5 because the core 'what' (answering/grounding answers in this specific talk) is conveyed through topic enumeration rather than an explicit capability statement, but it well exceeds the 1–2 concrete actions of anchor 3.

4 / 5

Completeness

The 'when' is explicit and rich ('Use when the user asks about... including questions about... or applying... to their own org'), and the 'what' is present but embedded in the enumerated question types rather than stated as a leading capability phrase. This fits anchor 4 (both present, one could be more explicit) rather than anchor 5, and clearly exceeds anchor 3 where 'when' is missing or only weakly implied.

4 / 5

Trigger Term Quality

Comprehensive natural-term coverage for the niche: 'Ian Thomas', 'AI Native Engineering', 'Meta / Reality Labs / Horizon Experiences', 'AI4P (AI For Productivity)', 'AI maturity model', 'self-assessment workshop', 'vanity metrics vs real productivity', and 'adoption playbook' — exactly the phrases a user asking about this talk would say, with synonyms spelled out. No missing variations are apparent.

5 / 5

Distinctiveness Conflict Risk

The description is anchored to one named talk by one named speaker with org-specific terms (AI4P, DRS, Horizon Experiences) — a clear niche with distinct triggers and minimal risk of firing for the wrong skill. It cannot overlap meaningfully with generic productivity or AI-adoption skills.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

jscraik/Agent-Skills

Path

Plugins/aidevcon/skills/talk-thomas-ai-native-engineering/SKILL.md

Reviewed

Table of Contents

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