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talk-jourdan-pipelines-to-prompts

Assists with questions about a practitioner panel talk titled 'From Pipelines to Prompts: Surviving the Shift to AI' featuring Stephane Jourdan, Simon (Saxo Bank), and Samantha. Use when a user asks about what panelists said, argued, or disagreed on regarding AI-native transformation, harness engineering, observability, developer cognitive load, feedback loops, reflector agents, or co-driving vs. self-driving analogies. Answers factual questions with verbatim transcript quotes, applies panelist frameworks to user situations, surfaces relevant panel insights during related discussions, and explains concepts like harness engineering, self-learning production agents, and explainability tooling.

71

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%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 with concrete, sequenced guidance for each question type and appropriate safety framing. It is efficient and actionable, with only minor verbosity and a small bundle-reference inconsistency holding it back from top marks.

Suggestions

Reconcile the quote-file name: the 'How to Answer' section references 'QUOTES.md' while 'Key quotes' references 'quote.md' — pick one and use it consistently.

Tighten the four Goal/Steps/Example blocks (e.g., merge the Goal line into the Steps preamble) to reduce token usage without losing clarity.

If a transcript or quote bundle ships with the skill, ensure the referenced filenames (transcript.md, quote.md) actually exist in the bundle so the signaled references resolve.

DimensionReasoningScore

Conciseness

Mostly efficient and purposeful — the concept framings earn their place because they capture panel-specific divergences from common usage, and the response patterns are concrete. Minor trimming is possible in the four Goal/Steps/Example blocks, which keeps it just below a 5.

4 / 5

Actionability

Each of the four question types comes with a Goal, numbered Steps, and a concrete example response pattern, giving mostly executable guidance. The bracketed placeholders (e.g., '[safe excerpts from transcript]') are intentional since content depends on the transcript, leaving only minor gaps.

4 / 5

Workflow Clarity

Each response pattern is a clearly sequenced numbered procedure, and paraphrase-flagging acts as a checkpoint ('flag it explicitly', 'state clearly that you are paraphrasing'). This is a non-destructive Q&A skill so the validation cap does not apply; it sits just below 5 because there is no explicit error-recovery feedback loop.

4 / 5

Progressive Disclosure

The body is well-organized into clearly signaled sections and points one-level-deep to bundle files (transcript.md, QUOTES.md, quote.md) with a documented fallback when bundles are absent. Minor organization gaps remain: no bundle files are actually present, and the quote file is referenced inconsistently as both 'quote.md' and 'QUOTES.md'.

4 / 5

Total

16

/

20

Passed

Description

100%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 precise, third-person description that clearly states both capabilities and explicit trigger conditions with concrete natural phrases. It is comprehensive, distinctive, and free of vague fluff or over-claims.

DimensionReasoningScore

Specificity

Names the domain (the specific panel talk) and lists multiple concrete actions — 'Answers factual questions with verbatim transcript quotes, applies panelist frameworks to user situations, surfaces relevant panel insights... and explains concepts' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' (the listed answer behaviors) and 'when' via a concrete 'Use when a user asks about...' clause with specific trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural trigger phrases a user would actually say — 'what panelists said, argued, or disagreed on' plus the full concept vocabulary (AI-native transformation, harness engineering, observability, feedback loops, reflector agents, co-driving vs. self-driving) and the talk title itself.

5 / 5

Distinctiveness Conflict Risk

Scoped to a single named talk with named panelists and a distinctive concept vocabulary, giving it a clear niche with minimal overlap risk against other skills.

5 / 5

Total

20

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
jscraik/Agent-Skills
Reviewed

Table of Contents

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