CtrlK
BlogDocsLog inGet started
Tessl Logo

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

View guide

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-grounding skill that provides clear, actionable workflows for answering questions about a specific talk. Its main strengths are the explicit grounding rules (preventing hallucination), concrete benchmark numbers, and well-sequenced multi-step workflows with validation. Its weaknesses are moderate redundancy between the grounding rules and common workflow sections, and an inconsistency in bundle file references (quotes.md mentioned but not declared in the bundle files section).

Suggestions

Consolidate the 'Grounding rules' and 'Common workflow' sections — they overlap heavily on verbatim quoting, line citation, and respecting transcript limits. Merge into a single 'Grounding rules & workflow' section to save ~15 lines.

Add quotes.md to the 'Bundle files' section at the top, since it's referenced in the 'Key quotes' section and is clearly a dependency.

DimensionReasoningScore

Conciseness

The skill is reasonably well-structured but includes some redundancy — the grounding rules and the 'common workflow' section overlap significantly (both emphasize verbatim quoting, citing lines, and respecting transcript limits). Some use-case sections repeat the same instructions (quote verbatim, cite lines) that are already covered in the common workflow. Could be tightened by ~30%.

2 / 3

Actionability

The skill provides highly concrete, specific guidance for each use case: exact benchmark numbers (74% merge rate, 84% zero-dev-touch), explicit step-by-step workflows for auditing/drafting/Q&A, clear labeling conventions (e.g., '[not from talk — added as a starting placeholder]'), and specific file navigation instructions. Every section tells Claude exactly what to do.

3 / 3

Workflow Clarity

Multi-step processes are clearly sequenced with explicit validation checkpoints: navigate outline first, then read transcript, quote verbatim, cite lines, and explicitly state when information isn't available. The audit workflow walks through every dimension in order and requires asking the user before scoring missing dimensions. The draft workflow includes a verification step (marking additions beyond the talk). Error handling is addressed (missing files, garbled transcription, strained connections).

3 / 3

Progressive Disclosure

The skill references bundle files (outline.md, transcript.md, quotes.md) with clear descriptions of their purpose, which is good structure. However, quotes.md is referenced in the 'Key quotes' section at the bottom but is not listed in the 'Bundle files' section at the top, creating an inconsistency. Additionally, no bundle files were actually provided for evaluation, and the skill itself is fairly long (~100+ lines) with some content that could potentially be split out (e.g., the detailed use-case sections).

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 that clearly identifies a narrow, specific domain (a particular conference talk) and provides extensive trigger terms covering the talk's key concepts, statistics, and applications. The explicit 'Use when' clause at the start and the comprehensive enumeration of subtopics make it highly effective for skill selection. The only minor concern is its length, but the specificity justifies it.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete topics and concepts: merge rate as an AI-adoption metric, non-technical contributors opening PRs, PR fatigue, specific statistics (~74% merge rate, ~84% zero-dev-touch), named companies (Uber, Microsoft, Shopify, Autonomy AI), Calamarous Coding, feature-flag-driven developer autonomy, and applying the framework.

3 / 3

Completeness

Clearly answers both 'what' (knowledge about Tammuz Dubnov's talk and its specific concepts/data points) and 'when' (opens with 'Use when the user asks about...' with extensive explicit trigger scenarios including applying the framework to the user's own org).

3 / 3

Trigger Term Quality

Excellent coverage of natural terms a user would say when asking about this talk: the speaker's name, the talk title, specific concepts like 'AI-native', 'merge rate', 'PR fatigue', 'harness engineering', company names, and specific statistics. These are highly specific and natural trigger terms for this domain.

3 / 3

Distinctiveness Conflict Risk

Extremely distinctive — tied to a specific person's specific talk with named concepts, unique statistics, and proprietary terminology like 'Calamarous Coding'. Virtually no risk of conflicting 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

Table of Contents