CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output.

Quality

70%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/ai-first-engineering/SKILL.md

The canonical home for this skill is ai-first-engineering in affaan-m/ECC

SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

The body is exceptionally lean and well-organized — every section is a tight, unpadded checklist that respects the token budget. Its main weakness is actionability: it states what to value (review focus areas, architecture properties, test requirements) without examples, templates, or criteria that would let a reader execute the guidance concretely.

Suggestions

Add one concrete example per major section — e.g. a before/after review-comment pair for 'Review for behavior regressions', or a sample acceptance criterion showing 'define measurable acceptance criteria'.

Turn abstract directives like 'Prefer architectures that are agent-friendly' into a short decision checklist or worked example (e.g. what an explicit boundary vs a hidden convention looks like in a real codebase).

For the Testing Standard, specify minimal concrete thresholds (e.g. what counts as 'touched domains' coverage, or a template edge-case assertion) so the bar is enforceable rather than aspirational.

DimensionReasoningScore

Conciseness

The body is pure checklist and bullet format with zero padding — lines like "Planning quality matters more than typing speed" and "Avoid implicit behavior spread across hidden conventions" contain no explanation of concepts Claude already knows. Every token earns its place; matches the 'Lean and efficient' anchor.

5 / 5

Actionability

Concrete enumerable checklists exist ("behavior regressions, security assumptions, data integrity, failure handling, rollout safety"; "required regression coverage for touched domains, explicit edge-case assertions"), so guidance goes beyond vague direction, but there are no steps, examples, templates, or assessment criteria for applying them — 'Some concrete guidance but incomplete; missing key details', short of 4's 'concrete code or commands with minor gaps'.

3 / 5

Workflow Clarity

This is an instruction-only standards skill, not a procedural workflow, and involves no destructive or batch operations, so the validation cap does not apply. Sections are clearly organized and each area's guidance is unambiguous, but the skill offers no application sequence or checkpoints across its five concern areas, so it does not merit the simple-skill score of 5. Between anchors 3 and 4 → 4.

4 / 5

Progressive Disclosure

The body is under 50 lines, no bundle files exist or are needed, and no external references are cited; sections are well-organized with clear headers. Per the scoring note for simple skills, this matches the top anchor for progressive disclosure.

5 / 5

Total

17

/

20

Passed

Description

48%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.

The description identifies a distinctive niche but is written as a category label rather than a capability statement: it names no concrete actions and provides no explicit 'Use when...' trigger guidance, which is explicitly capped at 3 for completeness. It would benefit from naming what the skill does and the natural trigger phrases users would say.

Suggestions

Add concrete actions to the description (e.g. 'Designs review processes, architecture guidelines, and testing standards for teams shipping AI-generated code').

Append an explicit trigger clause such as 'Use when the user mentions AI-first engineering, AI-generated code, agent-assisted development, or designing process/reviews for teams using AI agents'.

Include natural synonyms users would actually say (AI-assisted code, LLM agents, Copilot/agent-generated code) to improve trigger-term coverage.

DimensionReasoningScore

Specificity

"Engineering operating model for teams where AI agents generate a large share of implementation output" names the domain but lists no concrete actions — what the skill actually does (designing process, reviews, architecture) appears only in the body, not the description. This matches the anchor 'Names the domain but actions are minimal or generic', and falls short of 3 because no concrete action is stated.

2 / 5

Completeness

The 'what' is present (an engineering operating model), and 'when' is only weakly implied by the clause "for teams where AI agents generate a large share of implementation output"; there is no explicit 'Use when...' clause, which caps completeness at 3 per the judging guidelines. Not 4 because the 'when' is not explicitly stated as trigger guidance.

3 / 5

Trigger Term Quality

Terms like "Engineering", "operating model", "AI agents", and "implementation output" are relevant to the niche, but common natural phrasings users would say — e.g. "AI-generated code", "AI-assisted code review", "LLM agents", or tool names like Copilot — are missing. This matches 'Some relevant keywords but missing common variations or synonyms', and is below 4 because keyword coverage is not good enough to be merely 'a few natural terms missing'.

3 / 5

Distinctiveness Conflict Risk

The qualifier "teams where AI agents generate a large share of implementation output" carves a fairly distinct niche with minimal conflict risk, though the broad operating-model scope (process, reviews, testing, hiring) creates minor overlap with closely related engineering-process and code-review skills. Between anchors 3 and 4, noticeably above the midpoint → 4.

4 / 5

Total

12

/

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

valeriibeck-star/claude-skills

Path

skills/ai-first-engineering/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.