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ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

58

Quality

73%

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

Quality

Content

66%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 concise and well-organized for a short advisory skill, with no wasted tokens and clean section structure. Its weakness is actionability and workflow: most content states principles rather than giving concrete, executable guidance (examples, templates, checklists with specifics) and offers no sequenced adoption workflow with checkpoints. Adding a worked example per section or a step-by-step rollout sequence would materially raise the score.

Suggestions

Convert principle statements into concrete artifacts — e.g., an example review-gate checklist, a sample acceptance-criteria template, or a short worked case of applying the testing standard to a PR.

Add a sequenced adoption workflow (e.g., 1) inventory current review gates, 2) re-scope them to behavior/security/data integrity, 3) verify eval coverage exists for touched domains) so the guidance has an ordered path with checkpoints.

For 'Hiring and Evaluation Signals', either show how to test each signal in an interview/eval scenario or trim the section if it is out of scope for the skill's stated purpose.

DimensionReasoningScore

Conciseness

The body is lean bullet lists under clear headers ('Process Shifts', 'Architecture Requirements', 'Code Review in AI-First Teams', 'Hiring and Evaluation Signals', 'Testing Standard') with zero padding and no explanation of concepts Claude already knows — every token earns its place, matching the 'Lean and efficient' anchor.

5 / 5

Actionability

Some guidance is concrete and usable as checklists ('behavior regressions', 'security assumptions', 'data integrity', 'required regression coverage for touched domains'), but much is principle-level assertion ('Planning quality matters more than typing speed', 'Prefer architectures that are agent-friendly') without examples, templates, or specific steps. It sits between anchor 2's 'high-level hints' and anchor 4's executable guidance, landing at 'Some concrete guidance but incomplete'.

3 / 5

Workflow Clarity

There is no sequenced process at all — the 'Process Shifts' list is a philosophy, not an ordered workflow, and no validation checkpoints exist. This is not a destructive/batch skill so the cap does not apply, and the simple-skill exception does not apply since the skill covers multiple concerns; it matches 'Rough sequence present but many gaps; steps poorly defined', leaning lower because no sequence is offered.

2 / 5

Progressive Disclosure

The skill is under 50 lines with no external references needed, and content is organized into well-labeled sections with no buried references or inlined bulk that should be split out — per the rubric's simple-skill guideline this earns the 'Clear overview... easy navigation' level.

5 / 5

Total

15

/

20

Passed

Description

70%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 solid description with an explicit and well-targeted 'Use when' clause covering team process, review gates, and ownership rules for agent-written codebases. Its main weakness is that the 'what' is abstract ('operating model') rather than naming concrete capabilities, and a few natural trigger variations are absent. Third-person voice is correctly used throughout.

DimensionReasoningScore

Specificity

The description names the domain ('Engineering operating model for teams where AI agents generate a large share of implementation output') and touches several application areas ('team process, review gates, or ownership rules'), but the core 'what' is an abstract noun phrase rather than concrete actions, matching the anchor 'Names domain and 1-2 concrete actions, but not comprehensive'. It is not a 4 because no concrete capabilities are enumerated as actions the skill performs.

3 / 5

Completeness

Both parts are present: a 'what' ('Engineering operating model for teams where AI agents generate a large share of implementation output') and an explicit 'Use when setting team process, review gates, or ownership rules...'. Not a 5 because the 'what' is a fairly abstract characterization rather than a concrete capability statement with concrete trigger phrases on both sides.

4 / 5

Trigger Term Quality

Phrases like 'setting team process', 'review gates', 'ownership rules', and 'codebase largely written by agents' are natural things a user would say when needing this skill. It falls short of a 5 because common variations such as 'AI-generated code', 'agent-written code', or 'code review standards' are missing.

4 / 5

Distinctiveness Conflict Risk

The AI-agent-written-codebase niche is distinct with clear triggers ('teams where AI agents generate a large share of implementation output'), but terms like 'review gates' and 'team process' carry minor overlap risk with general code-review or process skills, matching 'Mostly distinct; minor overlap risk with closely related skills'.

4 / 5

Total

15

/

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

affaan-m/ECC

Path

skills/ai-first-engineering/SKILL.md

Reviewed

Table of Contents

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