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* fix(skills): move version into metadata and normalize to semver 29 skills declared `version` at the top level of their frontmatter. The schema reads it from `metadata`, so tooling that follows the schema either misses it or has to special-case the top level. Three motion skills also declared `version: 1.0`, which is not a valid semantic version; normalized to `1.0.0`. No behavioral change — frontmatter metadata only. * fix(skills): state activation triggers in skill descriptions 148 skills described what they cover but never named the situation that should trigger them. Since the description is what Claude matches against to decide whether to load a skill, a description without a trigger makes activation guesswork — the skill is either missed or loaded at the wrong time. Added a "Use when ..." clause to each, derived from the skill's own body (most already stated the trigger under "## When to Use" or in the opening line; that intent is now reflected in the frontmatter where it is actually read from). Descriptions were only appended to; no existing wording was removed. * fix(skills): sync activation triggers into the Codex skill mirror 10 of the skills whose descriptions changed are also mirrored under `.agents/skills/`, where the description was previously a verbatim copy. Left alone, the two surfaces would disagree about when the skill applies. Only the description line is synced; the Codex copies keep their reduced frontmatter, since that validator accepts only name, description, metadata, license, and allowed-tools. * fix(skills): correct three activation clauses from review - autonomous-loops: the clause pulled new loop work into a skill that its own body marks as a compatibility shim retained for one release. It now points at the canonical continuous-agent-loop instead. - continuous-learning: the description carried the v1 routing directive twice; collapsed to one. - homelab-pihole-dns: the clause fired on any broken home DNS. Narrowed to tasks that actually involve Pi-hole. * chore: retain current main lockfile --------- Co-authored-by: Çağrı Solakoğlu <cagri.solakoglu@vtcenerji.com> Co-authored-by: haelyra <49814733+haelyra@users.noreply.github.com>
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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. |
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AI-First Engineering
Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.
Process Shifts
- Planning quality matters more than typing speed.
- Eval coverage matters more than anecdotal confidence.
- Review focus shifts from syntax to system behavior.
Architecture Requirements
Prefer architectures that are agent-friendly:
- explicit boundaries
- stable contracts
- typed interfaces
- deterministic tests
Avoid implicit behavior spread across hidden conventions.
Code Review in AI-First Teams
Review for:
- behavior regressions
- security assumptions
- data integrity
- failure handling
- rollout safety
Minimize time spent on style issues already covered by automation.
Hiring and Evaluation Signals
Strong AI-first engineers:
- decompose ambiguous work cleanly
- define measurable acceptance criteria
- produce high-signal prompts and evals
- enforce risk controls under delivery pressure
Testing Standard
Raise testing bar for generated code:
- required regression coverage for touched domains
- explicit edge-case assertions
- integration checks for interface boundaries