Files
ECC/skills/agentic-engineering/SKILL.md
d29cf651c7 fix(skills): declare activation triggers in descriptions and normalize version metadata (#2618)
* 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>
2026-08-11 23:58:14 -04:00

1.8 KiB

name, description, metadata
name description metadata
agentic-engineering Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.
origin
ECC

Agentic Engineering

Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.

Operating Principles

  1. Define completion criteria before execution.
  2. Decompose work into agent-sized units.
  3. Route model tiers by task complexity.
  4. Measure with evals and regression checks.

Eval-First Loop

  1. Define capability eval and regression eval.
  2. Run baseline and capture failure signatures.
  3. Execute implementation.
  4. Re-run evals and compare deltas.

Task Decomposition

Apply the 15-minute unit rule:

  • each unit should be independently verifiable
  • each unit should have a single dominant risk
  • each unit should expose a clear done condition

Model Routing

  • Haiku: classification, boilerplate transforms, narrow edits
  • Sonnet: implementation and refactors
  • Opus: architecture, root-cause analysis, multi-file invariants

Session Strategy

  • Continue session for closely-coupled units.
  • Start fresh session after major phase transitions.
  • Compact after milestone completion, not during active debugging.

Review Focus for AI-Generated Code

Prioritize:

  • invariants and edge cases
  • error boundaries
  • security and auth assumptions
  • hidden coupling and rollout risk

Do not waste review cycles on style-only disagreements when automated format/lint already enforce style.

Cost Discipline

Track per task:

  • model
  • token estimate
  • retries
  • wall-clock time
  • success/failure

Escalate model tier only when lower tier fails with a clear reasoning gap.