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db7f2a6fd5
The official Agent Skills spec (agentskills.io/specification) whitelists exactly 6 top-level frontmatter keys (name/description/license/compatibility/metadata/ allowed-tools). A top-level `origin` key fails the official validator (anthropics/skills quick_validate.py ALLOWED_PROPERTIES; skills-ref validate). This moves `origin: X` -> `metadata.origin: X` across the canonical skills/ tree, preserving each value verbatim. Frontmatter-only, minimal diff. - 251 SKILL.md updated (242 new metadata block, 9 appended to existing metadata) - origin values preserved verbatim (verified 251/251) - YAML validated on all changed files - scoped to canonical skills/ only (docs/<lang> translations + tool mirrors .cursor/.kiro/.agents left untouched; presumably regenerated from canonical) Addresses #2233
65 lines
1.7 KiB
Markdown
65 lines
1.7 KiB
Markdown
---
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name: agentic-engineering
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description: Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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metadata:
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origin: ECC
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---
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# Agentic Engineering
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Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
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## Operating Principles
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1. Define completion criteria before execution.
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2. Decompose work into agent-sized units.
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3. Route model tiers by task complexity.
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4. Measure with evals and regression checks.
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## Eval-First Loop
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1. Define capability eval and regression eval.
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2. Run baseline and capture failure signatures.
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3. Execute implementation.
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4. Re-run evals and compare deltas.
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## Task Decomposition
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Apply the 15-minute unit rule:
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- each unit should be independently verifiable
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- each unit should have a single dominant risk
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- each unit should expose a clear done condition
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## Model Routing
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- Haiku: classification, boilerplate transforms, narrow edits
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- Sonnet: implementation and refactors
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- Opus: architecture, root-cause analysis, multi-file invariants
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## Session Strategy
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- Continue session for closely-coupled units.
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- Start fresh session after major phase transitions.
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- Compact after milestone completion, not during active debugging.
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## Review Focus for AI-Generated Code
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Prioritize:
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- invariants and edge cases
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- error boundaries
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- security and auth assumptions
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- hidden coupling and rollout risk
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Do not waste review cycles on style-only disagreements when automated format/lint already enforce style.
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## Cost Discipline
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Track per task:
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- model
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- token estimate
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- retries
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- wall-clock time
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- success/failure
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Escalate model tier only when lower tier fails with a clear reasoning gap.
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