Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

2026-06-10212.1k

orch-add-feature

affaan-m/ECC

Orchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.

2026-06-07212.1k

orch-build-mvp

affaan-m/ECC

Orchestrate bootstrapping a working MVP from a design or spec document — ingest the doc, plan thin vertical slices, scaffold the first end-to-end slice, then TDD-implement, review, and gated commit. Use to turn an SDD/PRD into a running starting point.

2026-06-07212.1k

orch-change-feature

affaan-m/ECC

Orchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.

2026-06-07212.1k

orch-fix-defect

affaan-m/ECC

Orchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, and gated commit — by delegating each phase to the matching ECC agent. Use when existing behavior is broken or wrong.

2026-06-07212.1k

orch-pipeline

affaan-m/ECC

Shared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invoked directly.

2026-06-07212.1k

name	agentic-engineering
description	Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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

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

Eval-First Loop

Define capability eval and regression eval.
Run baseline and capture failure signatures.
Execute implementation.
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.