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Affaan Mustafa c70874fae9 feat(pi): curated pi/core skills+prompts profile, CI load test, and 2.2.2 release sync (#3264 )

Adds a curated, Pi-native, skills+prompts-only profile at pi/core/ for downstream packagers that mirror GitHub Releases.

- manifests/pi-core.json: explicit include lists, per-item exclusion reasons, curation rules, safety allowlists; every root skill and command must be classified.
- scripts/build-pi-core.js regenerates pi/core deterministically (package.json from VERSION, LICENSE, README.md, CURATION.md, skills/, commands/); --check fails CI on drift. council is renamed ecc-council inside pi/core only.
- Safety checks: no callable endpoints outside the allowlist, no npx/curl|sh/pip install, no secrets, no absolute home paths, no symlinks, valid frontmatter, no duplicate names.
- CI: build + drift check and an offline Pi CLI load test of pi/core.
- Release: VERSION, package.json and pi/core/package.json at 2.2.2, CHANGELOG, tag-triggered release verification, and a two-week cadence in CONTRIBUTING.md.

pi/core: 123 of 293 skills and 24 of 94 commands; 35,006 characters of skill description text.

2026-09-29 20:24:05 -05:00

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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

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.

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