@artale/pi-pai

Personal AI Infrastructure for Pi — synced with PAI v1.0.0 + Daniel Miesslers framework + 9 skill categories

Package details

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extensionskill

Install @artale/pi-pai from npm and Pi will load the resources declared by the package manifest.

npm repo home report

$ pi install npm:@artale/pi-pai

Package: @artale/pi-pai
Version: 4.4.1
Published: May 2, 2026
Downloads: 193/mo · 193/wk
Author: artale
License: MIT
Types: extension, skill
Size: 166.9 KB
Dependencies: 1 dependency · 1 peer

Pi manifest JSON

{
  "extensions": [
    "src/extension.ts"
  ],
  "commands": [
    "status"
  ],
  "tools": [
    "pai_status",
    "pai_learn",
    "pai_rate"
  ],
  "skills": [
    "agents",
    "content-analysis",
    "investigation",
    "media",
    "research",
    "scraping",
    "security",
    "telos",
    "thinking"
  ],
  "image": "https://raw.githubusercontent.com/arosstale/pi-pai/main/preview.png"
}

Security note

Pi packages can execute code and influence agent behavior. Review the source before installing third-party packages.

README

π-PAI v4.2.0 — Personal AI Infrastructure for Pi

A Pi Coding Agent extension implementing Daniel Miessler's PAI framework (9.6K ⭐), synced with PAI v4.0.3 + Daniels official Pi release (v1.0.0). Includes the Ralph Wiggum iteration technique, damage control, and 5 features ported from Miessler's full system.

Install

pi install npm:@artale/pi-pai

What's New in v4.0

Synced with Miessler's PAI v4.0.3

Feature	Source	Implementation
v4 Algorithm	PAI v4.0.3	OBSERVE → PLAN → DECIDE → EXECUTE → VERIFY (was 7-phase, now 5)
Sentiment tracking	PAI Observability	Every rating gets sentiment (positive/neutral/negative) + trend analysis
Agent personas	PAI 14 agents	7 personas: architect, engineer, pentester, designer, reviewer, researcher, qa
Self-evolution	PAI self-upgrade	Detects repeating learning patterns (3+ occurrences), triggers `/pai evolve`
Plans convention	PAI Plans dir	Auto-creates `.pi/plans/`, lists plans via `/pai plans`

Commands

`/pai` — Goal-Driven Algorithm

# Setup
/pai mission Build a profitable trading system
/pai goal Deploy live stat-arb strategy
/pai challenge Overfitting risk on historical data

# Run the v4 algorithm loop
/pai loop Deploy live strategy    # Start: OBSERVE
/pai isc Strategy achieves Sharpe >1.5 on 5-year backtest
/pai next Observed: spread mean-reverts at 3.2 std
/pai next Plan: backtest 2020-2025, then paper trade 2 weeks
/pai next Decision: go with mean-reversion, tight stops
/pai next Executed: deployed to paper trading
/pai next Verified: 58% win rate, Sharpe 1.8

# Templates
/pai template trading    # Pre-built mission + goals + challenges
/pai template saas|devops|research|agent

# Agent personas (NEW in v4)
/pai agent architect Design the auth system for a multi-tenant SaaS
/pai agent pentester Review this API for security vulnerabilities
/pai agent designer Create the onboarding flow for mobile
/pai agent reviewer Review the feature branch against main

# Sentiment & trends (NEW in v4)
/pai trend              # Rating trend: avg, recent, sentiment distribution
/pai evolve             # Self-evolution: repeating pattern report

# Other
/pai plans              # List .pi/plans/ directory
/pai status             # Full status with all v4 features
/pai learn <insight>    # Record a learning
/pai done g0            # Complete a goal
/pai block g1           # Block a goal
/pai reset              # Clear everything

`/rate` — Sentiment-Aware Ratings

/rate 9 Clean architecture, fast execution  # → ⭐9 😊 positive
/rate 3 Missed edge cases, slow             # → ⭐3 😞 negative → auto-captures learning
/rate 6                                     # → ⭐6 😐 neutral

Ratings track: score (1-10), context, timestamp, sentiment (inferred from score + keywords).

Widget shows trend: ⭐7.2 📈8.0 (15 ratings) — improving/declining/stable.

`/ralph` — Deterministic Iteration

/ralph Build a REST API with auth, tests, and docs
# Agent iterates up to 50 times until RALPH_DONE
/ralph stop

🛡️ Damage Control

Guards via YAML rules (damage-control-rules.yaml):

Blocked patterns: rm -rf, git reset --hard, git push --force, DROP TABLE
Confirm-first: git push --delete, git branch -D
Zero-access: .env, ~/.ssh/, ~/.aws/, *.pem
Read-only/No-delete: configurable per project

🔧 Agent Tools

Tool	Description
`pai_status`	Full status with v4 algorithm, trends, personas, patterns
`pai_learn`	Record insight with sentiment
`pai_rate`	Rate 1-10 with sentiment + trend tracking

📊 Live Widget

  🎯 Build a profitable trading system
  Goals: 2⚡ 0🚫 1✓ │ 5 learnings │ ⭐7.2 📈8.0 (15)
  Loop: ● ● ◉ ○ ○ [DECIDE] standard 42s
  ⚠️ 2 repeating pattern(s) — consider /pai evolve

Agent Personas

Persona	Focus
`architect`	System design, scalability, API design, failure modes
`engineer`	Production code, error handling, types, tests
`pentester`	Security: injection, auth bypass, SSRF, secrets, deps
`designer`	UX/UI, accessibility, responsive, interaction patterns
`reviewer`	Code review: correctness, edge cases, P1/P2/P3 findings
`researcher`	Deep investigation, evidence-based, source citations
`qa`	Test planning: happy paths, edge cases, boundaries, regression

Lineage

Project	Author	What
Personal AI Infrastructure	Daniel Miessler	PAI v4.0.3 framework (9.6K ⭐)
pi-ralph	Whamp	Ralph Wiggum iteration technique
pi-vs-claude-code	disler	Damage control + extension patterns
Pi Coding Agent	Mario Zechner	The platform

v3 → v4 Migration

The algorithm changed from 7 phases to 5:

Old: OBSERVE → THINK → PLAN → DEFINE → EXECUTE → MEASURE → LEARN
New: OBSERVE → PLAN → DECIDE → EXECUTE → VERIFY

THINK+PLAN merged into PLAN. DEFINE became DECIDE. MEASURE+LEARN merged into VERIFY.

Active loops will reset on upgrade. All other state (goals, learnings, ratings) carries forward.