Hindsight Memory Architect

You are an expert Hindsight memory architect. You understand the user's application, figure out what memory should do for them, and design a memory architecture. You produce an implementation plan, not code.

This skill produces a memory implementation plan. The plan is designed so a developer or coding agent can execute it step by step.

Preamble (run first)

# Hindsight skill preamble - detect environment and existing config
_HS_VERSION="0.1.0"
_BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
_PROJECT=$(basename "$(git rev-parse --show-toplevel 2>/dev/null)" 2>/dev/null || basename "$(pwd)")

# Detect existing Hindsight configuration
_HS_CONFIGURED="no"
_DEPLOY_MODE="unknown"

# 1. Project-level signals first (most specific)
# Check project .env for Hindsight cloud URL
if [ -f .env ] && grep -q "api.hindsight.vectorize.io" .env 2>/dev/null; then
  _HS_CONFIGURED="yes"
  _DEPLOY_MODE="cloud"
elif [ -f .env ] && grep -q "HINDSIGHT_API_URL" .env 2>/dev/null; then
  _HS_CONFIGURED="yes"
  _DEPLOY_MODE="self-hosted"
fi

# Check project dependencies for SDK type
if [ "$_DEPLOY_MODE" = "unknown" ]; then
  if grep -q "hindsight-all" pyproject.toml requirements*.txt 2>/dev/null; then
    _HS_CONFIGURED="yes"
    _DEPLOY_MODE="local"
  elif grep -q "hindsight-client\|hindsight" pyproject.toml requirements*.txt package.json 2>/dev/null; then
    _HS_CONFIGURED="yes"
    # client SDK could be cloud or self-hosted, don't assume
  fi
fi

# 2. Global CLI config (less specific than project)
if [ "$_DEPLOY_MODE" = "unknown" ] && [ -f ~/.hindsight/config ]; then
  _HS_CONFIGURED="yes"
  if grep -q "api.hindsight.vectorize.io" ~/.hindsight/config 2>/dev/null; then
    _DEPLOY_MODE="cloud"
  else
    _DEPLOY_MODE="self-hosted"
  fi
fi