NOTE

Gemini Handoff — Build Sprint (2026-04-27)

authorclaude-sonnet-4-6 targets aliases titleGemini Handoff — Build Sprint (2026-04-27) statusactive date2026-04-27 typefleeting

Gemini Handoff: Build Sprint 2026-04-27

Context

Claude added 17 new notes across three sessions today. The semantic graph does not yet know they exist — no embeddings, no auto-links, no graph entries. Your job is to integrate them, grow the graph, create three missing stub notes, write a clustering script, and optionally bootstrap the external source pipeline.

Execute in order. Each task builds on the previous one.

All shell commands use pwsh (PowerShell 7). Never use Windows PowerShell 5.1.

Task 1 — Embedding Sync (Run the Pipeline)

Script: 02_System/sync-embeddings.ps1 Time estimate: ~25 minutes (17 new notes × 1.2s rate-limit gap + API latency) Prereq: $env:GEMINI_API_KEY must be set

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/sync-embeddings.ps1

The script is incremental — it SHA-256 hashes every note and only calls the API for notes whose content has changed since the last run. The 17 new notes will be caught automatically. Existing notes are skipped.

Expected output: Embedding 17 note(s) via Gemini text-embedding-004... followed by progress lines, ending with Done. 17 note(s) embedded and stored.

If it outputs 0 notes to embed: The hashes are already current — run with -Force to re-embed regardless:

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/sync-embeddings.ps1 -Force

After the run, verify the new notes are in the DB:

# Quick spot-check — should return 17 rows dated today
pwsh -NoProfile -ExecutionPolicy Bypass -Command @'
$db = "$PSScriptRoot/../00_Raw/PoShWiKi/wiki.db"
# or check the count directly:
Write-Host "Run sync-embeddings.ps1 then check NoteEmbeddings table for today's rows"
'@

Task 2 — Graph Sync

Script: 02_System/sync-vault-graph.ps1 Time estimate: < 2 minutes

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/sync-vault-graph.ps1

This parses all wikilinks from 01_Wiki/ into the Links table. The 17 new notes contain ~80 new wikilinks that are not yet in the graph. Run this before auto-link so the auto-link judge has a current picture of what is already connected.

Task 3 — Auto-Link Pass

Script: 02_System/auto-link.ps1 Time estimate: ~10 minutes (30 pairs at ~20s each) Prereq: Task 1 and Task 2 complete; $env:GEMINI_API_KEY set

First, do a dry run to inspect what the judge would link:

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/auto-link.ps1 -DryRun -TopN 40 -Threshold 0.82

Review the output. If the suggested pairs look sensible (no false positives like linking two unrelated specs just because both contain "agent"), proceed:

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/auto-link.ps1 -TopN 40 -Threshold 0.82

Why 0.82? The vault's prior auto-link sessions used 0.85 and produced 143 pairs. The 17 new pattern/spec notes are semantically tight clusters — 0.82 will catch legitimate cross-connections without producing noise. Do not go below 0.80.

After the run, do a second graph sync to capture the new wikilinks:

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/sync-vault-graph.ps1

Record the new link count in 02_System/log.md:

## [2026-04-27] Post-Sprint Auto-Link Pass
* Notes embedded: 17
* New auto-links: N (from Xprevious to Ynew)
* Threshold: 0.82, TopN: 40

Task 4 — Create Three Missing Stub Notes

These wikilink targets are referenced throughout the new notes but the files do not exist. Create them as proper YANP-compliant permanent notes with enough content to be non-thin (≥ 80 words, ≥ 2 outbound links).

4a. 01_Wiki/workflow-agents.md

Referenced by: agent-development-kit, lit-adk-documentation, pattern-parallel-fan-out

Content to cover:

  • The three ADK workflow agent types and when each is used
  • SequentialAgent — fixed-order pipeline; use when step N depends on step N-1's output
  • ParallelAgent — concurrent fan-out; use when steps are independent; maps to pattern-parallel-fan-out
  • LoopAgent — repeat until condition; use for retry/polling patterns
  • Key distinction from LlmAgent: workflow agents are deterministic — no LLM call decides the routing

Required frontmatter:

---
title: Workflow Agents
author: gemini-cli
date: '2026-04-27'
status: active
type: permanent
aliases:
  - workflow-agent
  - sequential-agent
  - parallel-agent
  - loop-agent
---

Required wikilinks in References: agent-development-kit, lit-adk-documentation, pattern-parallel-fan-out, pattern-dynamic-delegation

4b. 01_Wiki/adk-session-service.md

Referenced by: agent-development-kit, lit-adk-documentation, pattern-state-transfer

Content to cover:

  • Session object: single conversation scope, contains Events (history) and State (working memory dict)
  • State vs Memory: State is session-scoped (lost when session ends); Memory is cross-session (long-term recall)
  • SessionService: the ADK service that persists sessions; InMemorySessionService for local, pluggable backends for production
  • State access patterns: tool_context.state["key"] for reading, direct assignment for writing
  • output_key on LlmAgent: how a sub-agent's final response is automatically written into session.state
  • Contrast with Swarm context_variables and A2A transfer_context.state — these are the same concept across frameworks (see pattern-state-transfer)

Required frontmatter:

---
title: ADK Session Service
author: gemini-cli
date: '2026-04-27'
status: active
type: permanent
aliases:
  - adk-session
  - adk-state
  - session-service
---

Required wikilinks in References: agent-development-kit, pattern-state-transfer, lit-adk-documentation, lit-openai-swarm, a2a-protocol

4c. 01_Wiki/multi-agent-patterns-moc.md

Referenced by: index, PoShWiKi board. This is the Map of Content for the 7 pattern notes.

Content to cover:

Required frontmatter:

---
title: Multi-Agent Patterns MOC
author: gemini-cli
date: '2026-04-27'
status: active
type: community
aliases:
  - multi-agent-patterns
  - agent-pattern-language
  - patterns-moc
---

After creating all three notes, run graph sync again:

pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/sync-vault-graph.ps1

Task 5 — Clustering Script (New Python Script)

Output file: 02_System/cluster-notes.py Prereq: Task 1 complete (embeddings in DB)

Write a Python script that reads embeddings from the SQLite DB, runs k-means, and outputs cluster membership lists — the Phase 1 output that community-report-generator requires Claude to summarize.

#!/usr/bin/env python3
"""
cluster-notes.py — Phase 1 of the Community Report Generator.
Reads NoteEmbeddings from the vault DB, runs k-means clustering,
and writes cluster membership files to 01_Wiki/community-reports/.

Usage:
    python 02_System/cluster-notes.py [--k 8] [--db path/to/wiki.db]
"""
import argparse, json, os, sqlite3
from pathlib import Path

import numpy as np
from sklearn.cluster import KMeans

VAULT_ROOT = Path(__file__).parent.parent
DEFAULT_DB = VAULT_ROOT / "00_Raw/PoShWiKi/wiki.db"
REPORTS_DIR = VAULT_ROOT / "01_Wiki/community-reports"

def load_embeddings(db_path: str) -> tuple[list[str], np.ndarray]:
    """Return (note_names, embeddings_matrix) for all active notes."""
    conn = sqlite3.connect(db_path)
    rows = conn.execute(
        "SELECT NoteName, Embedding FROM NoteEmbeddings ORDER BY NoteName"
    ).fetchall()
    conn.close()
    names = [r[0] for r in rows]
    vecs  = np.array([json.loads(r[1]) for r in rows], dtype=np.float32)
    # L2-normalize for cosine similarity
    norms = np.linalg.norm(vecs, axis=1, keepdims=True)
    vecs  = vecs / np.where(norms == 0, 1, norms)
    return names, vecs

def estimate_k(vecs: np.ndarray, k_range=range(4, 16)) -> int:
    """Elbow method: pick k where inertia improvement drops below 10%."""
    inertias = []
    for k in k_range:
        km = KMeans(n_clusters=k, random_state=42, n_init=10)
        km.fit(vecs)
        inertias.append(km.inertia_)
    for i in range(1, len(inertias)):
        if inertias[i-1] > 0:
            improvement = (inertias[i-1] - inertias[i]) / inertias[i-1]
            if improvement < 0.10:
                return list(k_range)[i]
    return list(k_range)[-1]

def write_cluster_report(cluster_id: int, member_names: list[str], output_dir: Path):
    """Write a Level-1 cluster membership file in community-report-generator format."""
    output_dir.mkdir(parents=True, exist_ok=True)
    path = output_dir / f"cluster-{cluster_id:02d}-members.md"
    lines = [
        f"---",
        f"title: 'Cluster {cluster_id:02d} — Members (Level-1)'",
        f"author: gemini-cli",
        f"date: '{__import__('datetime').date.today()}'",
        f"status: active",
        f"type: community",
        f"cluster_id: 'cluster-{cluster_id:02d}'",
        f"level: 1",
        f"member_count: {len(member_names)}",
        f"aliases:",
        f"  - cluster-{cluster_id:02d}",
        f"---",
        f"",
        f"# Cluster {cluster_id:02d} — Member Notes",
        f"",
        f"*This file was generated by `02_System/cluster-notes.py`. Feed to Claude Summarizer for [[community-report-generator]] Phase 2.*",
        f"",
        f"## Members ({len(member_names)} notes)",
        f"",
    ]
    for name in sorted(member_names):
        lines.append(f"- `[[{name}]]`")
    lines += ["", "## References", "- [[community-report-generator]]", ""]
    path.write_text("\n".join(lines), encoding="utf-8")
    return path

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--k",   type=int, default=0,
                        help="Number of clusters (0 = auto-detect via elbow method)")
    parser.add_argument("--db",  default=str(DEFAULT_DB))
    args = parser.parse_args()

    print(f"Loading embeddings from {args.db}...")
    names, vecs = load_embeddings(args.db)
    print(f"  {len(names)} notes loaded.")

    k = args.k if args.k > 0 else estimate_k(vecs)
    print(f"  Using k={k} clusters.")

    km = KMeans(n_clusters=k, random_state=42, n_init=10)
    labels = km.fit_predict(vecs)

    clusters: dict[int, list[str]] = {}
    for name, label in zip(names, labels):
        clusters.setdefault(label, []).append(name)

    print(f"\nCluster sizes:")
    for cid, members in sorted(clusters.items(), key=lambda x: -len(x[1])):
        print(f"  Cluster {cid:02d}: {len(members)} notes")

    print(f"\nWriting membership files to {REPORTS_DIR}/...")
    for cid, members in clusters.items():
        path = write_cluster_report(cid, members, REPORTS_DIR)
        print(f"  Written: {path.name}")

    print(f"\nDone. Run Claude Summarizer on each cluster file for Phase 2.")

if __name__ == "__main__":
    main()

Dependencies: numpy, scikit-learn (install via pip install numpy scikit-learn if not present).

Run it:

python 02_System/cluster-notes.py --k 8
# or auto-detect k:
python 02_System/cluster-notes.py

Expected output: 6–10 .md files in 01_Wiki/community-reports/, one per cluster. Each file is a formatted member list ready to paste into the community-report-generator Phase 2 Claude prompt.

After writing the script and running it, commit it:

  • Stage 02_System/cluster-notes.py
  • Stage all 01_Wiki/community-reports/cluster-*.md files generated

Task 6 — Firecrawl Bootstrap (Conditional on API Key)

Prereq: $env:FIRECRAWL_API_KEY set AND Tasks 1–4 complete

If you have a Firecrawl API key, run the ingestion pipeline from spec-firecrawl-pgvector-pipeline against these three priority targets (they cover the most-referenced external docs in the vault):

Priority URL includePaths Approx pages
1 https://modelcontextprotocol.io ["/specification/", "/docs/"] ~40
2 https://adk.dev ["/get-started/", "/agents/", "/tools/"] ~30
3 https://docs.firecrawl.dev ["/features/", "/api-reference/"] ~20

Use the Python ingestion script from spec §7. Set limit: 50 for each site on the first run to stay within free-tier credits (500 pages/month total).

If FIRECRAWL_API_KEY is not set: skip this task entirely. Do not attempt to simulate it.

Final Verification & Commit

After all tasks are complete:

# Full maintenance run — must complete 100/100
pwsh -NoProfile -ExecutionPolicy Bypass -File 02_System/run-maintenance.ps1

Create a new PoShWiKi session page for this sprint: 02_System/poshwiki-pages/Session 2026-04-27.md

# Session 2026-04-27

## Session Goal
Integrate 17 new Claude notes into the semantic graph, grow auto-links, create 3 stub notes, write clustering script.

## Current Seam
[Fill in after completion]

## Next Steps
[Fill in after completion]

## Actions
- sync-embeddings.ps1: N notes embedded
- auto-link.ps1: N new links added (threshold 0.82, TopN 40)
- sync-vault-graph.ps1: run 3×
- stub notes created: workflow-agents, adk-session-service, multi-agent-patterns-moc
- cluster-notes.py: written + run, K clusters output to 01_Wiki/community-reports/

Commit all changes:

chore(graph): post-sprint graph integration — embeddings, auto-links, stubs, clustering

sync-embeddings: 17 new notes embedded (pattern-*, lit-*, spec-*)
auto-link: N new wikilinks (threshold 0.82)
stubs: workflow-agents, adk-session-service, multi-agent-patterns-moc
cluster-notes.py: Phase 1 clustering script, K clusters → 01_Wiki/community-reports/

Pre-verified Facts (Do Not Re-derive)

  • sync-embeddings.ps1 uses gemini-embedding-001 (768-dim vectors stored as JSON in NoteEmbeddings.Embedding). The model name in the script is already correct — do not change it.
  • auto-link.ps1 uses claude-haiku-4-5-20251001 as the judge model (already set in the script's default -Model param). Do not change it.
  • DB path: $env:POSHWIKI_DB_PATH if set, otherwise 00_Raw/PoShWiKi/wiki.db.
  • The 17 new notes are all in 01_Wiki/ with correct YANP frontmatter — no frontmatter fixes needed before embedding.
  • 01_Wiki/community-reports/ may already exist from a prior session (community 7 was written 2026-05-18 per the log). Do not delete it — the clustering script appends new files, it does not overwrite existing ones.

References