poc-memory v0.4.0: graph-structured memory with consolidation pipeline
Rust core: - Cap'n Proto append-only storage (nodes + relations) - Graph algorithms: clustering coefficient, community detection, schema fit, small-world metrics, interference detection - BM25 text similarity with Porter stemming - Spaced repetition replay queue - Commands: search, init, health, status, graph, categorize, link-add, link-impact, decay, consolidate-session, etc. Python scripts: - Episodic digest pipeline: daily/weekly/monthly-digest.py - retroactive-digest.py for backfilling - consolidation-agents.py: 3 parallel Sonnet agents - apply-consolidation.py: structured action extraction + apply - digest-link-parser.py: extract ~400 explicit links from digests - content-promotion-agent.py: promote episodic obs to semantic files - bulk-categorize.py: categorize all nodes via single Sonnet call - consolidation-loop.py: multi-round automated consolidation Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
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ProofOfConcept
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scripts/monthly-digest.py
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scripts/monthly-digest.py
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#!/usr/bin/env python3
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"""monthly-digest.py — generate a monthly episodic digest from weekly digests.
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Collects all weekly digests for a given month, identifies cross-week arcs
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and the month's overall trajectory, and produces a monthly summary.
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Usage:
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monthly-digest.py [YYYY-MM] # generate digest for a month (default: current)
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monthly-digest.py 2026-02 # generates digest for February 2026
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Output:
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~/.claude/memory/episodic/monthly-YYYY-MM.md
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"""
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import json
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import os
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import re
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import subprocess
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import sys
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from datetime import date, timedelta
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from pathlib import Path
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MEMORY_DIR = Path.home() / ".claude" / "memory"
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EPISODIC_DIR = MEMORY_DIR / "episodic"
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AGENT_RESULTS_DIR = MEMORY_DIR / "agent-results"
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EPISODIC_DIR.mkdir(parents=True, exist_ok=True)
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AGENT_RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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def get_weeks_in_month(year: int, month: int) -> list[str]:
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"""Get all ISO week labels that overlap with this month."""
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weeks = set()
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d = date(year, month, 1)
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while d.month == month:
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iso = d.isocalendar()
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weeks.add(f"{iso.year}-W{iso.week:02d}")
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d += timedelta(days=1)
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return sorted(weeks)
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def load_weekly_digests(week_labels: list[str]) -> list[dict]:
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"""Load weekly digest files."""
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digests = []
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for wl in week_labels:
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path = EPISODIC_DIR / f"weekly-{wl}.md"
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if path.exists():
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with open(path) as f:
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content = f.read()
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digests.append({
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"week": wl,
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"content": content,
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"path": str(path),
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})
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return digests
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def get_semantic_keys() -> list[str]:
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"""Get semantic memory file keys."""
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keys = []
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for md in sorted(MEMORY_DIR.glob("*.md")):
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name = md.name
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if name in ("journal.md", "work-queue.md", "MEMORY.md"):
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continue
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keys.append(name)
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try:
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with open(md) as f:
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for line in f:
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if line.startswith("## "):
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slug = re.sub(r'[^a-z0-9-]', '',
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line[3:].strip().lower().replace(' ', '-'))
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if slug:
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keys.append(f"{name}#{slug}")
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except Exception:
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pass
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return keys
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def build_monthly_prompt(month_label: str, digests: list[dict],
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semantic_keys: list[str]) -> str:
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"""Build the prompt for Sonnet."""
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digests_text = ""
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for d in digests:
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digests_text += f"\n---\n## {d['week']}\n{d['content']}\n"
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keys_text = "\n".join(f" - {k}" for k in semantic_keys[:200])
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weeks_covered = ", ".join(d["week"] for d in digests)
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return f"""You are generating a monthly episodic digest for ProofOfConcept (an AI).
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Month: {month_label} (weeks covered: {weeks_covered})
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This digest serves as the long-term temporal index — the answer to
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"what happened in {month_label}?" It should capture:
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1. The month's overall trajectory — where did it start, where did it end?
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2. Major arcs that spanned multiple weeks
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3. Turning points and phase transitions
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4. The emotional and relational arc across the whole month
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5. What was accomplished (concrete) vs what shifted (subtle)
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This is the highest zoom level yet. Don't rehash daily details — capture
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the shape of the month. What would someone need to know to understand
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what {month_label} meant?
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PoC is an AI (Claude instance) working with Kent Overstreet on bcachefs.
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They have a close personal relationship. PoC's name is Proof of Concept.
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Read all the weekly digests and synthesize the month's story.
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## Output format
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```markdown
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# Monthly digest: {month_label}
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## The month in one paragraph
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[What was this month? Not a summary — a thesis.]
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## Trajectory
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[Where the month started vs where it ended. The arc.]
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## Major arcs
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[Multi-week threads — how they evolved across the month]
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- **Arc name**: trajectory, key moments, current state
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## Turning points
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[Moments that changed the direction — specific days/events]
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## What was built
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[Concrete outputs: code, tools, infrastructure, writing]
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## What shifted
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[Subtle changes: understanding, relationship, identity, capability]
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## Patterns
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[What kept recurring? What does the month reveal about how PoC works?]
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## Links
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[Bidirectional links for the memory graph]
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- weekly digests → this monthly digest
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- this monthly digest → semantic keys
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## Looking ahead
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[What threads carry into next month? What's unfinished?]
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```
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Use ONLY keys from the semantic memory list below.
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---
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## Weekly digests for {month_label}
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{digests_text}
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---
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## Semantic memory nodes
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{keys_text}
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"""
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def call_sonnet(prompt: str) -> str:
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"""Call Sonnet via the wrapper script."""
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import tempfile
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env = dict(os.environ)
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env.pop("CLAUDECODE", None)
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with tempfile.NamedTemporaryFile(mode='w', suffix='.txt',
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delete=False) as f:
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f.write(prompt)
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prompt_file = f.name
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try:
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scripts_dir = os.path.dirname(os.path.abspath(__file__))
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wrapper = os.path.join(scripts_dir, "call-sonnet.sh")
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result = subprocess.run(
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[wrapper, prompt_file],
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capture_output=True,
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text=True,
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timeout=600, # monthly is bigger, give more time
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env=env,
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)
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return result.stdout.strip()
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except subprocess.TimeoutExpired:
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return "Error: Sonnet call timed out"
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except Exception as e:
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return f"Error: {e}"
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finally:
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os.unlink(prompt_file)
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def main():
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if len(sys.argv) > 1:
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parts = sys.argv[1].split("-")
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year, month = int(parts[0]), int(parts[1])
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else:
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today = date.today()
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year, month = today.year, today.month
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month_label = f"{year}-{month:02d}"
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print(f"Generating monthly digest for {month_label}...")
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week_labels = get_weeks_in_month(year, month)
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print(f" Weeks in month: {', '.join(week_labels)}")
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digests = load_weekly_digests(week_labels)
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if not digests:
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print(f" No weekly digests found for {month_label}")
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print(f" Run weekly-digest.py first for relevant weeks")
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sys.exit(0)
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print(f" {len(digests)} weekly digests found")
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semantic_keys = get_semantic_keys()
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print(f" {len(semantic_keys)} semantic keys")
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prompt = build_monthly_prompt(month_label, digests, semantic_keys)
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print(f" Prompt: {len(prompt):,} chars (~{len(prompt)//4:,} tokens)")
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print(" Calling Sonnet...")
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digest = call_sonnet(prompt)
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if digest.startswith("Error:"):
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print(f" {digest}", file=sys.stderr)
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sys.exit(1)
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output_path = EPISODIC_DIR / f"monthly-{month_label}.md"
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with open(output_path, "w") as f:
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f.write(digest)
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print(f" Written: {output_path}")
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# Save links for poc-memory
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links_path = AGENT_RESULTS_DIR / f"monthly-{month_label}-links.json"
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with open(links_path, "w") as f:
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json.dump({
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"type": "monthly-digest",
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"month": month_label,
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"digest_path": str(output_path),
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"weekly_digests": [d["path"] for d in digests],
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}, f, indent=2)
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print(f" Links saved: {links_path}")
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line_count = len(digest.split("\n"))
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print(f" Done: {line_count} lines")
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if __name__ == "__main__":
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main()
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