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Christian Nennemann d6beb9c0a0 v0.3.0: Gap-to-Draft pipeline, Living Standards Observatory, blog series

Gap-to-Draft Pipeline (ietf pipeline):
- Context builder assembles ideas, RFC foundations, similar drafts, ecosystem vision
- Generator produces outlines + sections using rich context with Claude
- Quality gates: novelty (embedding similarity), references, format, self-rating
- Family coordinator generates 5-draft ecosystem (AEM/ATD/HITL/AEPB/APAE)
- I-D formatter with proper headers, references, 72-char wrapping

Living Standards Observatory (ietf observatory):
- Source abstraction with IETF + W3C fetchers
- 7-step update pipeline: snapshot, fetch, analyze, embed, ideas, gaps, record
- Static GitHub Pages dashboard (explorer, gap tracker, timeline)
- Weekly CI/CD automation via GitHub Actions

Also includes:
- 361 drafts (expanded from 260 with 6 new keywords), 403 authors, 1,262 ideas, 12 gaps
- Blog series (8 posts planned), reports, arXiv paper figures
- Agent team infrastructure (CLAUDE.md, scripts, dev journal)
- 5 new DB tables, schema migration, ~15 new query methods

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-04 00:48:57 +01:00

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IETF Draft Analyzer — Project Instructions

What This Is

Python CLI tool (ietf) to track, categorize, rate, and map IETF Internet-Drafts on AI/agent topics. 361 drafts, 403 authors, 1,262 ideas, 12 gaps. Uses Claude for analysis, Ollama for embeddings, SQLite for storage.

Key Paths

Source: src/ietf_analyzer/
Database: data/drafts.db (NOT data/ietf_drafts.db)
Reports: data/reports/
Blog series: data/reports/blog-series/
Agent definitions: .claude/agents/
Team prompt: scripts/agent-team-prompt.md
Scripts: scripts/

Development Journal

Every agent and every session MUST log development milestones to data/reports/dev-journal.md.

This journal serves two purposes:

Track progress across sessions so nothing gets lost
Source material for the meta blog post about using Claude agent teams to build this project

What to Log

Append entries in this format:

### [DATE] [AGENT/SESSION] — [SHORT TITLE]

**What**: [What was done — features built, analyses run, posts written]
**Why**: [The reasoning or decision behind it]
**Result**: [Outcome, key numbers, links to artifacts]
**Surprise**: [Optional — anything unexpected, a lesson learned, a tool limitation hit]
**Cost**: [Optional — API tokens, time taken, model used]

Examples of What to Log

Pipeline runs (how many drafts processed, cost, any failures)
New features implemented (what, why, how it changed the analysis)
Blog posts drafted or revised (key editorial decisions)
Architectural decisions (why we structured something a certain way)
Agent coordination moments (when one agent's output changed another's direction)
Surprises in the data (unexpected findings that shifted the narrative)
Tool/infra issues (things that broke, workarounds found)

What NOT to Log

Routine file reads or searches
Minor formatting fixes
Anything already captured in git commits

Agent Team Conventions

When working as a team:

Architect designs the narrative arc and reviews everything for coherence
Analyst runs the pipeline, queries the DB, provides data packages
Coder implements new features following existing patterns (Click CLI, SQLite, rich output)
Writer produces the blog series from data packages and architectural guidance

All agents should:

Read scripts/agent-team-prompt.md for the full brief
Log milestones to data/reports/dev-journal.md
Write blog posts to data/reports/blog-series/
Save reusable scripts to scripts/
Follow existing code patterns (don't over-engineer)

Blog Series

7 posts planned in data/reports/blog-series/ (01 through 07), plus:

Post 8: "Agents Building the Agent Analysis" — Meta post about using Claude Code agent teams to analyze and write about IETF agent standards. The dev-journal.md is the source material for this post.

Code Conventions

CLI: Click commands in cli.py with @click.option() decorators
DB: Tables in db.py ensure_tables(), queries as methods on DraftDB
Reports: Report types in reports.py generate_report()
Always cache Claude API calls via llm_cache table
Use rich for console output
Save multi-step workflows as scripts in scripts/

Current Status (2026-03-03)

v0.2.0, 361 drafts (101 new, unprocessed)
101 new drafts need: analyze, authors, ideas, embed, gaps
Blog series: planned, not yet written
Agent team: defined in .claude/agents/, ready to launch

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