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ietf-draft-analyzer/README.md
Christian Nennemann 1ec1f69bee v0.3.0: Publication-ready release with blog site, paper update, and polish 
Release prep:
- Version bump to 0.3.0 (pyproject.toml, cli.py)
- Rewrite README.md with current stats (475 drafts, 713 authors, 501 ideas)
- Add CONTRIBUTING.md with dev setup and code conventions

Blog site:
- Add scripts/build-site.py (markdown → HTML with clean CSS, dark mode, nav)
- Generate static site in docs/blog/ (10 pages)
- Ready for GitHub Pages deployment

Academic paper (paper/main.tex):
- Update all counts: 474→475 drafts, 557→710 authors, 1907→462 ideas, 11→12 gaps
- Add false-positive filtering methodology (113 excluded, 361 relevant)
- Add cross-org convergence analysis (132 ideas, 33% rate)
- Add GDPR compliance gap to gap table
- Add LLM-as-judge caveats to rating methodology and limitations
- Add FIPA, IEEE P3394, W3C WoT to related work with bibliography entries
- Fix safety ratio to show monthly variation (1.5:1 to 21:1)

Pipeline:
- Fetch 1 new draft (475 total), 3 new authors (713 total)
- Fix 16 ruff lint errors across test files
- All 106 tests pass

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-08 17:54:43 +01:00

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# IETF Draft Analyzer
Track, categorize, rate, and map AI/agent-related IETF Internet-Drafts.
**475 drafts** analyzed (361 relevant after false-positive filtering) with **713 authors**, **501 extracted ideas**, **132 cross-org convergent ideas**, and **12 identified gaps** — spanning 2024 to March 2026.
## What This Does
The IETF is experiencing an unprecedented wave of standardization activity around AI agents. This tool provides a quantitative lens on that activity:
- **Fetches** draft metadata and full text from the IETF Datatracker API
- **Rates** each draft on 5 dimensions (novelty, maturity, overlap, momentum, relevance) using Claude
- **Embeds** drafts with Ollama for pairwise similarity and clustering
- **Extracts** discrete technical ideas and identifies landscape gaps
- **Analyzes** cross-organizational convergence (SequenceMatcher at 0.75 threshold)
- **Maps** the author collaboration network and organizational affiliations
- **Generates** markdown reports and a full web dashboard
- **Filters** false positives automatically (relevance-based + manual flagging)
## Quick Start
```bash
# Install
pip install -e .
# Set your API key (or add to .env file)
export ANTHROPIC_API_KEY=sk-ant-...
# Fetch drafts from IETF Datatracker
ietf fetch
# Rate all unrated drafts (--cheap uses Haiku for lower cost)
ietf analyze --all
ietf analyze --all --cheap # ~10x cheaper with Haiku
# Generate embeddings (requires Ollama running locally)
ietf embed
# Extract technical ideas
ietf ideas --all --cheap --batch 5
# Analyze cross-org convergence
ietf ideas convergence
# Identify gaps in the landscape
ietf gaps
# Fetch author data
ietf authors --fetch
# Generate reports
ietf report overview
ietf report landscape
ietf report authors
# Launch the web dashboard
./scripts/run-webui.sh
```
## Web Dashboard
A full interactive dashboard at `http://127.0.0.1:5000`:
```bash
./scripts/run-webui.sh
# or: FLASK_APP=src/webui/app.py flask run
```
| Page | What it shows |
|------|---------------|
| **Overview** | Stat cards, score histogram, category radar, submission timeline |
| **Draft Explorer** | Searchable/filterable/sortable table with category pills and score badges |
| **Draft Detail** | Score ring, dimension bars, ideas, references, linked authors |
| **Ratings** | Score distributions, box plots, category radar, novelty vs maturity scatter |
| **Landscape** | t-SNE embedding map, quality quadrants |
| **Authors** | Co-authorship force-directed graph, organization charts |
| **Ideas** | Extracted ideas grouped by type with search |
| **Gaps** | Gap cards sorted by severity with related drafts |
| **Citations** | RFC cross-reference graph |
| **Similarity** | Draft similarity network |
| **Timeline** | Submission trends over time |
| **Monitor** | Pipeline health, API costs, processing status |
Charts are interactive (Plotly.js). GDPR-compliant analytics (no cookies, daily-salted IP hashing).
## Blog Series
An 8-post analysis series in `data/reports/blog-series/`:
1. **The Gold Rush** — Growth from 9 drafts to 9.3% of all IETF submissions
2. **Who Writes the Rules** — Huawei's 16%, geopolitical dynamics, team blocs
3. **The OAuth Wars** — 14 competing OAuth proposals, fragmentation costs
4. **What Nobody Builds** — The safety deficit (4:1 ratio), 12 identified gaps
5. **Where Drafts Converge** — 132 cross-org convergent ideas, implicit consensus
6. **The Big Picture** — Architectural vision, EU AI Act implications
7. **How We Built This** — Methodology, cost ($9-15), limitations
8. **Agents Building the Agent Analysis** — Meta post on using Claude agent teams
## Key Findings
- **Safety deficit**: ~4:1 ratio of capability-building to safety proposals (varies 1.5:1 to 21:1 monthly)
- **Extreme fragmentation**: 155 competing A2A protocols, 42 overlap clusters
- **Organizational concentration**: Huawei ~16% of all drafts, Chinese orgs ~40%
- **Cross-org convergence**: 132 ideas (33%) independently proposed by multiple organizations
- **12 gaps identified**: 2 critical (behavior verification, human override), 5 high, 5 medium
- **Top-rated drafts**: Safety-focused proposals score highest (VOLT 4.75, DAAP 4.75)
## CLI Commands
### Core Pipeline
| Command | Description |
|---------|-------------|
| `ietf fetch` | Fetch AI/agent drafts from IETF Datatracker |
| `ietf analyze --all [--cheap] [--dry-run]` | Rate drafts using Claude |
| `ietf embed [--dry-run]` | Generate semantic embeddings via Ollama |
| `ietf ideas --all [--cheap] [--batch N] [--dry-run]` | Extract technical ideas |
| `ietf ideas convergence [--threshold 0.75]` | Cross-org convergence analysis |
| `ietf ideas dedup` | Deduplicate similar ideas |
| `ietf gaps [--dry-run]` | Identify landscape gaps |
| `ietf authors --fetch` | Fetch author/affiliation data |
### Exploration
| Command | Description |
|---------|-------------|
| `ietf list` | List tracked drafts |
| `ietf show <name>` | Show detailed info for a draft |
| `ietf search <query>` | Full-text search (FTS5) |
| `ietf similar <name>` | Find similar drafts by embedding similarity |
| `ietf clusters` | Find clusters of near-duplicate drafts |
| `ietf compare <name1> <name2>` | Compare drafts for overlap |
| `ietf authors` | Top authors and draft counts |
| `ietf network` | Organizational collaboration network |
### Reports (`ietf report`)
| Command | Description |
|---------|-------------|
| `ietf report overview` | Sortable table of all rated drafts |
| `ietf report landscape` | Category-grouped view with rankings |
| `ietf report authors` | Top authors, organizations, collaboration |
| `ietf report ideas` | Ideas by type, most common, unique |
| `ietf report gaps` | Gap analysis with severity ratings |
| `ietf report timeline` | Monthly submission trends |
| `ietf report overlap-matrix` | Similar pairs and cross-category matrix |
## Rating System
Each draft is scored 1-5 on five dimensions by Claude (LLM-as-judge, see [methodology](data/reports/methodology.md) for caveats):
| Dimension | What it measures |
|-----------|-----------------|
| **Novelty** | Originality relative to existing standards |
| **Maturity** | Completeness of specification |
| **Overlap** | Redundancy with other drafts (5 = heavily overlapping) |
| **Momentum** | Community engagement, revisions, adoption |
| **Relevance** | Importance to the AI/agent ecosystem |
**Important**: Ratings are generated from abstracts and partial full text without human calibration. They should be treated as relative rankings, not absolute quality measures.
## Tech Stack
- **Python 3.11+** with Click CLI
- **SQLite** with FTS5 full-text search and WAL mode
- **Anthropic Claude** (Sonnet/Haiku) for analysis, rating, idea extraction, gap analysis
- **Ollama** (nomic-embed-text) for local embeddings and similarity
- **Flask** with Jinja2 for the web dashboard
- **Plotly** for interactive visualizations
- **NumPy/SciPy/scikit-learn** for similarity computation and clustering
## Project Structure
```
src/ietf_analyzer/
cli.py # Click CLI entry point (~30 commands)
fetcher.py # IETF Datatracker API client
analyzer.py # Claude-based analysis (rating, ideas, gaps)
embeddings.py # Ollama embeddings + similarity + clustering
db.py # SQLite with FTS5 (8 tables)
models.py # Author, Draft, Rating dataclasses
reports.py # Markdown report generation
authors.py # Author network analysis
search.py # Hybrid FTS5 + embedding search
classifier.py # Two-stage Ollama classifier
readiness.py # Draft readiness scoring
config.py # Configuration
src/webui/
app.py # Flask application (20 API endpoints)
data.py # Data access layer with TypedDicts
auth.py # Admin authentication
analytics.py # GDPR-compliant pageview tracking
templates/ # Jinja2 templates (23 pages)
data/
drafts.db # SQLite database
reports/ # Generated reports + blog series
.cache/ # Similarity matrix cache
paper/
main.tex # arXiv paper
```
## Database Schema
| Table | Purpose | Records |
|-------|---------|--------:|
| `drafts` | Draft metadata + full text | 475 |
| `ratings` | 5-dimension ratings + summary + false_positive flag | 475 |
| `embeddings` | Semantic vectors (nomic-embed-text, 768-dim) | 475 |
| `llm_cache` | Claude API response cache (SHA-256 dedup) | ~1,500 |
| `authors` | Person records from Datatracker | 713 |
| `draft_authors` | Author-draft relationships | ~1,400 |
| `ideas` | Extracted + deduplicated technical ideas | 501 |
| `gaps` | Gap analysis results | 12 |
## Cost
Full pipeline for 475 drafts: ~$9-15 USD total
- Sonnet for rating + gap analysis (~$3)
- Haiku for bulk idea extraction (~$1)
- Ollama embeddings: free (local)
## License
MIT — see [LICENSE](LICENSE)
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