IETF Draft Analyzer v0.1.0 — track, categorize, and rate AI/agent drafts

Python CLI tool that fetches AI/agent-related Internet-Drafts from the IETF
Datatracker, rates them using Claude, generates embeddings via Ollama for
similarity/clustering, and produces markdown reports.

Features:
- Fetch drafts by keyword from Datatracker API with full text download
- Batch analysis with Claude (token-optimized, responses cached in SQLite)
- Embedding-based similarity search and overlap cluster detection
- Reports: overview, landscape by category, overlap clusters, weekly digest
- SQLite with FTS5 for full-text search across 260 tracked drafts

Initial analysis of 260 drafts reveals OAuth agent auth (13 drafts) and
agent gateway/collaboration (10 drafts) as the most crowded clusters,
while AI safety/alignment is underserved with the highest quality scores.

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

master-prompt.md

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+# IETF Draft Analyzer — Master Prompt
+
+## Vision
+
+A tool to **track, categorize, compare, and rate** the growing flood of IETF Internet-Drafts related to AI and autonomous agents — helping an informed reader stay on top of novel ideas, spot overlaps, and identify gaps worth filling.
+
+## Problem
+
+The IETF is seeing a surge of AI/agent-related drafts. Many overlap significantly, some introduce genuinely novel concepts, and it's hard to maintain a mental map of the landscape. Manual tracking doesn't scale.
+
+## Primary Data Sources
+
+- **Recent drafts:** https://datatracker.ietf.org/doc/recent
+- **Keyword search (e.g. "agent"):** https://datatracker.ietf.org/doc/search?name=agent&sort=&rfcs=on&activedrafts=on&by=group&group=
+- **Datatracker API:** https://datatracker.ietf.org/api/ (machine-readable metadata)
+- Additional keyword searches: `ai`, `llm`, `autonomous`, `machine-learning`, `ml`, `intelligent`, `inference`, etc.
+
+## Core Features
+
+### 1. Draft Ingestion & Tracking
+- Fetch drafts from Datatracker (API + scraping where needed)
+- Track new drafts, revisions, and status changes over time
+- Store metadata: title, authors, abstract, WG, dates, status, keywords
+- Download and parse full draft text for deeper analysis
+
+### 2. Categorization & Tagging
+- **Auto-categorize** drafts into topic clusters, e.g.:
+  - Agent-to-agent communication protocols
+  - AI safety / guardrails / alignment in networking
+  - ML-based traffic management / optimization
+  - Autonomous network operations (intent-based, closed-loop)
+  - Identity / authentication for AI agents
+  - Data formats / semantics for AI interop
+  - Policy / governance / ethical frameworks
+- Support user-defined tags and manual overrides
+- Detect which IETF working groups / areas are involved
+
+### 3. Overlap & Novelty Detection
+- **Similarity analysis** between drafts (abstract-level and full-text)
+  - Semantic similarity (embeddings-based)
+  - Structural similarity (do they define similar mechanisms?)
+- Flag clusters of highly overlapping drafts
+- Highlight **novel contributions** — ideas that don't appear elsewhere
+- Track how ideas evolve across draft revisions
+
+### 4. Brief Rating / Assessment
+- Per-draft rating along dimensions like:
+  - **Novelty** — How new/unique is the core idea?
+  - **Maturity** — How complete and well-specified is the draft?
+  - **Overlap** — How much does it duplicate existing work?
+  - **Momentum** — Author track record, WG adoption, revision frequency
+  - **Relevance** — How central is it to the AI/agent topic?
+- Generate a short (2–4 sentence) AI summary + assessment for each draft
+- Optional: composite score for quick sorting
+
+### 5. Overview & Visualization
+- **Dashboard view** — sortable/filterable table of tracked drafts
+- **Landscape map** — visual clustering of drafts by topic similarity
+- **Timeline** — when drafts appeared, how the space is evolving
+- **Diff view** — compare two drafts side-by-side (key claims, mechanisms)
+- **Overlap matrix** — heatmap showing which drafts cover similar ground
+
+### 6. Staying Informed
+- **Watch list** — mark drafts of special interest
+- **Change alerts** — notify on new drafts matching keywords, new revisions, status changes
+- **Periodic digest** — weekly/on-demand summary of what's new in the space
+- **Gap analysis** — suggest areas not yet covered by any draft
+
+## Stretch / Nice-to-Have Features
+
+- **Idea extraction** — pull out discrete technical ideas/mechanisms from each draft, track them independently
+- **Cross-reference with RFCs** — link drafts to existing standards they build on or conflict with
+- **Author network** — who collaborates with whom, which orgs are active
+- **Meeting tracking** — link to relevant IETF meeting agenda items, minutes, slides
+- **Export** — generate reports (markdown, PDF) for sharing or personal reference
+- **Personal notes** — attach annotations and thoughts to any draft
+
+## Design Principles
+
+- **Local-first** — runs on the user's machine, data stored locally
+- **CLI + optional web UI** — start simple (CLI/TUI), add a local web dashboard later
+- **LLM-assisted but transparent** — use AI for summarization/rating but always show reasoning
+- **Incremental** — can start with a small set of drafts and scale up
+- **Open data** — all source data comes from public IETF resources
+
+## LLM Strategy
+
+Two viable options — use the best tool for each job:
+
+### Option A: Claude API (via subscription)
+- **Pro:** Superior reasoning for summarization, rating, novelty detection, and comparative analysis of dense technical text. IETF drafts are complex — quality matters here.
+- **Pro:** Better at structured output (JSON ratings, consistent categorization)
+- **Con:** API costs / rate limits (though subscription helps)
+- **Best for:** Summarization, rating, categorization, overlap analysis, gap detection
+
+### Option B: Local Ollama
+- **Pro:** Free, private, no rate limits, works offline
+- **Pro:** Good enough for embeddings (e.g. `nomic-embed-text`, `mxbai-embed-large`)
+- **Con:** Smaller models struggle with nuanced technical assessment of IETF-grade content
+- **Best for:** Embeddings for similarity/clustering, bulk preprocessing, quick triage
+
+### Recommended: Hybrid Approach
+| Task | Model |
+|------|-------|
+| **Embeddings** (similarity, clustering) | Ollama local (`nomic-embed-text` or similar) |
+| **Quick triage** (is this draft AI-related?) | Ollama local (fast, cheap) |
+| **Summarization & rating** | Claude (better quality on technical text) |
+| **Overlap/novelty analysis** | Claude (needs strong reasoning) |
+| **Gap analysis & insights** | Claude (creative + analytical) |
+
+This keeps costs down (embeddings are the bulk operation) while using Claude where quality actually matters. The tool should support both backends with a simple config switch, so you can fall back to all-local or all-Claude as needed.
+
+## Decisions Made
+
+- **Tech stack:** Python
+- **Storage:** SQLite (single DB file, FTS5 for full-text search)
+- **Scope:** AI/agent-focused first, generalizable later
+- **Interface:** CLI + Markdown report output (v1)
+- **LLM:** Hybrid — Ollama for embeddings/triage, Claude for analysis/rating