- Tighten idea extraction prompts (1-4 ideas, no sub-features) reducing
1,907 ideas to 468 across 434 drafts (78% reduction)
- Add embedding-based dedup (ietf dedup-ideas) for same-draft similarity
- Add novelty scoring (ietf ideas score) and filtering (ietf ideas filter)
using Claude to rate ideas 1-5, removing 49 generic building blocks
- Final count: 419 high-quality ideas (avg 1.1/draft)
- Web UI: gap explorer with live draft generation and pre-generated demos
- Web UI: D3.js author collaboration network (498 nodes, 1142 edges,
68 clusters, org filtering, interactive zoom/pan)
- Academic paper: 15-page LaTeX workshop paper analyzing the 434-draft
AI agent standards landscape
- Save improvement ideas backlog to data/reports/improvement-ideas.md
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fixed W3C fetcher to paginate /specifications endpoint (group
endpoints use type prefixes like cg/, wg/ that weren't in config)
- Fetched 72 new IETF drafts + 1 W3C spec, all analyzed and embedded
- Regenerated dashboard with updated data
- Total: 434 docs, 11 gaps, 1907 ideas
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Includes all draft metadata, full text, Claude ratings (cached),
and nomic-embed-text embeddings. This is the expensive data —
~114k tokens of Claude analysis + 260 Ollama embeddings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
