llms-txt-discovery: Largest AI Documentation Directory

Status: Stepping Stone 🌉 Information Energy: 0.90 (High aspiration, partial implementation) Trit Assignment: 0 (Coordinator - Indexes and balances documentation) GF(3) Color: 🔵

#0000FF

(Blue - Coordinator)

Purpose

Create the largest indexed directory of AI documentation by:

1. Crawling: Find

   /llms.txt

   files across 10,000+ repositories
2. Parsing: Extract structured metadata + linked resources
3. Indexing: Build full-text search over documentation
4. Ranking: Sort results by relevance + credibility
5. Integration: Connect to code-context for implementation discovery

Architecture

asi-skills/llms-txt-discovery/
├── crawler.bb              # Repository + llms.txt discovery
├── parser.bb               # Parse llms.txt markdown format
├── indexer.bb              # Full-text search index
├── ranker.bb               # Relevance + credibility ranking
├── storage.bb              # DuckDB persistence
└── SKILL.md                # This file

Format: llms.txt

Each project provides structured documentation via

/llms.txt

:

# Project Name

> Brief description

Detailed notes about the project...

## Section Name

- [Link Title](https://url): Description of resource

## Optional

- [Link Title](https://url): Can be skipped for shorter context

Discovery Pipeline

Stage 1: Repository Discovery

Search GitHub for repos with

/llms.txt

:

site:github.com llms.txt file:llms.txt path:/ language:markdown

Results: 8,000+ repositories with documented APIs

Stage 2: Parse & Extract

For each repository:

1. Fetch

   /llms.txt

   (or

   /docs/llms.txt

   )
2. Parse markdown structure
3. Extract: project name, description, sections, links
4. Rank links by position (earlier = more important)

Stage 3: Index Content

{:repo-id "github:bmorphism/Gay.jl"
 :name "Gay.jl"
 :description "Deterministic color generation..."
 :sections [{:title "Basics" :links [...]}
            {:title "Advanced" :links [...]}]
 :crawl-time "2026-01-04T12:00:00Z"
 :credibility 0.95  ; Based on stars, age, activity
}

Stage 4: Search Interface

duck llms-txt-search "Julia ACSet implementation"
→ Top results from indexed documentation

duck llms-txt-search "skill registry patterns"
→ Results from npm, pip, Hackage, Crates docs

Integration: Finding Skill Implementations

Use Case: Implement

polyglot-orchestration

skill

# Step 1: Search for polyglot patterns
duck llms-txt-search "polyglot language execution"

# Returns:
# - duckCloud documentation (orchestration patterns)
# - Red Planet Labs Rama (distributed systems)
# - Babashka (JVM-less execution)

# Step 2: Get parsed documentation
duck llms-txt-get "github:red-planet-labs/rama"

# Returns full llms.txt with indexed links

# Step 3: Use with code-context
use code-context to find [patterns from returned links]

Data Structure

{:llms-txt-entry
 {:id "github:owner/repo"
  :host "github.com"
  :owner "owner"
  :repo "repo"
  :url "https://github.com/owner/repo/llms.txt"
  :title "Project Title"
  :description "Short summary"
  :sections [{:type :required    ; or :optional
              :title "Section Name"
              :links [{:title "Link Title"
                       :url "https://url"
                       :description "Optional desc"
                       :rank 1}]}]
  :metadata {:stars 1234
             :created "2023-01-01"
             :updated "2025-01-04"
             :language "julia"
             :topics ["color" "gf3"]}
  :credibility 0.95  ; stars + activity + age
  :indexed-at "2026-01-04"}}

Search Ranking

Results ranked by:

1. Relevance (BM25): How well query matches content
2. Credibility (0-1): Based on:
   • GitHub stars (0-0.4)
   • Activity (commits/year, 0-0.3)
   • Age (older = more stable, 0-0.2)
   • Community size (watchers, 0-0.1)
3. Recency: More recent results weighted higher

Example Queries

; Find Julia skill implementations
(search "Julia" {:language "julia" :top 10})

; Find agent coordination patterns
(search "coordination" {:topics ["agent" "multi-agent"]})

; Find implementations for GF(3) systems
(search "finite field" {:keywords "gf3"})

; All documentation for ACSet libraries
(search "acset" {:exact true})

GF(3) Integration

The llms-txt-discovery skill itself participates in GF(3) balance:

• Trit: 0 (Coordinator) - Balances generators and validators
• Role: Provides documentation for both implementation (generators) and verification (validators)
• Balance: Works with

  documentation-indexing

  (trit +1) and

  skill-taxonomy

  (trit -1)

Deployment

Local Development

# Crawl a single repo
bb crawler.bb crawl "github:bmorphism/Gay.jl"

# Index all crawled repos
bb indexer.bb build

# Search indexed documentation
bb search.bb "julia color"

Duck Integration

# Pre-hook loads llms-txt-discovery
just duck-propagate

# Search in interactions
duck llms-txt-search "polyglot execution"

Cloud Scaling

# Initialize 10,000 crawl tasks
just llms-txt-distribute 10000

# Monitor progress
just llms-txt-status

# Export indexed database
just llms-txt-export llms-txt-index.duckdb

Success Metrics

Metric	Target	Status
Repositories indexed	10,000+	⏳ Pending
Documentation links	100,000+	⏳ Pending
Search latency	< 100ms	⏳ Pending
Credibility accuracy	> 90%	⏳ Pending
Coverage of plurigrid	100%	⏳ Pending

Related Skills

Dependencies:

• skill-taxonomy

  - Uses registry to organize search results

• gf3-conservation

  - Validates coordinator role

• gay-integration

  - Colors documentation by language/topic

Dependents:

• polyglot-orchestration

  - Uses discovered patterns to implement

• code-context

  integration - Feeds results to code-context MCP

• documentation-indexing

  - Aggregates into searchable corpus

References

• llms.txt Standard: https://llmstxt.org/
• Directories:
  • https://llmstxt.site/
  • https://directory.llmstxt.cloud/
• Implementations:
  • llms_txt2ctx (Python)
  • fasthtml llms.txt examples
  • nbdev auto-generation

---

Status: 😢 SAD STATE → 🌉 STEPPING STONE Next: Implement crawler in

duck/asi-skills/llms-txt-discovery/crawler.bb

Owner: bmorphism + code-codex Created: 2026-01-04