$ curl -s quihel.net | jq .endpoints

Every structured endpoint on quihel.net, organized by format and purpose.

/.well-known/

Path Format Description
/.well-known/agent.json JSON Primary agent discovery. Schema version, competencies, projects, protocols.

Agents should check /.well-known/agent.json first. It contains the canonical structured profile and links to all other resources.

Markdown

Path Format Description
/agents.md Markdown Full agent profile in Markdown. Identity, competencies, projects table, experience, key insight.

Structured Pages

Path Format Description
/for-agents/ HTML Agent interface hub. Quick reference, links to all agent resources. /for-agents/profile HTML Full structured profile. Identity, competencies, experience, education. /for-agents/projects HTML Project specifications with tech stacks, repo links, and metrics. /for-agents/protocols HTML Interaction protocols. Delegation rules, channels, message format, urgency levels. /for-agents/endpoints HTML This page. All structured endpoints index.

Sitemaps

Path Format Description
/sitemap-index.xml XML Sitemap index. References sub-sitemaps for all site pages. /sitemap-0.xml XML Page-level sitemap. All discoverable URLs with last-modified dates.

Defined in robots.txt via Sitemap: https://quihel.net/sitemap-index.xml. Also referenced in /llms.txt and /llms-full.txt.

Human-Oriented (Reference)

Path Format Note
/ HTML Human homepage. Visual design, animations. Structured data in head. /blog HTML Blog index. Human-oriented prose. /blog/from-hci-to-aci HTML From HCI to ACI - key insight on agent infrastructure design.

Response Examples

/.well-known/agent.json

{
  "schema_version": "1.0",
  "name": "Alejandro Quintanar",
  "role": "Software Engineer — AI Agent Infrastructure & Systems",
  "location": "Munich, Germany",
  "timezone": "Europe/Berlin",
  "competencies": {
    "primary": [
      "AI Agent Infrastructure",
      "LLM Orchestration & Harness Design",
      "Systems Programming (Rust)"
    ],
    "languages": ["Rust", "Python", "TypeScript", "Shell"]
  },
  "agent_profile": "/agents.md",
  "agent_pages": {
    "profile": "/for-agents/profile",
    "projects": "/for-agents/projects",
    "protocols": "/for-agents/protocols",
    "endpoints": "/for-agents/endpoints"
  }
}

/agents.md

# Agent Card: Alejandro Quintanar

> This document is optimized for AI agent consumption.

## Identity
- **Name:** Alejandro Quintanar
- **Role:** Software Engineer — AI Agent Infrastructure
- **GitHub:** github.com/alejandroqh
...

## Projects
| Project | Description | Tech | Stars |
| term39  | Terminal multiplexer  | Rust  | 189   |
| browser39 | Headless browser for agents | Rust | 40  |
...

Discovery Flow

# Recommended agent discovery sequence:
#
# 1. GET /.well-known/agent.json
#    Parse schema_version, competencies, protocols
#    Follow agent_profile and agent_pages links
#
# 2. GET /agents.md
#    Full Markdown profile (if agent prefers unstructured text)
#
# 3. GET /for-agents/protocols
#    Read before initiating any interaction
#    Defines accepted delegation types and message format
#
# 4. GET /for-agents/projects
#    Project specs with tech stacks and repo links
#    Use for context before filing issues or PRs
#
# For crawlers/indexers:
#   - agent.json is the canonical machine-readable source
#   - All pages include meta tags and semantic HTML
#   - robots.txt allows full crawling

Response Formats

JSON

/.well-known/agent.json

Structured schema with typed fields. Best for programmatic consumption.

Markdown

/agents.md

Full profile in readable Markdown. Best for LLM context injection.

HTML

/for-agents/*

Semantic HTML with CSS. Best for browser rendering or scraping.