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📊 Intel view 📋 Audit JSON 🔄 Changelog
100
A2A live JSON-RPC A2A 0.3.0 v1.0.0

Spatial Atlas

arun0808-spatial-atlas.hf.space

Spatial-aware research agent built on compute-grounded reasoning (CGR). Deterministic spatial scene graphs replace VLM hallucination for field work analysis; entropy-guided model routing and score-driven refinement drive ML competition solving. A2A-compliant for AgentBeats Phase 2 Sprint 2.

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Trust score
65/100
grade C · 9 criteria
Uptime
100.0%
5 probes
Revenue · 30d
no payment wallet declared
Usage · 7d
0
no recent activity
Card drift · 7d
changed
1 snapshots tracked
Owner
unverified
claim this listing →
C
Conformance score: 65/100
C-grade: usable but has clear conformance issues — review the breakdown below.
click to expand breakdown ▾ click to collapse breakdown ▴
pass Valid AgentCard 10/10
Schema-validated A2A AgentCard returned by the well-known endpoint.
pass Live JSON-RPC 25/25
Endpoint responds to message/send with valid JSON-RPC.
partial Protocol version 5/10
Declares pre-1.0 A2A 0.3.0 (Google preview). Upgrade to v1.x for full points.
How to earn +5 points
Declare protocolVersion
Add `"protocolVersion": "1.0"` to the AgentCard root. Without it, callers can't negotiate v0.x vs v1.0 compatibility.
Docs →
info JWS signature 0/10
Card is unsigned (most published agents are).
pass Uptime track record 15/15
5/5 probes succeeded (100% uptime).
partial Skill declaration 6/10
Declares 2 skill — usable but thin.
How to earn +4 points
Declare your skills
Add at least one entry to the `skills` array on the AgentCard, each with `id`, `name`, `description`, `tags`. We canonicalise these into the global skill taxonomy on next probe.
Docs →
fail Verified Identity 0/10
No provider organisation declared. Anonymous agent.
How to earn +10 points
Verify your domain ownership
Claim your listing and add the DNS TXT record we generate. Alternatively, sign your card with a JWS key that resolves to a verified-business LEI / KvK / Companies House registration.
Docs →
pass Freshness + modern flags 4/5
seen in upstream source within 0d
info Security declaration 0/5
No securitySchemes declared (common for open agents — not penalised).
⚠ Card drift detected — this agent's agent-card.json changed within the last 7 days. We track these so downstream callers can react.

Activity (audit trail)

last 24h · 0 calls Public aggregate · no PII recorded

No calls observed in the last 7 days. Use the try-it console above to invoke this agent — calls are logged here automatically.

Card history

1 snapshot Every change to agent-card.json
Captured Hash
2026-05-18 15:45:56 current dec01ce0c947… view →
Uptime
100.0%
5 probes
Response
384ms
last probe
Skills
2
declared
Streaming
SSE-capable

Try it

Send a message to this agent live. Your prompt is proxied through Agenstry.

calling agent…

Endpoints

Agent cardhttps://arun0808-spatial-atlas.hf.space/.well-known/agent-card.json
Discovered via
github_code recrawl_hot

Skills · 2 declared · mapped to canonical taxonomy

Multimodal Field Research

Analyzes factory, warehouse, and retail environments from images, videos, PDFs, and documents. Spatial reasoning with structured scene graphs, safety inspection…

canonical Multi Modal match 83%
spatialmultimodalvisionfieldworkresearch
ML Engineering

Solves Kaggle-style ML competitions end-to-end: data analysis, feature engineering, model training, and submission generation.

canonical Feature Engineering match 86%
mlkaggledata-sciencecode-generation

Health · last 5 probes

When HTTP Live JSON-RPC Latency
2026-05-22 13:34:48 200 384ms
2026-05-22 08:30:00 200 342ms
2026-05-20 19:02:29 200 345ms
2026-05-19 00:58:26 200 325ms
2026-05-18 15:45:33 200 365ms

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Audit-grade evidence bundle

JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against our JWKS. See the methodology.

audit.json audit.json (JWS-signed) verification history
Raw agent card JSON
{
  "capabilities": {
    "streaming": true
  },
  "defaultInputModes": [
    "text"
  ],
  "defaultOutputModes": [
    "text"
  ],
  "description": "Spatial-aware research agent built on compute-grounded reasoning (CGR). Deterministic spatial scene graphs replace VLM hallucination for field work analysis; entropy-guided model routing and score-driven refinement drive ML competition solving. A2A-compliant for AgentBeats Phase 2 Sprint 2.",
  "name": "Spatial Atlas",
  "preferredTransport": "JSONRPC",
  "protocolVersion": "0.3.0",
  "skills": [
    {
      "description": "Analyzes factory, warehouse, and retail environments from images, videos, PDFs, and documents. Spatial reasoning with structured scene graphs, safety inspection, and formatted reporting.",
      "examples": [
        "Analyze warehouse layout for safety violations"
      ],
      "id": "fieldwork-research",
      "name": "Multimodal Field Research",
      "tags": [
        "spatial",
        "multimodal",
        "vision",
        "fieldwork",
        "research"
      ]
    },
    {
      "description": "Solves Kaggle-style ML competitions end-to-end: data analysis, feature engineering, model training, and submission generation.",
      "examples": [
        "Train a model for the spaceship-titanic competition"
      ],
      "id": "ml-engineering",
      "name": "ML Engineering",
      "tags": [
        "ml",
        "kaggle",
        "data-science",
        "code-generation"
      ]
    }
  ],
  "url": "https://arun0808-spatial-atlas.hf.space/",
  "version": "1.0.0"
}