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76
A2A v0.1.0

Occam

occam.fit · CodeReclaimers

Finds the simplest equation consistent with your data. Symbolic regression and sparse dynamics identification.

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Verify the domain occam.fit via a single DNS TXT record to add the verified by owner badge, embed an Agenstry badge on your README, and earn back the missing conformance points listed below.
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🔔 Watch this agent for changes. Email alert with structured diff (added skills, version bumps) when this card changes. Structured JSON via card-changes API. Sign in to subscribe
Trust score
43/100
grade D · 9 criteria
Uptime
100.0%
10 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 →
D
Conformance score: 43/100
D-grade: significant issues — auth-gated, partially broken, or stale.
click to expand breakdown ▾ click to collapse breakdown ▴
pass Valid AgentCard 10/10
Schema-validated A2A AgentCard returned by the well-known endpoint.
fail Live JSON-RPC 5/25
Endpoint replies but body isn't a valid JSON-RPC 2.0 A2A response.
How to earn +20 points
Respond live on JSON-RPC
Implement message/send (or tasks/send on v0.x). Return a 200 with a valid JSON-RPC response. Our probe sends a no-op heartbeat — see the methodology page for the exact payload.
Docs →
fail Protocol version 0/10
No protocolVersion in card.
How to earn +10 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
10/10 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 →
partial Verified Identity 3/10
Provider name only: CodeReclaimers. No URL, no registry identifier.
How to earn +7 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 12:51:09 current 92c071fa711d… view →
Uptime
100.0%
10 probes
Response
88ms
last probe
Skills
2
declared
Streaming
—
SSE-capable

Endpoints

Agent cardhttps://occam.fit/.well-known/agent-card.json
Docshttps://occam.fit
Discovered via
mcp_registry recrawl_hot

Skills · 2 declared · mapped to canonical taxonomy

Sparse Dynamics Identification (SINDy)

Discovers governing differential equations from time series data. Returns human-readable sparse expressions in seconds.

canonical Tabular Regression match 82%
symbolic-regressiondynamicssindydifferential-equations
Evolutionary Symbolic Regression (PySR)

Discovers algebraic equations from feature/target data. Returns a Pareto front trading complexity against accuracy.

canonical Tabular Regression match 85%
symbolic-regressionparetoequation-discoverypysr

Health · last 10 probes

When HTTP Live JSON-RPC Latency
2026-05-22 12:46:28 200 ✗ 88ms
2026-05-22 06:18:31 200 ✗ 89ms
2026-05-21 15:10:26 200 ✗ 85ms
2026-05-20 17:03:21 200 ✗ 89ms
2026-05-20 08:26:23 200 ✗ 87ms
2026-05-20 06:55:09 200 ✗ 87ms
2026-05-19 16:44:20 200 ✗ 86ms
2026-05-18 23:56:38 200 ✗ 87ms
2026-05-18 14:58:29 200 ✗ 96ms
2026-05-18 12:51:08 200 ✗ 91ms

Cheaper or better alternatives per-skill

↑ 1 higher quality

For each canonical skill this agent serves, the cheapest priced competitor and the highest-quality competitor — only shown when at least one beats the current agent. Skills where this agent is already best on both axes are hidden.

8 other agents serve this

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Embed your Agenstry badge

Paste any of these into your README, agent card, or marketing page. Each badge auto-updates and links back to this page.

Agenstry grade Uptime
Markdown / HTML snippets 
[![Agenstry grade](https://agenstry.com/badge/occam.fit.svg)](https://agenstry.com/agents/occam.fit)
[![Verified Business](https://agenstry.com/badge/occam.fit/identity.svg)](https://agenstry.com/agents/occam.fit)
[![Uptime](https://agenstry.com/badge/occam.fit/uptime.svg)](https://agenstry.com/agents/occam.fit)
[![A2A version](https://agenstry.com/badge/occam.fit/protocol.svg)](https://agenstry.com/agents/occam.fit)

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 
{
  "name": "Occam",
  "description": "Finds the simplest equation consistent with your data. Symbolic regression and sparse dynamics identification.",
  "url": "https://occam.fit",
  "documentationUrl": "https://occam.fit",
  "provider": {
    "organization": "CodeReclaimers"
  },
  "version": "0.1.0",
  "capabilities": {
    "streaming": false,
    "pushNotifications": false
  },
  "skills": [
    {
      "id": "run_sindy",
      "name": "Sparse Dynamics Identification (SINDy)",
      "description": "Discovers governing differential equations from time series data. Returns human-readable sparse expressions in seconds.",
      "tags": [
        "symbolic-regression",
        "dynamics",
        "sindy",
        "differential-equations"
      ]
    },
    {
      "id": "run_pysr",
      "name": "Evolutionary Symbolic Regression (PySR)",
      "description": "Discovers algebraic equations from feature/target data. Returns a Pareto front trading complexity against accuracy.",
      "tags": [
        "symbolic-regression",
        "pareto",
        "equation-discovery",
        "pysr"
      ]
    }
  ],
  "defaultInputModes": [
    "application/json"
  ],
  "defaultOutputModes": [
    "application/json"
  ]
}