Quickstart

You have 60 seconds. Let us not waste them.

Install

From crates.io:

cargo install vajra-cli

From source:

git clone https://github.com/copyleftdev/vajra
cd vajra
cargo build --release
# Binary lands at ./target/release/vajra

Verify:

vajra --help

Four Commands That Prove the Point

1. Inspect a JSON document

Feed Vajra a medical claim. Get back its skeleton — every path, every type, every fingerprint.

vajra inspect claim.json

=== Document Metadata ===
  Total nodes:    847
  Max depth:      6
  Distinct paths: 23
  Raw size:       14208 bytes

=== Wildcard Paths ===
  PATH                                           TYPE      COUNT  INSTABILITY  NULL_RATE
  $                                              object        1       0.0000     0.0000
  $.claims                                       array         1       0.0000     0.0000
  $.claims[*]                                    object        1       0.0000     0.0000
  $.claims[*].patient.id                         string        1       0.0000     0.0000
  $.claims[*].diagnosis[*].code                  string        2       0.0000     0.0000
  $.claims[*].service_lines[*].procedure_code    string       14       0.0000     0.0000
  $.claims[*].service_lines[*].charge_amount     number       14       0.0000     0.0000
  $.claims[*].service_lines[*].allowed_amount    number       11       0.0000     0.2143
  $.claims[*].service_lines[*].status            string       14       0.0000     0.0000

=== Fingerprints ===
  Path set:    a1b2c3d4e5f6...
  Typed path:  f7e8d9c0b1a2...
  Shape:       1234abcd5678...

=== Domain Type Recognition ===
  $.claims[*].diagnosis[*].code           E11.9      ICD-10-CM
  $.claims[*].service_lines[*].procedure_code  99213  CPT

Every path. Every type. Every structural fingerprint. Domain-specific codes recognized automatically. Zero configuration.

2. Generate an essence

Compress the entire document into what matters, shaped for a specific audience.

vajra essence claim.json --profile staff

=== Essence (staff profile) ===

Document Summary:
  1 claim with 14 service lines, 1 patient, 2 diagnosis codes.
  Primary status: partially adjudicated.

What Stands Out:
  - 3 service lines are missing allowed amounts (lines 2, 7, 11).
    This field is present in 79% of service lines — its absence is notable.
  - Adjustment reason code "CO-45" repeats across 8 of 14 lines.
    Repetition at this frequency suggests a systematic pattern, not random variation.
  - 1 diagnosis structure differs from the other.
    The second diagnosis carries an extra "qualifier" field.

What This Likely Means:
  - Most of the claim is consistent and well-formed.
  - A subset of service lines appears incomplete or differently processed.
  - The repeated adjustment code points to a systematic issue.

Same command, different audience:

vajra essence claim.json --profile ai --format json --budget 500

{
  "vajra_essence": {
    "version": "0.1.0",
    "profile": "ai",
    "structure": {
      "root_type": "object",
      "total_nodes": 847,
      "distinct_paths": 23,
      "max_depth": 6
    },
    "dominant_motif": {
      "path": "$.claims[0].service_lines[*]",
      "count": 14,
      "shape_hash": "f2c1..."
    },
    "anomalies": [
      {"path": "$.claims[0].service_lines[2,7,11].allowed_amount", "type": "missing", "severity": 4.2},
      {"path": "$.claims[0].diagnosis[1]", "type": "structural_deviation", "severity": 3.1}
    ]
  }
}

3. Detect drift between versions

Compare yesterday’s API response to today’s. Find what changed and how much it changed.

vajra drift baseline.json current.json

Drift Report: baseline.json -> current.json
Structural similarity: 0.94 (Jaccard)

Added paths (2):
  $.response.metadata.processing_flags    [array of strings]
  $.response.metadata.api_version         [string]

Removed paths (0): none

Type changes (1):
  $.response.items[*].quantity            string -> number

Distribution shifts (1):
  $.response.items[*].status              JSD: 0.34
    before: {"active": 0.82, "pending": 0.15, "error": 0.03}
    after:  {"active": 0.61, "pending": 0.12, "error": 0.27}
    note: "error" rate increased 9x

Overall severity: MEDIUM

Two paths added. One type migrated. The error rate in status jumped ninefold. Vajra found all of it in one pass.

4. Surface anomalies

Find what deviates from the population — without defining what “normal” looks like.

vajra anomalies claims_batch.ndjson

=== Anomaly Report ===
Records analyzed: 1,247
Anomalies found:  8

Numeric outliers:
  $.claims[*].service_lines[*].charge_amount
    Record 834: value 47,250.00 (z_MAD = 6.3, median = 285.00, MAD = 195.00)
    Record 1102: value 0.01 (z_MAD = -4.8)

Rarity outliers:
  $.claims[*].status
    Record 419: value "voided" (self-information = 10.3 bits, seen 1/1247)

Structural deviations:
  Record 662: missing 4 paths present in 99%+ of records
    - $.claims[*].subscriber.group_number
    - $.claims[*].subscriber.member_id
    - $.claims[*].provider.npi
    - $.claims[*].provider.taxonomy

Type instability:
  $.claims[*].service_lines[*].quantity
    Records 88, 204, 917: string where number expected (instability = 0.002)

Eight anomalies across four dimensions. Every one carries its score, its evidence, and the statistical context that makes it interpretable.

What Just Happened

You did not configure a schema. You did not define rules. You did not train a model.

Vajra read the raw structure, computed its statistical profile, and surfaced what deviates from the population — deterministically, explainably, in seconds.

That is the point.

Next Steps

Philosophy — why Vajra exists and what it refuses to be
Commands — all 11 commands at a glance
Profiles — tune the lens for your audience
Algorithms — the mathematics behind every score

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