Profiles

Profiles are the lens. They do not change what Vajra analyzes — they change how results are scored, ranked, and rendered.

The same document analyzed with --profile staff and --profile engineer produces the same underlying statistics. The difference is which observations surface, what language describes them, and what gets collapsed as noise.

The Scoring Model

Every observation in the analysis pipeline receives a composite importance score:

score = sum(weight_i * signal_i)

Six signal dimensions, each normalized to [0, 1]:

The profile defines the weights. The weights determine what rises to the top.

Built-in Profiles

staff

For: Non-technical operations staff who need “what is this and what stands out.”

Rendering: Plain language. No JSONPath. No technical jargon. Anomalies described in terms of business impact. Structural boilerplate hidden.

Section headers: “Document Summary,” “What Stands Out,” “What This Likely Means.”

vajra essence claim.json --profile staff

Document Summary:
  1 claim with 14 service lines, 1 patient, 2 diagnosis codes.

What Stands Out:
  - 3 service lines are missing allowed amounts.
  - Adjustment reason "CO-45" repeats across 8 of 14 lines.

What This Likely Means:
  - A subset of service lines appears incomplete.
  - The repeated adjustment code suggests a systematic issue.

engineer

For: Engineers who need schema details, structural analysis, and regression signals.

Rendering: Technical. JSONPath paths, type annotations, cardinalities. Diff-style output for drift. Fingerprints displayed.

vajra essence claim.json --profile engineer

Structure: 847 nodes, 23 distinct paths, max depth 6
Fingerprint (path set): a1b2c3d4...

Notable paths:
  $.claims[*].service_lines[*].allowed_amount
    null_rate: 0.214, entropy: 3.12, type: number (100%)

  $.claims[*].service_lines[*].adjustment.reason
    entropy: 1.56, cardinality: 4, dominant: "CO-45" (57.1%)

auditor

For: Auditors and compliance teams who need completeness, traceability, and consistency evidence.

Rendering: Formal vocabulary. Missing fields listed with full paths. Type inconsistencies documented with examples. Drift metrics with severity scores.

Concern relevance boosts: completeness, traceability, required-field absence.

vajra essence claim.json --profile auditor --format markdown

## Audit Essence

### Completeness Assessment
- **21.4%** of service lines are missing `allowed_amount`
  (3 of 14 service line records; field path: `$.claims[*].service_lines[*].allowed_amount`)
- Provider `taxonomy` field: absent
  (expected presence rate in comparable data: 94%)

### Type Consistency
- All paths exhibit 100% type stability. No type inconsistencies detected.

### Pattern Observations
- Adjustment reason code `CO-45` appears in 57.1% of service lines (8 of 14).
  This concentration exceeds typical variance for this field.

ai

For: Downstream LLM consumption. Maximum information density per token.

Rendering: Compact, machine-readable. Motifs collapsed aggressively. Repeated structures represented once with count. Explicit caveats on inferences.

vajra essence claim.json --profile ai --format compact-ai --budget 300

{"v":"vajra/1","n":847,"p":23,"d":6,"motif":{"p":"$.claims[0].service_lines[*]","c":14,"f":["procedure_code","charge_amount","allowed_amount","status","adjustment"]},"a":[{"p":"$.claims[0].service_lines[2,7,11].allowed_amount","t":"miss","s":4.2}],"drill":[{"p":"$.claims[*].service_lines","avail":["stats","anomalies"]}]}

fraud

For: Fraud and risk analysts who need suspicious patterns, outliers, and unusual combinations.

Rendering: Investigative framing. Outliers with full context. Benford’s Law departures. Suspicious value repetition. Unusual co-occurrence patterns.

Concern relevance boosts: numeric anomalies, identifier patterns, timing irregularities.

vajra essence claims_batch.ndjson --profile fraud

=== Fraud Screening Essence ===

Flagged Patterns:
  - charge_amount outlier: $47,250.00 in record 834
    (z_MAD = 6.3, population median = $285.00)
    This value is 165x the median. Review recommended.

  - Status value "voided" in record 419
    (seen once in 1,247 records, self-information = 10.3 bits)
    Extremely rare status. May warrant investigation.

  - Benford's Law departure for charge_amount leading digits
    Chi-squared: 14.2 (p = 0.028)
    Observed leading digit "1": 18% (expected: 30%)
    Observed leading digit "5": 22% (expected: 8%)
    Suggestive of non-natural distribution.

  - Identical charge_amount ($285.00) in 47 records from same provider
    Exact-value concentration: 3.8% of population
    Pattern is unusual for this field's typical variance.

health

For: Project and repository health assessment. Identifies risks, governance patterns, and sustainability signals.

Rendering: Assessment-oriented. Sections organized around risk, governance, and sustainability. Designed for repository and project analysis.

Section headers: “Key Risks,” “Governance Signals,” “Sustainability Assessment.”

vajra essence ./my-repo --profile health

Key Risks:
  - Bus factor: 2 contributors account for 78% of commits.
  - Fix rate declining: 31% of bugs fixed in March vs 18% in January.
  - Mean time to fix increasing: 2.3 days -> 4.1 days over 3 months.

Governance Signals:
  - Review coverage: 64% of PRs received at least one review.
  - Bot contribution: 33% of PRs from automated tools.
  - Consistent commit cadence: 4.2 commits/day (low variance).

Sustainability Assessment:
  - Moderate risk. High contributor concentration and declining fix rates
    suggest capacity constraints. Review coverage is below recommended
    thresholds for projects of this activity level.

Custom Profiles

Define custom profiles in TOML. Load with --config path/to/profiles.toml.

Full TOML Example

[profile.claims_review]
name = "claims-review"
description = "Internal review for claims processing teams"

[profile.claims_review.weights]
rarity = 0.15
instability = 0.20
entropy_signal = 0.10
structural_coverage = 0.10
anomaly_strength = 0.25
concern_relevance = 0.20

[profile.claims_review.rendering]
vocabulary = "plain"           # plain | technical | formal
show_paths = false             # hide JSONPath in output
show_scores = false            # hide numeric scores
motif_collapse_threshold = 3   # collapse motifs repeated > N times
anomaly_threshold = 3.5        # MAD z-score threshold for flagging

[profile.claims_review.concern_boosts]
paths_containing = ["denied", "adjustment", "override", "void"]
observation_types = ["missingness", "type_instability"]
boost_factor = 1.5

Loading Custom Profiles

vajra essence claim.json --profile claims-review --config ./profiles.toml

Multiple Custom Profiles in One File

[profile.claims_review]
name = "claims-review"
description = "Internal claims processing review"
# ... weights, rendering, boosts ...

[profile.vendor_audit]
name = "vendor-audit"
description = "Vendor data feed quality assessment"
# ... weights, rendering, boosts ...

[profile.ml_preprocessing]
name = "ml-preprocessing"
description = "Data quality check before ML pipeline ingestion"
# ... weights, rendering, boosts ...

Listing Available Profiles

vajra profiles

=== Built-in Profiles ===
  staff        Plain vocabulary, narrative rendering; emphasizes anomalies and structural coverage
  engineer     Technical vocabulary, list-based rendering; balanced scoring
  auditor      Formal vocabulary, completeness-focused; emphasizes instability and concern relevance
  ai           Compact terse rendering optimized for machine consumption
  fraud        Investigative framing; emphasizes outliers, rarity, and suspicious patterns
  health       Assessment-oriented; emphasizes risks, governance, and sustainability

=== Custom Profiles ===
  claims-review   Internal claims processing review

vajra profiles --config ./profiles.toml --format json

[
  {"name": "staff", "description": "...", "source": "built-in"},
  {"name": "engineer", "description": "...", "source": "built-in"},
  {"name": "auditor", "description": "...", "source": "built-in"},
  {"name": "ai", "description": "...", "source": "built-in"},
  {"name": "fraud", "description": "...", "source": "built-in"},
  {"name": "claims-review", "description": "Internal claims processing review", "source": "custom"}
]

Rendering Vocabulary

Deterministic Tie-Breaking

When two observations have identical composite scores, ties are broken by:

1. Path depth — shallower paths first (broader impact)
2. Lexicographic path order — alphabetical by wildcard path

This ensures identical scores always resolve in the same order, regardless of platform or run.