# Chitragupta

> Structured logging, audit trails, and request tracing standards. Use when adding logging to any service, implementing audit requirements, tracing requests across services, or reviewing what gets logged and how.

- Skill: `arjuncrevathi/chitragupta` (Agent Skill)
- Install (CLI): `npx skillmds@latest add arjuncrevathi/chitragupta`
- Raw SKILL.md: https://api.skillmd.com/api/skills/arjuncrevathi/chitragupta/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: arjuncrevathi (https://skillmd.com/u/arjuncrevathi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/arjuncrevathi/chitragupta

---


# Chitragupta — Keeper of Records (Logging & Audit)

Chitragupta records every deed: every request, every state change, every call — accurately and nothing more.

## Structured logging only

- All logs are structured JSON. No bare `print()`, no f-string log soup.
  - Python: `structlog` with JSON renderer. Bind context once (`structlog.contextvars`), not per-line.
  - Node/TS: `pino`. Use child loggers for per-request context.
- One event per log line. Put data in fields, not in the message string: `log.info("order_created", order_id=..., amount=...)`.
- Log to stdout/stderr; the platform ships them. Never write log files inside containers.

## Log levels

- `DEBUG`: developer diagnostics, off in prod by default.
- `INFO`: normal business events (request completed, job finished).
- `WARN`: degraded but handled (retry succeeded, fallback used).
- `ERROR`: something failed and needs attention. Always include the exception/stack.
- Never log expected control flow at ERROR, and never swallow exceptions at DEBUG.

## Correlation & tracing

- Every inbound request gets a correlation/trace ID (accept `X-Request-ID`/W3C `traceparent`, or generate one).
- Propagate the ID to every downstream call, queue message, and background job. Include it in every log line and error response.
- Prefer OpenTelemetry trace context over homegrown IDs (see `surya`).

## What never gets logged

- Secrets, API keys, tokens, passwords, session cookies, `Authorization` headers.
- PII (emails, phone numbers, addresses) unless explicitly required — then mask (`a***@gmail.com`).
- Full request/response bodies by default; log sizes and status instead.
- Add a redaction processor (structlog processor / pino `redact` paths) so this is enforced in code, not by discipline.

## Log at boundaries

- Request in: method, path, correlation ID. Response out: status, duration ms.
- Every external call (DB excepted): target, duration, status, retry count.
- Background jobs: start, finish/fail, duration, items processed.

## Audit trail

- Every state-changing action (create/update/delete, permission change, login) writes an audit record: who (user/service ID), what (action + entity + before/after where feasible), when (UTC timestamp), from where (IP/origin).
- Audit records are append-only — separate table or stream, never edited or deleted by application code.
- Define retention up front: app logs 30 days hot, audit logs ≥ 1 year (or per compliance requirement).

## AI-native specifics

- Log every LLM call with: prompt template version, model ID, input/output token counts, latency ms, finish reason, cost estimate.
- Redact or hash raw user content in LLM logs where policy requires; store full transcripts only in a designated, access-controlled store.
- Log tool-call sequences for agent runs so failures can be replayed step by step.

## Before shipping logging — checklist

- [ ] JSON structured output, correct levels, one event per line
- [ ] Correlation ID present and propagated end to end
- [ ] Redaction in place — no secrets, tokens, or unmasked PII
- [ ] State-changing actions produce audit records (who/what/when)
- [ ] LLM calls log model, prompt version, tokens, latency