redact-pii-for-tracinglisted
Install: claude install-skill ContextJet-ai/awesome-llm-observability
# PII-safe / compliant LLM tracing
Observability captures prompts and completions - which, for a finance or healthcare app, means you may be shipping account numbers, SSNs, or PHI to a third-party SaaS. That's a compliance incident waiting to happen. This skill adds tracing **without** the leak.
## Decision order (most-compliant first)
1. **Self-host the backend.** For regulated data, prefer an OSS platform you host: [Langfuse](https://github.com/langfuse/langfuse) (MIT), [Arize Phoenix](https://github.com/Arize-ai/phoenix), [Comet Opik](https://github.com/comet-ml/opik), [Helicone](https://github.com/Helicone/helicone). Data never leaves your VPC.
2. **If using a SaaS backend, redact before export.** Strip/mask PII in the span-processor pipeline so raw values never leave the process.
3. **Or don't capture content at all.** Many SDKs let you record *metadata only* (tokens, latency, model, span structure) and omit prompt/completion text. You lose content-level debugging but keep full cost/perf/shape observability.
## Redaction pipeline (SaaS path)
Insert redaction **between span creation and export** so it always runs:
1. **Detect** PII with a real detector - [Microsoft Presidio](https://github.com/microsoft/presidio), [LLM Guard](https://github.com/protectai/llm-guard), or a domain ruleset (account/card/IBAN/SSN patterns for finance). Don't rely on ad-hoc regex alone.
2. **Transform** - mask (`****1234`), hash (for join-ability without exposure), or drop the field. Choo