mcpbeat

Audit

glebis/audit

Run a corpus-scale, STATS-ONLY PII audit over a folder of session transcripts LOCALLY and produce an aggregate report — counts by type and by layer, the per-session redaction-rate distribution, document lengths, and a coarse residual proxy. Use when the user says "audit my sessions", "scan folder for PII", "how much PII across these transcripts", "PII stats for my corpus", "is my redaction holding at scale", or points at a directory of transcripts and asks how much personal data it contains. Fully local — raw text never leaves the machine; the report carries ZERO PII values, transcript substrings, or filenames (only anonymized own-NN ids and counts), so the aggregates are safe to surface. Run it on a RED (raw) corpus to size the PII, or on a GREEN (already-redacted) corpus to check residual leakage.

5k tokens
context cost
the whole folder, loaded on every use
2
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
337
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/glebis/claude-skills --skill audit

What comes with it

17 648 bytes besides the instruction
scripts/audit.py

The instruction itself

7 sections, as written by the author

confide:audit — corpus-scale, stats-only PII audit

Measure how much PII lives across a whole folder of sessions, without ever exposing any

of it. The audit runs the layered LOCAL detector stack from shared/confide_core.py

(regex → Natasha → local LLM) over each file and emits only aggregates. This mirrors

the real_session_eval privacy contract: read text only in-process, emit counts.

Privacy invariants (do not violate)

  • Local-only. No cloud APIs. Raw transcript text never leaves the machine.
  • Stats-only output. The report (markdown + json + optional HTML) contains ONLY

counts and rates — never a transcript substring, never a detected PII value.

  • No filenames. Per-file rows are keyed by anonymized ids own-00, own-01, …

The original path/name is never written or printed. On an unreadable file, only the

index + exception class name is recorded.

  • Safe to surface. Because it is counts-only, the aggregate report can be shared

with a cloud agent or pasted into a chat. The PII stays on the machine.

What it reports

  • n_files, total / mean / min / max document chars
  • spans_by_type (PERSON, EMAIL, PHONE, DATE, …) and spans_by_layer (regex / natasha / llm)
  • overall_redaction_rate plus the per-session redaction-rate distribution

(min / median / mean / max)

  • a coarse residual proxy: spans still detectable after redaction — ~0 on a clean RED

corpus, a leakage signal on a GREEN corpus.

Run it

Point it at a folder (recurses, processes every .md/.txt; skips confide's own

*.green.md / *.stats.json outputs):

python3 skills/audit/scripts/audit.py FOLDER

Options:

  • --list paths.txt — also/instead audit absolute paths listed one per line.
  • --layers regex,natasha,llm — choose detection layers (default from config).

Use --layers regex for a fully offline, deterministic pass (no models/network).

  • --out report.md — report path; a report.json sibling is written alongside.
  • --html — also write a Tufte-ish dashboard (report.html, counts only).

Writes the markdown + json report (and optional HTML) and prints the aggregate summary —

all counts only.

RED vs GREEN

  • RED (raw) corpus: sizes the PII problem before any redaction.
  • GREEN (redacted) corpus: the residual proxy and remaining spans_by_type tell you

whether redaction is holding at scale.

After running

  • Report the aggregate summary (file count, span totals by type/layer, redaction-rate

distribution, residual proxy) — never paste PII.

  • If residual is non-trivial on a GREEN corpus, point the user at confide:anon to

re-redact and confide:red to probe re-identification risk.

Setup

Layer availability (Natasha, local LLM via Ollama) comes from config — run

confide:setup if they aren't installed. --layers regex always works offline.

How to use it

Copy the folder

Take glebis/audit from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.