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Anon

Community
glebis
anon

De-identify a session transcript (file or folder) by redacting PII LOCALLY before any sharing or cloud use. Produces a redacted GREEN copy with unique reserved-sentinel placeholders ([CONFIDE_PERSON_0001], [CONFIDE_EMAIL_0001], [CONFIDE_DATE_0002]...) plus a counts-only stats summary, and a local secret <name>.map.json (0600, gitignored) that enables confide:rehydrate to restore real values after a cloud analysis. Use when the user says "anonymize this transcript", "redact PII", "de-identify session", "make safe to share", "strip personal data", "anonymize notes before sending to an LLM", or points at a transcript/folder that should be scrubbed. Local-only by default — raw text never leaves the machine; the map is the only artifact with originals and stays local; nothing printed is PII; human review is still required before sharing.

Overview

Publisherglebis
Repositoryclaude-skills
Skill nameanon
Stars
379
Forks
56
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by glebis on GitHub. Read the source before you install it.

Installation

Install the Anon AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/glebis/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/confide/skills/anon .claude/skills/anon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anon in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Anon on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Anon is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

confide:anon — local PII redaction

Redact personally identifying information from a transcript (or a whole folder) using the layered local stack in shared/confide_core.py: regex (emails / URLs / phones / IDs / dates) → Natasha (RU named entities) → local LLM (quasi-identifiers). Spans are interval-merged and replaced with placeholders. The result is a GREEN copy safe to review.

By default anon emits a reversible map: unique reserved-sentinel placeholders (the same EXACT value always becomes the same [CONFIDE_<TYPE>_<NNNN>], e.g. [CONFIDE_PERSON_0001], [CONFIDE_EMAIL_0001], [CONFIDE_DATE_0002]) plus a sibling <name>.map.json (structured: schema_version, doc_id, green_sha256, created, entries[]) mapping each placeholder to its original. The CONFIDE_ sentinel is reserved — a real transcript essentially never contains it, so there is no collision risk and rehydrate never touches ordinary prose like "Person 1". This is exact-value matching, not entity coreference: inflected forms (e.g. RU "Марина" vs "Марины") are SEPARATE placeholders (no lemmatized merge). That map is the secret — the ONLY artifact with originals; it stays local and enables confide:rehydrate to put real values back into a cloud analysis of the GREEN text (round-trip: redact → analyze the green → rehydrate locally). Use --no-map for the legacy non-reversible [TYPE] style (no map written).

Privacy invariants (do not violate)

  • Local-only. No cloud APIs. Raw text never leaves the machine.
  • By design, original PII is written ONLY to the local, 0600, gitignored <name>.map.json, which never leaves the machine. It is never printed, and never written to the GREEN copy (the GREEN holds placeholders only; the original file is read, never rewritten). The map is the SECRET — the one artifact with originals. A .gitignore covering *.map.json, *.view.html, and *.restored.md is written/updated in the output dir so these local-only artifacts can never be committed. If the output dir looks cloud-synced (iCloud / Dropbox / OneDrive / Google Drive), anon prints a WARNING that the secret map would be uploaded.
  • Counts only. stdout and the *.stats.json files carry counts (by type, by layer, redaction rate) — never PII values or redacted text dumps.
  • Human review still required. Redaction is a floor, not a guarantee. A human must read the GREEN copy before sharing. Pair with confide:red to check residual re-identification risk.

Run it

Run the script on a single file or a directory (processes every .md/.txt):

bash
python3 skills/anon/scripts/anon.py PATH

For each input it writes, next to the file (or into --out DIR):

  • <name>.green.md — the redacted text (the only thing safe to look at / share after review)
  • <name>.stats.json — counts only
  • <name>.map.json — the reversible map (secret; 0600; gitignored; local only). Skipped with --no-map. A .gitignore with *.map.json is also written/updated in the output dir.

Options:

  • --layers regex,natasha,llm — override which detection layers run (default from config). Use --layers regex for a fully offline, deterministic pass (no models/network).
  • --out DIR — write outputs to DIR instead of next to each input.
  • --dry-run — compute and print stats only; write no files.
  • --no-map — disable the reversible map; emit non-unique [TYPE] placeholders, no map.json.

Already-emitted *.green.md / *.stats.json / *.map.json are skipped, so a folder can be re-run safely.

After running

  1. Report the counts summary (types, layers, redaction rate) — never paste PII.
  2. Tell the user the GREEN copy still needs human review before sharing.
  3. Remind them the <name>.map.json is the secret (originals) — it stays local, never committed/shipped — and that confide:rehydrate uses it to restore real values into a cloud analysis of the GREEN text.
  4. Offer confide:red to probe what an attacker could still infer/link.

Setup

Layer availability (Natasha, local LLM via Ollama) and defaults come from config — run confide:setup first if Natasha/Ollama aren't installed. --layers regex always works offline.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Anon AI skill do?

De-identify a session transcript (file or folder) by redacting PII LOCALLY before any sharing or cloud use. Produces a redacted GREEN copy with unique reserved-sentinel placeholders ([CONFIDE_PERSON_0001], [CONFIDE_EMAIL_0001], [CONFIDE_DATE_0002]...) plus a counts-only stats summary, and a local secret <name>.map.json (0600, gitignored) that enables confide:rehydrate to restore real values after a cloud analysis. Use when the user says "anonymize this transcript", "redact PII", "de-identify session", "make safe to share", "strip personal data", "anonymize notes before sending to an LLM", o...

Why use Anon on TypingMind?

Because you install it once and use it with any model. Anon is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Anon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/claude-skills/tree/main/confide/skills/anon. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Anon?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Anon?

As many as you like. As long as a model supports skills, you can use Anon with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Anon AI skill free?

Yes. It is published on GitHub by glebis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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