A workflow for having people label personally identifying information (PII) in text and creating a trusted reference set of correct labels. It also measures how consistently the annotators agree using agreement scores such as kappa.

Use it to label PII in synthetic or consented transcripts, export each annotator's labels, measure disagreements, and draft an agreed reference dataset.

It provides a controlled way to check whether people apply the same labeling rules, while keeping sensitive transcript text and names local.

annotate — Skill for Claude Code by glebis

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add glebis/claude-skills --skill annotate

Clone the repo

git clone --depth 1 https://github.com/glebis/claude-skills

Made for: Claude Code.

Or install confide, the plugin that ships this one along with the rest of its 8 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/glebis/claude-skills/annotate.svg)](https://agentmods.dev/skills/glebis/claude-skills/annotate)

Your own site

<a href="https://agentmods.dev/skills/glebis/claude-skills/annotate"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/annotate.svg" alt="Measured on agentmods" height="20"></a>

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,365 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01365
Opus 5	$0.00000	$0.00682
Sonnet 5	$0.00000	$0.00273
Haiku 4.5	$0.00000	$0.00136

Measured 8d ago against content hash 66b985478e32, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

annotate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/gold_to_labels.py, scripts/score_iaa.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

confide/skills/annotate/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

confide:annotate — human PII gold set + inter-annotator agreement

Humans label PII spans in a transcript; you measure how much they agree (κ) and draft an adjudicated gold from their labels. Annotators are first-class here — most of this skill is plain instructions FOR a person doing the labelling, plus a coordinator path to score it.

Privacy invariants (do not violate)

Synthetic or consented data only. Never load a real client transcript the person did not consent to share. When in doubt, anonymize first (confide:anon) and annotate the GREEN copy.
Names stay local. The annotator's labels (which contain real surface text spans) live in their browser and the exported JSON file on their own machine. Collect label files locally.
Nothing PII is re-shared. Only κ / F1 / disagreement clusters travel between people if needed. The transcript text and the original PII are never re-distributed by this skill.

Bundled assets

assets/annotator.html — zero-install browser annotation tool (EN/RU, runs offline).
references/codebook.md — the labelling rulebook (10 PII types, direct/quasi, harm).
references/tool-guide.md — how to drive the tool + scorer step by step.
scripts/score_iaa.py — Cohen's/Fleiss' κ, span-F1, disagreement queue, draft gold (stdlib).
scripts/gold_to_labels.py — turn an existing gold into a "reference annotator" to test solo.

FOR THE ANNOTATOR (no coding needed)

Open the tool. Double-click assets/annotator.html (or open it in Chrome/Firefox/ Safari). It runs entirely in your browser — nothing is uploaded; labels stay on your machine until you Export.
Read the rules. Open references/codebook.md first. It defines the 10 types (PERSON, LOCATION, ORG, PHONE, EMAIL, ID, DATE, MEDICATION, AGE, PROFESSION), what counts as a span (the minimal identifying text), and direct vs. quasi-identifier.
Set your annotator id and load the transcript in the tool (e.g. A, B, or your name). Use only synthetic or consented text.
Label every PII span. Select the minimal text that identifies a real person (the client or third parties they mention) and assign its type. Record direct/quasi, entity id, role, and harm as the codebook describes. Do not rewrite or redact — only label.
When unsure, log it — don't guess silently. Add a note starting with QUESTION: on the span (e.g. QUESTION: gym or city?). These flow straight into the adjudication queue.
Export. Click Export → you get labels.<doc>.<annotator>.json (schema: {doc_id, annotator, text, spans:[{start,end,text,type,...}]}). Keep it local and hand only this file to the coordinator. Two+ people should label the same doc independently (blind) for a meaningful κ.

Read the full file on GitHub · 81 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

8d ago First seen · 81 lines · 0 tokens per session scan A 66b985478e32

Subscribe to this mod's changes

annotate is a skill published in the GitHub repository glebis/claude-skills (372 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,365 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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