Annotate Skill for Claude

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.

FOR THE COORDINATOR (measure + adjudicate)

• Collect every labels.<doc>.<annotator>.json into one folder, e.g. labels/.
• Score IAA:

   python3 skills/annotate/scripts/score_iaa.py --labels-dir labels/ --out-dir results/

It writes (per doc + overall): Cohen's κ (pairwise), Fleiss' κ (3+ annotators),

span-F1, a disagreement queue (*-iaa-disagreements.json: every cluster annotators

don't fully agree on, plus any QUESTION: spans), and a draft adjudicated gold

(*-adjudicated-gold-draft.json: majority span per overlap-cluster, ties/questions marked

needs_review:true). Character-level κ sidesteps tokenization disputes.

• Target κ ≥ 0.80 = a defensible gold. Lower usually means an unclear codebook rule, not a

careless annotator — fix the rule and re-label, don't just discard.

• Adjudicate. Walk the disagreement queue with a human adjudicator; resolve each

needs_review cluster. The resulting label set is the published gold; report

post-adjudication κ too. Nothing is ever auto-finalised.

Test the loop solo (no second person yet)

Treat an existing gold JSONL as one "reference annotator", label the same doc yourself in

annotator.html as another, then score the pair:

python3 skills/annotate/scripts/gold_to_labels.py --gold GOLD.jsonl --name gold --out-dir labels/
# label the same doc yourself in annotator.html as "me" -> drop labels.<doc>.me.json into labels/
python3 skills/annotate/scripts/score_iaa.py --labels-dir labels/ --out-dir results/

(--sessions-dir DIR lets gold_to_labels.py read transcript text from disk so char offsets

match the gold exactly.)

Output

IAA results (κ, F1) + a disagreement list + a draft adjudicated gold — labels/stats only.

Transcript text and original PII stay local; only what's needed to adjudicate is shared.