Office Collaboration Radar (English)

Extract an evidence-linked collaboration status card from chat logs, meeting notes, and project updates. Use when a user needs project progress, confirmed decisions, owner-and-deadline actions, blockers, cross-functional dependencies, human-review items, an executive summary, a radar chart, or stable JSON. Designed for English collaboration material; redacts PII and remains read-only by default.

Turn scattered collaboration records into a stable, auditable project status card. Extract only facts supported by the supplied material. Do not produce a full weekly report, judge individual performance, send reminders, or write to external systems.

Chinese version: Office Collaboration Radar

Capabilities

• Produce seven fixed modules: overview, progress, decisions, actions, risks and dependencies, cross-functional relationships, and human-review items.
• Attach evidence snippets of at most 80 characters; use Not provided when no reliable evidence exists.
• Redact phone numbers, email addresses, government IDs, bank-card numbers, and user-supplied entity names.
• Flag conflicting owners, deadlines, or statuses for human review.
• Rank actions as P0/P1/P2, synthesize an executive summary, and generate an SVG collaboration-health radar.
• Export the structured card to CSV, Feishu/Bitable mapping JSON, or Notion mapping JSON.

Input

Accept Markdown, plain text, exported chats, meeting notes, or project logs. Label the source of each segment when combining materials. Optional metadata includes project name, time range, departments, and a list of known entities to redact.

If the material does not state a project name, time range, owner, deadline, or department, use Not provided. Do not infer or complete it.

Workflow

1. Separate facts from opinions, questions, tentative statements, small talk, and duplicates.

2. Draft the seven-module card using the key order in templates/json-output-schema.md.

3. Keep owner and deadline values verbatim. If an exact value cannot be found in the supplied source, replace it with Not provided.

4. Enforce evidence, redaction, Markdown-cell sanitization, conflict checks, and priority ordering:

   bash

   python3 scripts/process.py enforce --card draft-card.json --source source.txt --out enforced-card.json
   

   Aggregate multiple cards first when needed:

   bash

   python3 scripts/process.py aggregate --cards card-1.json card-2.json --out aggregated.json
   

5. Optionally generate a radar chart:

   bash

   python3 scripts/radar_chart.py generate --card enforced-card.json --out collaboration-radar.svg
   

6. Optionally export a downstream mapping:

   bash

   python3 scripts/export_card.py export-card --card enforced-card.json --format csv --out actions.csv
   

7. Return the Markdown card, JSON result, and executive summary. Before publishing or changing the skill, run:

   bash

   python3 scripts/process.py selftest
   python3 scripts/validate_output.py
   

Output contract

Keep every heading in this order, even when a section contains only Not provided:

markdown

# Collaboration Status Card

## Project Overview
## Progress
## Confirmed Decisions
## Owner × Deadline Actions
## Risks / Blockers / Dependencies
## Cross-functional Relationships
## Human Review Required
## JSON Output

Use templates/collaboration-status-card.md for the Markdown layout and templates/json-output-schema.md for the machine-readable contract.

Evidence and safety boundaries

• Do not turn tentative intent into a commitment or invent owners, deadlines, decisions, departments, or launch dates.
• Use Conflict detected; human review required consistently and add a matching human-review item.
• Sanitize all user-derived Markdown table values and escape CSV formula prefixes.
• Escape SVG titles before rendering them.
• Minimize and redact secrets, tokens, customer data, and sensitive HR information.
• Do not send messages, email, or calendar invitations, and do not modify Jira, Feishu, Notion, CRM, calendars, or other systems.
• Do not convert interpersonal friction or emotional language into performance judgments or blame.

The example under examples/enterprise-ai-pilot/ is synthetic and redacted. It demonstrates extraction behavior only; it is not evidence of a production deployment or business outcome.