workplace.digital — built on Buzz
Work orders on a Kanban board. Powered by Buzz.
workplace.digital manages every work order as a card on a board — with Buzz as the collaboration platform underneath. Your people, your agents, your projects — ready to run on your own machine.
- Your M365, mirrored into Buzz — emails, chats, transcripts, calendars and Teams channels become RAG context for Claude or ChatGPT.
- WordPress client panel — set up, document and even promote projects with AI-generated videos.
- White-collar agents — add one to any project with a single click.
artificial-intelligence-engineer/
├─ SKILL.md
├─ references/
│ ├─ profile · tasks · skills
│ ├─ market.md ← live job-ad evidence
│ ├─ ai-skills.md ← … tiered agent skills
│ ├─ practitioner-qa.md ← Stack Exchange
│ ├─ usecases · intake · quality
│ └─ glossary · literature
├─ evals/
└─ PROVENANCE.md
Three pieces, one workplace
Kanban for work orders
Manage every work order on a board — capture, assign, track, finish. Buzz is the collaboration layer underneath: every card is backed by repos, chat and agents.
Your M365, mirrored into Buzz
Emails, chats, meeting transcripts, calendars and Teams channels sync into Buzz and become RAG context for Claude or ChatGPT — collaborate on top of everything your team already writes and says.
Client panel with AI videos
A WordPress-based client panel where projects are set up cleanly, documented — and even promoted with AI-generated videos.
Get Buzz on your desk
One download, zero configuration. Come test the early stages with us.
Buzz for Windows — preconfigured relay + appcoming soon
A preconfigured Buzz relay with the Buzz app already set up. Unzip, start the Windows application, and everything is ready to use — no accounts, no setup wizard.
Every processed occupation gets its skill rankings from real, relevance-checked job ads — click to browse the finished ones.
The crawl started … and has been running for … — computer-based professions first.
Each ad is distilled by a self-hosted model (gemma3 on our GPU — no data leaves the house) into structured facts: skills, tools, seniority. One count = one ad.
Libraries already integrated vs. discovered candidates still being screened (cloned, checked for real skill content and license). The count grows as the screening run progresses.
Add a white-collar agent to any project — one click.
3,039 professions, each a ready-made agent: lawyer, AI engineer, web designer, database administrator. Pick one and it joins the project board instantly — preloaded with the skills of its trade.
Preconfigured agents for every white-collar job
Add them to a project with one click. Every agent lives right here in this instance: one repository per profession
in the skills-core and
skills-community orgs —
for example project-manager — sorted by the most sought-after skills for
Claude and ChatGPT. Open a repo and read what the agent knows; every file renders directly in the repository.
AI engineer
Every source compiled: taxonomy profile, gated job-ad evidence (US + DACH), expert curation, tiered agent skills and Stack Exchange practitioner Q&A.
Lawyer
Legal profile, case-related tasks and competences — plus mapped compliance & contract agent skills.
Database administrator
The deepest AI-skill mapping in the catalog: 90 agent skills for design, migrations, ops and security.
Web designer
Design competences plus 49 mapped skills — frontend, accessibility, UI patterns and brand systems.
Public occupation knowledge becomes an agent
Occupational knowledge belongs to nobody — so nobody maintains it. Buzz changes that: three sources, one compilation, versioned in git.
Occupation taxonomies
3,000+ occupations from ESCO & O*NET — public, standardized, multilingual. Joined via the official crosswalk.
Job ads
Current skill requirements from global job platforms — weighted market evidence with percentages and an as-of date.
Expert knowledge
Best practices from literature and the web, 900+ proven agent skills from 16 open-source libraries (Anthropic, NVIDIA, Google, community) — and curated practitioner Q&A from six Stack Exchange communities, each entry attributed (CC-BY-SA).
Every item knows where it comes from
After compilation, each package reports its source mix.
Shown here: the AI-engineer package with every source compiled —
every repo carries the same breakdown as PROVENANCE.md with a rendered chart.
- Job boards — market evidence
Full market report from real job ads (JSearch API): ranked requirements with share, seniority distribution, title variants — extracted facts only, aggregated live.
53% - O*NET — tasks & tools
Task statements, work activities and software from the U.S. occupation database.
31% - Wikipedia & AI expert curation
Glossary, literature, use cases, intake questions, quality criteria and evals — AI-curated with cited web sources.
10% - ESCO — occupation & competences
The European profile: essential and optional competences per occupation.
6% - Anthropic — official Claude skills
All 17 skills Anthropic ships for Claude Code (docx, xlsx, pptx, pdf, mcp-builder, frontend-design, skill-creator & more), each mapped to the white-collar occupations that exercise it — reported as its own source.
0% - External AI skill packs — mapped
The open-source skills ecosystem: 500 libraries with 24,835 agent skills — curated flagships (obra/superpowers, wshobson/agents) plus hundreds of auto-discovered repos, continuously re-scanned every week. Each skill is matched to the occupations that exercise it and linked with per-source attribution, never copied.
1% - Stack Exchange — practitioner Q&A
What experienced practitioners actually advise: quality-filtered questions and answers from six professional Stack Exchange communities (Workplace, Project Management, Law, Money, Software Engineering, Data Science), condensed into per-profession insights — each entry attributed to its author, CC-BY-SA 4.0.
0%
Numbers load live from the package manifest. The job-ad rollout for all 3,039 occupations is running — packages gain their market-evidence segment as it lands. See the library-wide chart →
Private stays private. Public knowledge grows.
Knowledge rises by proving itself
- Same insight in ≥ 2 projects → suggested promotion into the org overlay
- Proven org knowledge → anonymized second distillate as a community PR
- Hard rule: nothing leaves
projects/automatically — the path outward is always an explicitly confirmed, generalized, anonymized distillate - Agents load the cascade Community → Organisation → Project; more specific beats more general
Your profession's agent — in Claude, ChatGPT and Gemini
The git repo is the canonical format: plain Markdown, YAML, JSON — no vendor syntax in the content. Thin adapters build the same knowledge into an Anthropic agent skill, a Custom-GPT bundle and a Gemini bundle; one MCP gateway handles reading and writing for all three.
Claude
Agent-skill package per profession: trigger description, contribution behaviour, competence files as lazy references — installable via plugin marketplace.
ChatGPT
Custom-GPT bundle: generated system instruction + competence files as knowledge upload; writing goes through an Action onto the same gateway.
Gemini
Instruction + grounding files; writing via function calling against the same gateway. One content, three thin adapters.
Enrichment path A — chat distillate
Solved something real in a conversation? The agent offers to save it: a project distillate into your private org repo, and — if a transferable core exists — an anonymized distillate as a community pull request. You confirm the full text; nothing commits directly to main.
Enrichment path B — M365 mining
A connector mirrors mails, Teams chats and meeting transcripts into SQL Server. A batch job extracts lesson candidates (consent-gated, roles instead of names, raw data never leaves the database) and files them as a review queue of pull requests — no auto-merge.
One sentence saves the lesson
A project manager — Buzz agent installed, connector linked — just learned in a Claude conversation that customer A ignores her slide-deck status reports, but responds within hours to a three-bullet email. She keeps that experience:
Save the key insight from this chat as a lesson learned in my Buzz repo.
Happy to — I suggest two placements. Please review both full texts; exactly what you confirm gets committed.
# Customer A: status updates as a three-bullet email **Situation:** Weekly slide-deck status reports to customer A went unanswered for weeks. **Insight:** Customer A's project lead reads email on the go and never opens attachments. A three-bullet email — done / next / blocked — gets a response within hours. **Action for this project:** Send Friday status as a three-bullet email; keep the deck for the monthly review only.
# Match the report format to how the reader actually reads **Pattern:** Status reports fail when the format suits the sender, not the reader — decks go unread by people who live in their inbox. **Transferable practice:** Ask (or observe) how your counterpart consumes information, then default to the shortest format that fits it — e.g. a three-bullet email: done / next / blocked. Keep long formats for milestones.
Both, just one, or neither?
both
Done — both filed as pull requests (never a direct commit):
✅ Org repo: PR #41 · customer-a/rollout-2026 — "Status as three-bullet email"
✅ Community: PR #587 · project-manager — "Match the report format to the reader"
Both await maintainer review. I checked the target folders for similar lessons first — no duplicates found.
Works the same in ChatGPT and Gemini (adapters on the same gateway). Requirements: the profession agent installed + the Buzz connector linked — setup guide →
Refreshed monthly — skills follow the market
Put your work orders on the board.
Kanban for work orders, your M365 mirrored into Buzz as RAG for Claude or ChatGPT, a WordPress client panel with AI-generated videos — and 3,039 profession agents, each one click away from your project. Come test the early stages with us.