version: "1.0.0"

Setting up the knowledge-wiki quickstart

Follow these steps in order when the user asks you to set up or run this cookbook. Confirm each step's output before moving to the next.

0. Check access (blocks the consolidation step)

Two features must be enabled on the user's organization:

• Managed Agents — memory stores and agent sessions.
• Dreaming — a gated research preview.

Ask the user to confirm both, and point them at <https://claude.com/form/claude-managed-agents> if not. Without dreaming, POST /v1/dreams returns 404 and the notebook fails at the consolidation step. Everything before that still runs, so it's fine to proceed for a partial walkthrough — just say so up front rather than letting them discover it an hour in.

1. Install dependencies

pip install -r requirements.txt

Dreaming is not in the public PyPI anthropic package — it ships in a dedicated preview SDK build. Do not try to source that build yourself: the preview onboarding provides it when the org is enrolled. If the user is enrolled, ask them to install it per those instructions; confirm with:

python3 -c "import anthropic; print(hasattr(anthropic.Anthropic().beta, 'dreams'))"

If that prints False, the notebook will run up to the consolidation step and no further.

2. Authentication and environment

The notebook constructs Anthropic() with no arguments, so it resolves credentials through the standard chain: ANTHROPIC_API_KEY, then ANTHROPIC_AUTH_TOKEN, then an ant auth login profile, then Workload Identity Federation. Pick whichever the user's org uses:

# Either an API key…
export ANTHROPIC_API_KEY=...
 
# …or an interactive login, which stores a profile the SDK finds on its own.
ant auth login

Do not set ANTHROPIC_API_KEY alongside a profile — a stale exported key silently shadows the profile, and a key set next to ANTHROPIC_AUTH_TOKEN makes the SDK send both headers, which the API rejects. ant auth status shows which credential source won.

One more variable is required regardless:

export EDGAR_USER_AGENT="your-name your-email"  # SEC policy

If EDGAR_USER_AGENT is missing, EDGAR requests will be refused.

3. Pick a tier and set the cost expectation

Tell the user what they're about to spend before fetching anything.

Costs are order-of-magnitude estimates from the committed run on claude-sonnet-5; the user's run will vary with model choice and API pricing at the time.

4. Fetch the corpus

Run in this order (network required):

python3 build_manifest.py
python3 fetch_data_room.py --tier=mini    # or the tier chosen in step 3
python3 fetch_real_deck.py                # 6 slides of a real board deck (~1 MB)
python3 make_analyst_docx.py

Expected after this: data_room/docs/ contains one .txt file per document in the chosen tier — 8 files for quickstart, 26 for mini.

5. Open the notebook

jupyter lab distill_documents_into_knowledge_wiki.ipynb

Recommend the user reads README.md §"How it works" before running cells — the pipeline has a few non-obvious steps (the resolve pass, the read-only analyst attach, the usage-driven re-dream).

Gotchas to warn about

• The dream step can take 20–60 minutes; tell the user to kick it off

and check back rather than watching it.

• Model choice: the default is claude-sonnet-5 everywhere. The setup

cell notes that an Opus-tier dream model (claude-opus-5) is a reasonable A/B against the default.

• If the user hits an error on client.beta.memory_stores.create, their

org likely doesn't have Managed Agents enabled — see step 0.