195 lines
9.6 KiB
Text
195 lines
9.6 KiB
Text
{# Default analyst-onboarding workspace prompt for "da analyst setup".
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Rendered server-side by src/claude_md.py. Edit this file to change
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the OSS default; admins override per-instance via /admin/workspace-prompt.
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Available context (see docs/agent-workspace-prompt.md for the full reference):
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instance.name, instance.subtitle
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server.url, server.hostname
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sync_interval — string from instance.yaml
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data_source.type — keboola | bigquery | local
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tables — list of {name, description, query_mode}
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metrics.count, metrics.categories
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marketplaces — list of {slug, name, plugins:[{name}]}
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user.id, user.email, user.name, user.is_admin, user.groups
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now, today — datetime / date string
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#}
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# {{ instance.name }} — AI Data Analyst
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This workspace is connected to {{ server.url }}.
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{% if instance.subtitle %}Operated by **{{ instance.subtitle }}**.{% endif %}
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## Rules
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- Before computing any business metric: run `da metrics show <category>/<name>`
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- **For canonical table list with query modes: `da catalog`.** `data/metadata/schema.json` covers `query_mode: "local"` tables only — for remote/hybrid tables it's incomplete. Treat `da catalog` as source of truth.
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- Do not use DESCRIBE/SHOW COLUMNS — use `da schema <table>` instead
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- Save work output to `user/artifacts/`
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- Sync data regularly with `da sync`
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- **Personal customizations go in `.claude/CLAUDE.local.md`, NOT here.** This file is regenerated by `da analyst setup --force`; edits here will be lost. CLAUDE.local.md is preserved across regeneration and uploaded on `da sync --upload-only`.
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## Metrics Workflow
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1. `da metrics list` — find the relevant metric ({{ metrics.count }} available, categories: {{ metrics.categories | join(", ") or "none yet" }})
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2. `da metrics show <category>/<name>` — read SQL and business rules
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3. Use the canonical SQL from the metric definition, adapt to the question
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4. Never invent metric calculations — always check existing definitions first
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## Data Sync
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- `da sync` — download current data from server
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- `da sync --docs-only` — just metadata and metrics (fast refresh)
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- `da sync --upload-only` — upload sessions and local notes to server
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- Data on the server refreshes every {{ sync_interval }}
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## Available Datasets
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{% for t in tables -%}
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- `{{ t.name }}`{% if t.description %} — {{ t.description }}{% endif %}{% if t.query_mode == "remote" %} *(remote, queried on demand)*{% endif %}
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{% else -%}
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- _No tables registered yet — ask an admin to register tables in the dashboard._
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{% endfor %}
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{% if marketplaces -%}
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## Plugins available to you
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{% for mp in marketplaces -%}
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- **{{ mp.name }}** ({{ mp.slug }}): {{ mp.plugins | map(attribute="name") | join(", ") }}
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{% endfor %}
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{% endif -%}
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## Remote Queries (BigQuery) — when data isn't on the laptop
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Not every table is synced. Tables registered with `query_mode: "remote"` live in
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BigQuery, accessed server-side via DuckDB's BQ extension — no parquet on disk.
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Tables you don't see in `data/parquet/` may still be queryable.
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### Discovery first
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```
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da catalog --json | jq '.[] | {name, source_type, query_mode}' # see all tables + their modes
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da schema <table> # columns + types
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da describe <table> -n 5 # sample rows
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```
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For local-mode tables, query directly with `da query "SELECT … FROM <table>"`.
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### Three patterns for `query_mode: "remote"` tables
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| Pattern | Tool | Use when |
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|---------|------|----------|
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| **`da fetch`** (preferred) | materializes a filtered subset locally → query the snapshot | repeated questions on same slice |
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| **`da query --remote`** | one-shot, server-side execution against BigQuery (works for BASE TABLE rows directly + VIEW/MATERIALIZED_VIEW rows via the BQ jobs API; cost-guarded by a 5 GiB scan cap configurable in /admin/server-config) | single aggregate / cheap probe |
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| **`da query --register-bq`** | hybrid joins between local snapshots and ad-hoc BQ subqueries | crossing local + remote |
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### Permission model + cost — important
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- BQ access goes through the **agnes server's GCE service account**, not your personal Google credentials. If a query fails with a permission error, the table is in a project the server SA cannot read — escalate to admin, do NOT try to authenticate yourself.
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- Every BQ query bills the SA's GCP project for **bytes scanned**. A naive `SELECT * FROM <large_table>` can cost real money. ALWAYS:
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- filter via `--where` on the partition column (typically a date)
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- list specific columns in `--select` — column-store BQ skips the rest, cheaper
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- run `--estimate` first when unsure of the table size or partitioning
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### `da fetch` discipline
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```
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# 1. ESTIMATE first — refuses to fetch without knowing the cost
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da fetch <table> --select col1,col2 --where "date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)" --estimate
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# 2. If reasonable, fetch as a named snapshot
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da fetch <table> --select col1,col2 --where "..." --as my_recent
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# 3. Query the local snapshot
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da query "SELECT col1, COUNT(*) FROM my_recent GROUP BY 1"
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# 4. List + drop snapshots when done
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da snapshot list
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da snapshot drop my_recent
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```
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Rules of thumb:
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- ALWAYS list specific columns in `--select`. Avoid implicit SELECT *.
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- ALWAYS include a `--where` for remote tables; otherwise add `--limit`.
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- ALWAYS run `--estimate` first when the table is `partition_by` / `clustered_by`
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per `da schema`, or could plausibly exceed 1 GB local bytes.
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- Reuse snapshots across questions in the same conversation — `da snapshot list`
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before fetching.
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### Snapshot freshness — when to refresh
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Snapshots are point-in-time copies. They go stale as the source data updates (most BQ tables refresh daily; check `sync_schedule` per `da catalog`). For each new conversation:
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```
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da snapshot list # see existing snapshots + their ages
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da snapshot drop my_recent # drop stale ones
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da fetch <table> --select ... --where ... --as my_recent # re-fetch
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```
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If the question is time-sensitive (e.g. "today's orders"), assume any snapshot older than the table's `sync_schedule` is stale and refresh.
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### Hybrid query example — local + remote in one query
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`da query --register-bq` lets a single SQL statement join a local table with an ad-hoc BQ subquery. The BQ subquery runs first (server-side), result registered as a DuckDB view, then the joined query runs locally.
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```
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da query \
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--register-bq "traffic=SELECT date, country, SUM(views) AS views \
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FROM \`prj.web_analytics.sessions\` \
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WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) \
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GROUP BY 1, 2" \
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--sql "SELECT o.date, o.country, o.revenue, t.views, o.revenue / NULLIF(t.views,0) AS rev_per_view \
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FROM orders o \
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JOIN traffic t ON o.date = t.date AND o.country = t.country \
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ORDER BY 1 DESC"
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```
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The BQ subquery MUST contain `WHERE` and/or `GROUP BY` to keep the registered result manageable (target: under 500K rows, well under 100 MB). Multiple `--register-bq` flags can compose multiple BQ sources. For complex SQL, use `--stdin` mode (`echo '{"register_bq":{...},"sql":"..."}' | da query --stdin`).
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### BigQuery SQL flavor for `--where`
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Source-typed `bigquery` tables use BigQuery dialect, not DuckDB:
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- Date literal: `DATE '2026-01-01'`
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- Timestamp literal: `TIMESTAMP '2026-01-01 00:00:00 UTC'`
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- Now: `CURRENT_DATE()`, `CURRENT_TIMESTAMP()`
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- Date arithmetic: `DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)`
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- Regex: `REGEXP_CONTAINS(col, r'pattern')` (raw string!)
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- Cast: `CAST(x AS INT64)` (NOT `INT`)
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### When the table you want isn't in `da catalog`
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The table may exist in BigQuery but not be registered with Agnes yet. Two options:
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1. **Ad-hoc one-shot** — register a BQ subquery as a view inline, no admin needed
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if the agnes server SA has BQ access:
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```
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da query --register-bq "live=SELECT * FROM \`project.dataset.table\` WHERE date >= '...' LIMIT 1000" \
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--sql "SELECT * FROM live"
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```
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2. **Ask admin to register** the table with `query_mode: "remote"` so it shows up
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in `da catalog` and supports `da fetch` / `da query --remote`. This is the
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right path for any table you'll query repeatedly.
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### Deeper guidance
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For the full protocol, including hybrid-query examples, snapshot hygiene, and
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when NOT to use `da fetch`, run:
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```
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da skills show agnes-data-querying
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```
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## Corporate Memory
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Rules injected by `da sync` from the server's corporate knowledge base live in `.claude/rules/km_*.md`. They are automatically loaded by Claude Code on every session start.
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- `km_<id>.md` — mandatory rules (always enforced)
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- `km_approved.md` — approved guidance (confidence × recency ranked)
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Run `da sync` to refresh. Rules are pruned automatically when items are revoked.
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## Directory Structure
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- `data/` — read-only data downloaded from server
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- `data/parquet/` — table data in Parquet format
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- `data/duckdb/` — local analytics DuckDB database
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- `data/metadata/` — profiles, schema, metrics cache
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- `user/` — your workspace (persistent across syncs)
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- `user/artifacts/` — analysis outputs, reports, charts
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- `user/sessions/` — Claude Code session logs
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- `.claude/CLAUDE.local.md` — your personal notes + workspace customizations. **Never overwritten by `da analyst setup --force`.** Uploaded to the server on `da sync --upload-only`. Put any local-only Claude instructions, project-specific reminders, or temporary notes here — NOT in CLAUDE.md (this file is regenerated from a template).
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_Hello {{ user.name or user.email }} — generated {{ today }}._
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