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setup-context

Name: Setup Context
Author: getnao

// Bootstrap a nao agent for a project — gather warehouse + scope + extra-context info in one round, look up the warehouse-specific config from nao docs, write nao_config.yaml, run nao init + nao sync, set up the LLM key, and generate the first RULES.md. Use when the user has just decided to use nao on a new project. Only for first-time setup; for editing rules, generating tests, or reviewing an existing context, use write-context-rules / create-context-tests / audit-context.

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stars:1229

forks:164

updated:4 de mayo de 2026, 14:45

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deploy-context.md

from "getnao/nao"

Wire a nao project's context folder to a remote nao instance (Cloud or self-hosted) so every push to `main` automatically runs `nao deploy` via GitHub Actions. Use when the user has a working local nao project versioned in Git and wants their team's deployed instance to always reflect the latest committed context. Covers `nao deploy` usage, the GitHub Actions workflow, organization API keys, GitHub Secrets, `.naoignore`, environment-variable references in `nao_config.yaml`, and create-vs-update behavior. Do not use for first-time project setup (use `setup-context`) or for `nao sync` automation that commits warehouse metadata back to the repo (covered in the docs' synchronization page).

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add-semantic-layer.md

from "getnao/nao"

Wire a semantic layer into a nao agent so that metric queries are routed through a single source of truth. Supports dbt MetricFlow (dbt Cloud with Semantic Layer), Snowflake (views or semantic views via MCP), an in-house nao YAML semantic layer, or other tools (via MCP discovery). Installs the right MCP server, updates RULES.md to route metric queries through the semantic layer, and (for the nao YAML option) generates starter metric files. Use after a first round of tests has shown the agent struggling with metric reliability. Do not use for raw rule writing (write-context-rules) or first-time setup (setup-context).

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audit-context.md

from "getnao/nao"

Diagnose the health of a nao context at any stage of its lifecycle. Use when the user wants a structured review of what's been synced, how RULES.md compares to the target structure, whether every table is documented, whether the data model is MECE, whether tests exist and what their failures reveal, and whether context files are bloated. Outputs a structured audit report with ranked recommendations. Do not use for first-time setup (setup-context) or routine rule writing (write-context-rules).

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create-context-tests.md

from "getnao/nao"

Generate a test suite of natural-language → SQL pairs that becomes the quality benchmark for a nao agent, then run it via `nao test`. Use when the user wants to start measuring agent reliability, extend an existing test suite, or add tests for new metrics. Tests are the only honest answer to "is the context working?". Do not use for writing rules (write-context-rules) or diagnosing failures (audit-context).

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write-context-rules.md

from "getnao/nao"

Create or extend a nao project's RULES.md. Owns the RULES.md template. Use when the user wants to generate the initial RULES.md from synced metadata (called by setup-context), or improve their existing RULES.md. Do not use for first-time scope setup (use setup-context) or for diagnosing existing problems (use audit-context).

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package.json

"author": "getnao"

"repository": "getnao/nao"

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setup-context

description

Bootstrap a nao agent for a project — gather warehouse + scope + extra-context info in one round, look up the warehouse-specific config from nao docs, write nao_config.yaml, run nao init + nao sync, set up the LLM key, and generate the first RULES.md. Use when the user has just decided to use nao on a new project. Only for first-time setup; for editing rules, generating tests, or reviewing an existing context, use write-context-rules / create-context-tests / audit-context.

setup-context

Take the user from pip install nao-core to a synced project with a starter RULES.md.

Be brief. One batch of questions, then act. Don't ping-pong.

Scope ceiling: ≤100 tables. Above that, sync gets slow and per-table context budget gets thin. 20 is a great target.

Step 1 — Ask everything in one round

Send a single message asking for:

Warehouse + auth — type (BigQuery / Snowflake / Postgres / Redshift / DuckDB / Databricks / Athena / ClickHouse / Fabric / MSSQL / MySQL / Trino), and the auth credentials they have on hand. Tell them you'll fetch the exact field names from the nao docs once they pick a type.
Scope — which tables. Two valid shapes:
- Broad — gold/marts across multiple domains (exec / cross-functional agents).
- Deep — silver + gold for one domain (team-specific agents).
Extra context — dbt / ETL / BI repos, Notion, internal docs. Ask for the SSH git URL of each repo (e.g. git@github.com:org/repo.git) — sync clones them. No local paths.
LLM — provider and key. The model is selected in the nao UI, not in the config. Key comes later (Step 5).

Step 2 — Look up warehouse fields, write `nao_config.yaml`, run `nao init`

Fetch the warehouse-specific config from docs.getnao.io/nao-agent/context-builder/databases. Each warehouse has its own required and optional fields (e.g. BigQuery needs project_id + dataset_id (optional); Snowflake needs account_id + warehouse + schema_name (optional); Postgres needs host + port + database + schema_name (optional). Ask the user for any required field you don't already have.
Write nao_config.yaml from the answers (skeleton in appendix below).
Run nao init — it detects the existing yaml and offers to update; confirm. Folder scaffold gets created. Say "no" to optional providers (skills / MCPs / Notion / Slack); edit the yaml directly afterwards if needed.

Use this command (unsets leaked env vars from the parent agentic CLI — see Step 5):
```
unset ANTHROPIC_BASE_URL ANTHROPIC_API_KEY && source ~/.zshrc 2>/dev/null; nao init 2>&1
```

Print a summary of nao_config.yaml to the user before going further. Format example:

nao_config.yaml summary
  • project: <name>
  • warehouse: BigQuery (project=<id>, dataset=<id>, auth=service-account)
  • scope: include=["analytics.fct_*", "analytics.dim_*"], exclude=[]
  • templates: [columns, preview, description]
  • repos: company-dbt (git@github.com:org/company-dbt.git)
  • llm: anthropic (key via ${ANTHROPIC_API_KEY})

The model is configured in the nao UI, not in nao_config.yaml — don't include a model ID in the summary or the yaml.

Ask the user to confirm before continuing. This is the last cheap chance to catch a wrong project, a misspelled dataset, or a missing repo.

Database `templates` field

Per database in the yaml, set:

templates: [columns, preview, description]

That's the set this skill ships. Other values are valid per-warehouse (how_to_use, profiling, ai_summary, and indexes for ClickHouse) — see the docs link above — but stick to [columns, preview, description] unless the user specifically asks otherwise.

Don't use accessors — deprecated (renamed to templates).

nao init creates: nao_config.yaml, empty RULES.md, .naoignore, and folders databases/, repos/, docs/, semantics/, queries/, tests/, agent/{tools,mcps,skills}/.

Step 3 — `nao sync`

After the user confirms the summary in Step 2:

cd <project>   # where nao_config.yaml lives — every nao command runs from here
nao sync

Common failures: auth (fix yaml), tables not found (check schema casing), permission denied (grant read access), repo missing (fix repos: block, confirm SSH key). Don't move on until sync is clean.

Step 4 — Generate `RULES.md` (no confirmation)

Hand off directly to write-context-rules. Don't ask.

Step 5 — Wire up the LLM key

The key lives in nao_config.yaml. Two safe options:

Preferred: env-var ref. Write api_key: ${ANTHROPIC_API_KEY}; tell the user to export the key in their shell.
If they insist on a literal: tell them to edit the yaml themselves and add it to .gitignore. Never ask them to paste a key into chat.

Then nao debug to confirm.

Known issue — `AI_APICallError: Not Found`

If nao chat / nao debug / nao test fails with that error and the URL is https://api.anthropic.com/messages (no /v1/), the parent agentic CLI (Claude Code, Cursor, Codex) is leaking ANTHROPIC_BASE_URL into the child env. Fix:

unset ANTHROPIC_BASE_URL ANTHROPIC_API_KEY && source ~/.zshrc 2>/dev/null; nao chat 2>&1

Regular human terminals aren't affected.

Step 6 — Recommend next steps

Smoke test: nao chat, ask 3-5 real questions.
Review RULES.md for wrong inferences.
Pick a next skill: write-context-rules (refine), create-context-tests (benchmark), audit-context (anytime), add-semantic-layer (only after tests reveal metric-reliability gaps).

Guardrails

cd into the project directory before any nao command.
Cap at ~100 tables.
One batch of questions. Look up warehouse-specific fields from the docs, don't keep pinging the user.
Run nao init non-interactively with the yaml pre-written.
Use templates: [columns, preview, description]. Don't use accessors.
Repos: SSH git URLs only. No local paths in the repos: block.
Print the nao_config.yaml summary and get user confirmation before nao sync.
Never have the user paste their LLM key into chat.
Don't ask before invoking write-context-rules — just hand off.

Appendix — `nao_config.yaml` skeleton (BigQuery example)

Use this shape and adapt the databases: block per warehouse — see docs.getnao.io/nao-agent/context-builder/databases for the exact required/optional fields for Snowflake, Postgres, Redshift, Databricks, Athena, ClickHouse, Fabric, MSSQL, MySQL, Trino.

project_name: <project>

databases:
    - type: bigquery
      name: <connection-name>
      project_id: <gcp-project-id>
      dataset_id: <dataset>
      credentials_path: /path/to/service-account.json # or `sso: true`
      include: ['<dataset_pattern>.<table_pattern>'] # e.g. "analytics.fct_*" - use '*' as multiple patterns
      exclude: ['<pattern>']
      templates: [columns, preview, description]

llm:
    provider: anthropic # openai | bedrock | azure | gemini | mistral | ollama
    api_key: ${ANTHROPIC_API_KEY}

repos:
    - name: <repo-name>
      url: git@github.com:<org>/<repo>.git # SSH only

setup-context

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setup-context

Step 1 — Ask everything in one round

Step 2 — Look up warehouse fields, write nao_config.yaml, run nao init

Database templates field

Step 3 — nao sync

Step 4 — Generate RULES.md (no confirmation)

Step 5 — Wire up the LLM key

Known issue — AI_APICallError: Not Found

Step 6 — Recommend next steps

Guardrails

Appendix — nao_config.yaml skeleton (BigQuery example)

setup-context

Step 1 — Ask everything in one round

Step 2 — Look up warehouse fields, write nao_config.yaml, run nao init

Database templates field

Step 3 — nao sync

Step 4 — Generate RULES.md (no confirmation)

Step 5 — Wire up the LLM key

Known issue — AI_APICallError: Not Found

Step 6 — Recommend next steps

Guardrails

Appendix — nao_config.yaml skeleton (BigQuery example)

Step 2 — Look up warehouse fields, write `nao_config.yaml`, run `nao init`

Database `templates` field

Step 3 — `nao sync`

Step 4 — Generate `RULES.md` (no confirmation)

Known issue — `AI_APICallError: Not Found`

Appendix — `nao_config.yaml` skeleton (BigQuery example)

Step 2 — Look up warehouse fields, write `nao_config.yaml`, run `nao init`

Database `templates` field

Step 3 — `nao sync`

Step 4 — Generate `RULES.md` (no confirmation)

Known issue — `AI_APICallError: Not Found`

Appendix — `nao_config.yaml` skeleton (BigQuery example)