Health Sync

1. Bot runs npx health-sync init remote bootstrap.
2. Bot sends user: npx health-sync init --remote <bootstrap-token>.
3. User sends back encrypted archive.
4. Bot runs npx health-sync init remote finish <ref> <archive>.

For full operational instructions, always consult:

• references/setup.md

Do not use or recommend legacy direct setup flows such as:

1. health-sync init as the primary user instruction
2. health-sync auth <provider> as a standalone onboarding path

Those commands may still exist for maintenance/debugging, but they are not the setup flow this skill should guide.

Runtime And Data Disclosure (Mandatory)

This skill assumes the bot environment has local CLI and filesystem access.

1. Required binary:
   • node
   • npm
   • npx
2. Expected local working paths:
   • workspace/health-sync/health-sync.toml
   • workspace/health-sync/.health-sync.creds
   • workspace/health-sync/health.sqlite
3. Sensitive-data handling:
   • Remote onboarding imports encrypted archives that contain provider credentials/tokens.
   • Finish flow writes decrypted secrets to local files on the bot host.
   • These files must be treated as sensitive at rest (access controls, backups, retention).
4. Chat-safety boundary:
   • Never ask users to paste raw secrets in chat.
   • Only collect encrypted archive files via remote bootstrap flow.

Schema Handling

To understand data schemas and query correctly, read the provider reference files:

• references/oura.md
• references/withings.md
• references/hevy.md
• references/strava.md
• references/whoop.md
• references/eightsleep.md

Freshness Rule (Mandatory)

Before any analysis, always run:

npx health-sync sync

If sync fails, report the failure clearly and continue analysis only if the user explicitly asks to proceed with potentially stale data.

Analysis Workflow

1. Run npx health-sync sync first.
2. Identify the user question and which provider/resource(s) are relevant.
3. Read the provider schema reference before forming SQL.
4. Query records, sync_state, and sync_runs as needed.
5. Produce a clear, user-friendly answer with concrete numbers and dates.
6. Highlight meaningful patterns and offer practical guidance.
7. When data quality or coverage is limited, say so explicitly.

Output Style

• Be concise, clear, and practical.
• Focus on useful interpretation, not just raw data dumps.
• Connect metrics to actionable insights (sleep, recovery, training, consistency, etc.).
• Ask follow-up questions only when necessary to improve analysis quality.