Axiom

WORKING SLICE
Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine.

Build this scoped Axiom-inspired workflow with a documented data model and visible failure states.

Architecture
- Next.js App Router and TypeScript for server-rendered pages and validated mutations.
- PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.

Prerequisites and limits
A supported Node release, PostgreSQL, HTTPS for a shared deployment, and a backup destination. Begin with one workspace and explicit owner/member permissions.
Outside this release: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.

Data model and correctness
datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations
Invariant: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.

Security and privacy
Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML.

Recovery and export
Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.
2. Phase 2 — Durable model. Model datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.
4. Phase 4 — Permissions and integration failure. Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.
Use real source data or clearly labeled fixtures. Explain unsupported input and provider failures; do not fabricate analytics, delivery receipts, accuracy claims or security guarantees.

Optional agent guidance
Optional external skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — Improve React and Next.js data fetching, rendering, bundle size and server performance. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [supabase-postgres-best-practices](https://github.com/supabase/agent-skills/blob/main/skills/supabase-postgres-best-practices/SKILL.md) — Review PostgreSQL schemas, queries, indexes, pooling, concurrency and row-level security. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations
Project rule — preserve this invariant: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
Project rule — acceptance evidence: Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.

# AGENTS.md — Build guide for Axiom

## Project scope
Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine.

Catalogue verdict: kinda. The core loop is buildable, but a dependable replacement becomes a real weekend or multi-day project. For Axiom, ingest bounded logs, search them, and retain a compact local event store. The hard boundary is high-performance cloud storage, query engine, integrations, and managed operations, plus independent infrastructure and reliable alerting.
Use the implementation prompt below to define the deliverable. Complete each phase's acceptance checks before extending the scope.

## Working agreement
- Inspect the repository and its existing instructions before choosing paths, dependencies or commands. Keep one coherent stack and explain changes to the proposed architecture.
- Plan a vertical slice that accepts a real input and produces the useful output described below. Persist only the state the prompt calls for; respect memory-only and upstream-managed workflows. Use fixtures only when they are clearly labelled.
- After scaffolding, document the actual install, development, check and build commands in README and keep them synchronized with the package or project manifest. Do not report commands as successful unless they ran.
- Work in small steps. At handoff, list implemented flows, checks actually performed, remaining blockers, and any credentials or provider setup the owner must supply.
- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.

## Prerequisites
- A supported Node release, PostgreSQL, HTTPS for a shared deployment, and a backup destination. Begin with one workspace and explicit owner/member permissions.
- Implementation components: Next.js App Router and TypeScript for server-rendered pages and validated mutations. PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.
- Scope boundary: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.

## Stack and architecture
- Next.js App Router and TypeScript for server-rendered pages and validated mutations.
- PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.
- Domain model: datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations

## Security and data integrity
- Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML.
- Correctness boundary: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
- Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.
- Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching.

## Agent implementation rules
- Project rule — data model: datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations
- Project rule — preserve this invariant: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
- Project rule — acceptance evidence: Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.

## Optional agent skills and references
- Optional external skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — Improve React and Next.js data fetching, rendering, bundle size and server performance. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
- Optional external skill: [supabase-postgres-best-practices](https://github.com/supabase/agent-skills/blob/main/skills/supabase-postgres-best-practices/SKILL.md) — Review PostgreSQL schemas, queries, indexes, pooling, concurrency and row-level security. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
- Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.

Read the linked SKILL.md and its dependencies before adding a skill. Select only the skills matching this project's runtime and task; their documentation does not supply API access, credentials or approval to perform external actions. Pin the reviewed revision where the tool supports it. Follow the chosen agent's documented project-level installation mechanism.

## Distribution ideas
These are optional planning notes. Obtain the owner's approval before publishing or contacting anyone.
- Demonstrate the actual Axiom-inspired workflow with owned or clearly labeled sample data: Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine.
- Publish a reproducible walkthrough with this observable result: Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.
- Explain who can operate this scoped tool, its setup and ongoing costs, and these remaining product gaps: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope. Avoid guaranteed savings, performance scores or implied endorsement.

## Engineering roadmap
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.
2. Phase 2 — Durable model. Model datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.
4. Phase 4 — Permissions and integration failure. Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

## Paid-product capabilities outside this build
- high-performance cloud storage, query engine, integrations, and managed operations
- global probe network
- phone and SMS delivery
- massive retention
- advanced incident response and support

## Implementation prompt
WORKING SLICE
Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine.

Build this scoped Axiom-inspired workflow with a documented data model and visible failure states.

Architecture
- Next.js App Router and TypeScript for server-rendered pages and validated mutations.
- PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.

Prerequisites and limits
A supported Node release, PostgreSQL, HTTPS for a shared deployment, and a backup destination. Begin with one workspace and explicit owner/member permissions.
Outside this release: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.

Data model and correctness
datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations
Invariant: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.

Security and privacy
Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML.

Recovery and export
Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Ingest structured logs for one service, search by time and selected fields, show a live tail and save threshold alerts. Use PostgreSQL JSONB and indexes for the initial bounded volume instead of inventing an APL-compatible engine. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Large-scale analytics infrastructure, APL compatibility and managed retention guarantees are outside scope.
2. Phase 2 — Durable model. Model datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.
4. Phase 4 — Permissions and integration failure. Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.
Use real source data or clearly labeled fixtures. Explain unsupported input and provider failures; do not fabricate analytics, delivery receipts, accuracy claims or security guarantees.

Optional agent guidance
Optional external skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — Improve React and Next.js data fetching, rendering, bundle size and server performance. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [supabase-postgres-best-practices](https://github.com/supabase/agent-skills/blob/main/skills/supabase-postgres-best-practices/SKILL.md) — Review PostgreSQL schemas, queries, indexes, pooling, concurrency and row-level security. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: datasets, authenticated ingest keys, bounded JSON log events, parsed fields, saved queries and alert evaluations
Project rule — preserve this invariant: Event time and receive time remain separate; arbitrary SQL and secrets in payloads are rejected or redacted before storage.
Project rule — acceptance evidence: Replay an event ID without duplicating the log; a late event appears at its event time and an exceeded alert cooldown suppresses repeated notifications.

## Completion evidence
Demonstrate the prompt's acceptance scenarios against the scoped workflow. Include setup from a clean checkout and failure recovery. Check persistence across restart and export/restore only for the state the prompt says to store; for memory-only tools, confirm that temporary content is discarded as specified. Record actual results and remaining limitations. A detailed plan alone does not establish a working replacement.