Scalenut

WORKING SLICE
Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft.

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

Architecture
- Node.js, TypeScript and Express with server-rendered HTML and small browser modules.
- SQLite through better-sqlite3 with migrations, prepared statements and a single background worker.
- A single provider SDK with a configured supported model, schema-validated responses and revisioned prompt templates.

Prerequisites and limits
A supported Node release, a writable local data directory and a separate backup location. Bind to localhost; remote use requires authentication and HTTPS first. Optional AI generation needs a provider key, a usage budget and approval to send the selected material.
Outside this release: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data

Data model and correctness
topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions
Invariant: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.

Security and privacy
Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication.

Recovery and export
Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft. Record prerequisites, select representative user-owned fixtures and document the unsupported features: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data
2. Phase 2 — Durable model. Model topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
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. Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.
4. Phase 4 — Permissions and integration failure. Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.
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: [copywriting](https://github.com/coreyhaines31/marketingskills/blob/main/skills/copywriting/SKILL.md) — Write landing pages and product copy grounded in the intended audience, product value and a clear next action. 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.
Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions
Project rule — preserve this invariant: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
Project rule — acceptance evidence: Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.

# AGENTS.md — Build guide for Scalenut

## Project scope
Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft.

Catalogue verdict: kinda. The core loop is buildable, but a dependable replacement becomes a real weekend or multi-day project. For Scalenut, research a topic, build a content brief, and draft against selected SERP concepts. The hard boundary is seo datasets, topic clustering, workflow automation, and team features, plus workflow, data, and model tuning.
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, a writable local data directory and a separate backup location. Bind to localhost; remote use requires authentication and HTTPS first. Optional AI generation needs a provider key, a usage budget and approval to send the selected material.
- Implementation components: Node.js, TypeScript and Express with server-rendered HTML and small browser modules. SQLite through better-sqlite3 with migrations, prepared statements and a single background worker. A single provider SDK with a configured supported model, schema-validated responses and revisioned prompt templates.
- Scope boundary: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data

## Stack and architecture
- Node.js, TypeScript and Express with server-rendered HTML and small browser modules.
- SQLite through better-sqlite3 with migrations, prepared statements and a single background worker.
- A single provider SDK with a configured supported model, schema-validated responses and revisioned prompt templates.
- Domain model: topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions

## Security and data integrity
- Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication.
- Correctness boundary: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
- Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.
- Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data.

## Agent implementation rules
- Project rule — data model: topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions
- Project rule — preserve this invariant: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
- Project rule — acceptance evidence: Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.

## Optional agent skills and references
- Optional external skill: [copywriting](https://github.com/coreyhaines31/marketingskills/blob/main/skills/copywriting/SKILL.md) — Write landing pages and product copy grounded in the intended audience, product value and a clear next action. 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.
- Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. 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 Scalenut-inspired workflow with owned or clearly labeled sample data: Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft.
- Publish a reproducible walkthrough with this observable result: Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.
- Explain who can operate this scoped tool, its setup and ongoing costs, and these remaining product gaps: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data Avoid guaranteed savings, performance scores or implied endorsement.

## Engineering roadmap
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft. Record prerequisites, select representative user-owned fixtures and document the unsupported features: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data
2. Phase 2 — Durable model. Model topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
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. Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.
4. Phase 4 — Permissions and integration failure. Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections. 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
- SEO datasets, topic clustering, workflow automation, and team features
- proprietary ranking data
- brand-trained models
- team workflows
- large template libraries

## Implementation prompt
WORKING SLICE
Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft.

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

Architecture
- Node.js, TypeScript and Express with server-rendered HTML and small browser modules.
- SQLite through better-sqlite3 with migrations, prepared statements and a single background worker.
- A single provider SDK with a configured supported model, schema-validated responses and revisioned prompt templates.

Prerequisites and limits
A supported Node release, a writable local data directory and a separate backup location. Bind to localhost; remote use requires authentication and HTTPS first. Optional AI generation needs a provider key, a usage budget and approval to send the selected material.
Outside this release: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data

Data model and correctness
topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions
Invariant: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.

Security and privacy
Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication.

Recovery and export
Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Research from a user-approved source set, propose an outline and draft one section at a time after approval. Show claim citations and a transparent term-coverage checklist, then export the research bundle with the draft. Record prerequisites, select representative user-owned fixtures and document the unsupported features: SEO datasets, topic clustering, workflow automation, and team features; proprietary ranking data
2. Phase 2 — Durable model. Model topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
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. Use short SQLite transactions and persist job state before starting work. Give retries stable operation IDs; report incomplete or unknown results instead of silently repeating them.
4. Phase 4 — Permissions and integration failure. Reject unexpected origins and unbounded request bodies even on localhost. Keep credentials outside the database export and redact sensitive text from logs. Treat source documents as untrusted data, prevent them from changing tool permissions, and require review of factual claims before publication. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Use a consistent SQLite backup and an attachment manifest. Export portable JSON/CSV, then restore to a new directory without overwriting the original data. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.
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: [copywriting](https://github.com/coreyhaines31/marketingskills/blob/main/skills/copywriting/SKILL.md) — Write landing pages and product copy grounded in the intended audience, product value and a clear next action. 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.
Optional external skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — Review security-sensitive APIs and configuration for dangerous defaults and easy-to-misuse interfaces. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: topic briefs, permitted source excerpts, approved outlines, term suggestions and article revisions
Project rule — preserve this invariant: Coverage metrics are not search-ranking predictions; facts without source support remain marked unverified.
Project rule — acceptance evidence: Remove a source and flag its dependent claims; an interrupted generation preserves the approved outline and completed sections.

## 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.