# AGENTS.md — Build guide for CueScout ## Project scope Draft buyer questions for a product, approve a fixed sample, query configured answer APIs and compare brand mentions with competitors. Display raw answers beside a transparent mention-rate calculation. Catalogue verdict: kinda. The visibility-check loop is the weekend-buildable half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing. 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 - Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.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: the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API ## 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: buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations ## 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: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. - 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: buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations - Project rule — preserve this invariant: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. - Project rule — acceptance evidence: Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. ## 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 CueScout-inspired workflow with owned or clearly labeled sample data: Draft buyer questions for a product, approve a fixed sample, query configured answer APIs and compare brand mentions with competitors. Display raw answers beside a transparent mention-rate calculation. - Publish a reproducible walkthrough with this observable result: Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. - Explain who can operate this scoped tool, its setup and ongoing costs, and these remaining product gaps: the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API Avoid guaranteed savings, performance scores or implied endorsement. ## Engineering roadmap 1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Draft buyer questions for a product, approve a fixed sample, query configured answer APIs and compare brand mentions with competitors. Display raw answers beside a transparent mention-rate calculation. Record prerequisites, select representative user-owned fixtures and document the unsupported features: the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API 2. Phase 2 — Durable model. Model buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. 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. Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. 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 - the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model - Google rank badges on matched threads, sourced from a paid search API - the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number - weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise - a hosted, shareable report link you can hand a client without exposing your own infra ## Implementation prompt WORKING SLICE Draft buyer questions for a product, approve a fixed sample, query configured answer APIs and compare brand mentions with competitors. Display raw answers beside a transparent mention-rate calculation. Build this scoped CueScout-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: the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API Data model and correctness buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations Invariant: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. 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: Draft buyer questions for a product, approve a fixed sample, query configured answer APIs and compare brand mentions with competitors. Display raw answers beside a transparent mention-rate calculation. Record prerequisites, select representative user-owned fixtures and document the unsupported features: the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API 2. Phase 2 — Durable model. Model buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. 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. Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder. Acceptance Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. 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: buyer-question sets, branded aliases, provider runs, mention spans and citation-domain observations Project rule — preserve this invariant: Any GEO score must expose its formula and denominator; API observations do not establish consumer search rankings or market-wide visibility. Project rule — acceptance evidence: Rename a brand alias and preserve the original raw run while recomputing annotations; a failed query is shown separately from a genuine non-mention. ## 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.