AmICited

WORKING SLICE
Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples.

Build this scoped AmICited-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: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.

Data model and correctness
buyer questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs
Invariant: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
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: Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples. Record prerequisites, select representative user-owned fixtures and document the unsupported features: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.
2. Phase 2 — Durable model. Model buyer questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
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. A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.
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 questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs
Project rule — preserve this invariant: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
Project rule — acceptance evidence: A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.

# AGENTS.md — Build guide for AmICited

## Project scope
Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples.

Catalogue verdict: kinda. The scoring loop is genuinely weekend-buildable, but a faithful replacement is not, and that gap is the honest reason to keep paying. The naive personal build sends your questions through the model APIs, but API answers are not what a real user sees when they open ChatGPT, Perplexity, or AI Overviews, so that number is only a proxy. AmICited does not use LLM APIs at all: it drives real browsers to ask the actual consumer surfaces the way a person in a given country would, a browser-automation fleet routed through country-level proxies, kept working as every surface changes. The other gaps are the historical archive that compounds from day one and cannot be backfilled, an MCP server that lets your AI agents act on your visibility gaps, and, on higher plans, human AEO consulting from real experience that no script replaces. You can build the weekend proxy; the prompt below is that honest consolation build, with its limits stated plainly.
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: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.

## 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 questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs

## 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: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
- 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 questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs
- Project rule — preserve this invariant: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
- Project rule — acceptance evidence: A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.

## 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 AmICited-inspired workflow with owned or clearly labeled sample data: Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples.
- Publish a reproducible walkthrough with this observable result: A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.
- Explain who can operate this scoped tool, its setup and ongoing costs, and these remaining product gaps: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces. Avoid guaranteed savings, performance scores or implied endorsement.

## Engineering roadmap
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples. Record prerequisites, select representative user-owned fixtures and document the unsupported features: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.
2. Phase 2 — Durable model. Model buyer questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
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. A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series. 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 faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers
- country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up
- scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring
- months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late
- human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces

## Implementation prompt
WORKING SLICE
Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples.

Build this scoped AmICited-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: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.

Data model and correctness
buyer questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs
Invariant: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
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: Run a fixed set of buyer questions against one supported answer API and store the raw answers with model, time and retrieval settings. Compare brand mentions and citation domains over repeat runs, showing the denominator and failed samples. Record prerequisites, select representative user-owned fixtures and document the unsupported features: No browser evasion, proxy fleet, historical backfill or claim to measure real consumer search surfaces.
2. Phase 2 — Durable model. Model buyer questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
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. A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.
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 questions, brands and aliases, engine configurations, immutable runs, raw answers and extracted citation URLs
Project rule — preserve this invariant: API answers are a sampled proxy, not evidence of consumer-app visibility; missing provider citations remain missing rather than inferred.
Project rule — acceptance evidence: A failed provider run is excluded with a visible failure count; identical prompts with different model settings are displayed as separate series.

## 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.