# AGENTS.md — Build guide for 100 Questions ## Project scope Run a reviewed five-question pilot across configured search-capable model providers, then compare brand mentions and owned-domain citations in an evidence-linked report. Catalogue verdict: kinda. A personal CLI that asks the same questions across four model APIs and compares the answers is weekend-buildable, but matching the product's web-grounded runs, source normalization, failure handling, durable evidence, scoring, and polished reports takes substantially more work. 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 - Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.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 - Runtime and tools: Node, TypeScript, Commander, better-sqlite3 and the configured providers' documented SDKs. - Before starting: A Node runtime, provider keys, reviewed question JSON and editable model/pricing configuration; use a five-question pilot before larger runs. ## Stack and architecture - Node, TypeScript, Commander, better-sqlite3 and the configured providers' documented SDKs - Data design: Store QuestionSet, Run, ProviderAttempt, Citation and CostReservation; a provider denominator includes completed grounded answers only, while failed, uncited and unknown outcomes stay visible. - Setup: A Node runtime, provider keys, reviewed question JSON and editable model/pricing configuration; use a five-question pilot before larger runs ## Security and data integrity - Keep source evidence, model/config version, draft output and reviewer changes separately. Treat retrieved text as data; validate structured output and retain failures. Never silently send private material to a fallback provider. - Preserve identical question text and model/tool settings per comparison. Estimated reservations are not guaranteed spend caps, and API answers do not represent consumer search-interface rankings. - Keep secrets outside client bundles and exported projects; document what leaves the device and make retention/deletion controls visible. ## Agent implementation rules - Project rule — domain: Store QuestionSet, Run, ProviderAttempt, Citation and CostReservation; a provider denominator includes completed grounded answers only, while failed, uncited and unknown outcomes stay visible. - Project rule — scope and recovery: Preserve identical question text and model/tool settings per comparison. Estimated reservations are not guaranteed spend caps, and API answers do not represent consumer search-interface rankings. - Project rule — acceptance: Run three questions where one provider times out and another returns no citations; neither outcome may inflate grounded coverage or silently trigger a second charged request. - Project rule — delivery: document real setup commands and permissions; do not claim a build, accuracy level, performance result or security certification that has not been demonstrated. ## Optional agent skills and references - Recommended skill: [sharp-edges](https://github.com/trailofbits/skills/blob/main/plugins/sharp-edges/skills/sharp-edges/SKILL.md) — review configuration and API defaults against the app-specific invariants and recovery boundaries above; this is not a security certification. Follow the maintainer's installation instructions and match its requirements to the chosen runtime. - Recommended skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — review keyboard access, focus, validation, error recovery and the readable work/review interface or HTML report. Follow the maintainer's installation instructions and match its requirements to the chosen runtime. 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 this working slice using synthetic or explicitly authorized non-sensitive examples: Run a reviewed five-question pilot across configured search-capable model providers, then compare brand mentions and owned-domain citations in an evidence-linked report. - Share a synthetic example export and the acceptance walkthrough; keep real customer, health, financial and source data private: Run three questions where one provider times out and another returns no citations; neither outcome may inflate grounded coverage or silently trigger a second charged request. - State the limits before asking someone to replace their existing tool: Preserve identical question text and model/tool settings per comparison. Estimated reservations are not guaranteed spend caps, and API answers do not represent consumer search-interface rankings. ## Engineering roadmap 1. Phase 1 — Pin the working slice and create its example input: Run a reviewed five-question pilot across configured search-capable model providers, then compare brand mentions and owned-domain citations in an evidence-linked report. Confirm setup: A Node runtime, provider keys, reviewed question JSON and editable model/pricing configuration; use a five-question pilot before larger runs. 2. Phase 2 — Implement durable result files and command invariants before formatting terminal output: Store QuestionSet, Run, ProviderAttempt, Citation and CostReservation; a provider denominator includes completed grounded answers only, while failed, uncited and unknown outcomes stay visible. 3. Phase 3 — Connect CLI commands to real saved state and explicit result/exit statuses. Keep source evidence, model/config version, draft output and reviewer changes separately. Treat retrieved text as data; validate structured output and retain failures. Never silently send private material to a fallback provider. 4. Phase 4 — Expose the app-specific limits and recovery path in context: Preserve identical question text and model/tool settings per comparison. Estimated reservations are not guaranteed spend caps, and API answers do not represent consumer search-interface rankings. 5. Phase 5 — Walk through this concrete acceptance case and preserve its exported evidence: Run three questions where one provider times out and another returns no citations; neither outcome may inflate grounded coverage or silently trigger a second charged request. Finish the README and backup/restore instructions; report unfinished capabilities explicitly. ## Paid-product capabilities outside this build - reliable orchestration and retries across four providers - normalized citations and evidence-linked metrics - competitor and missed-question extraction - stored point-in-time reports and comparisons - polished exports and action recommendations ## Implementation prompt Build me a local four-provider AI visibility benchmark CLI as a limited substitute for 100 Questions. Requirements: - Use Node, TypeScript, Commander, and better-sqlite3. Use the official openai, @anthropic-ai/sdk, and @google/genai packages, plus xAI's documented HTTPS API through fetch; keep model IDs and search-tool options in config rather than hardcoding dated versions. - Generate 25 buyer questions from a supplied brand description, or import a reviewed JSON question list. Show the list before sending anything and default to a five-question pilot; a full run needs an explicit flag. - Send identical question text to each configured provider with its documented web-search capability. If search or citation metadata is unavailable, record that outcome rather than treating an uncited answer as web-grounded. - Before each call, reserve a configurable estimated maximum from remaining run budget using the user's price table, max output tokens, and search allowance. Stop dispatch when it will not fit; show that actual provider charges may differ and are not a guaranteed hard cap. - Store request, provider/model/tool settings, response, citation metadata, usage, status, and attempt time in SQLite. Retry only definite rate-limit or transient failures; mark ambiguous timeouts 'unknown' for manual retry to avoid duplicate charges. - Compute brand and competitor mentions and owned-domain citations from completed grounded answers only. Report per-provider completed, failed, and unknown counts as denominators; retain raw evidence beside every metric. - Export static escaped HTML and CSV. A SHA-256 digest may detect accidental file changes but must not be called tamper-proof. Keep API keys in .env and out of reports; no telemetry. README: provider setup, current pricing checks, cost uncertainty, and partial-run recovery. EDITORIAL IMPLEMENTATION CONTRACT Working slice: Run a reviewed five-question pilot across configured search-capable model providers, then compare brand mentions and owned-domain citations in an evidence-linked report. Data and invariants: Store QuestionSet, Run, ProviderAttempt, Citation and CostReservation; a provider denominator includes completed grounded answers only, while failed, uncited and unknown outcomes stay visible. Boundary and recovery: Preserve identical question text and model/tool settings per comparison. Estimated reservations are not guaranteed spend caps, and API answers do not represent consumer search-interface rankings. Acceptance walkthrough: Run three questions where one provider times out and another returns no citations; neither outcome may inflate grounded coverage or silently trigger a second charged request. Record actual dependency versions, permissions and provider access in setup instructions. Preserve originals, expose partial failures and document backup/restore. These are acceptance requirements, not a claim of a completed or production-certified build. Add the domain, recovery and acceptance rules to AGENTS.md so future edits preserve them. ## 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.