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Free Tool

Generate Codex launchers for repo sweeps without rebuilding the shell script each time.

Use this when you want a reusable runner for prompts-gpt.com hardening passes. Pick the maintained sweep family, set the default model and iteration count, target one functionality or the whole app, and download a launcher that stays aligned with the repo scripts.

Free Tool

Generate a Codex sweep launcher that matches your run.

Configure the runner once, then download a reusable shell launcher for the prompts-gpt.com repo. The generated file points at the existing maintained sweep scripts instead of drifting into a second copy.

Downloads as codex-feature-launcher.sh.

Broadest option. Starts with a whole-codebase review, then implements targeted fixes, features, security, performance, and final validation.

The launcher exports this as the default `CODEX_MODEL`.

Each iteration runs the selected sweep runner once end to end.

Used by the maintained sweep runner when that preset supports parallel passes.

Defaults the launcher to `USE_BACKGROUND_AGENTS=yes|no`.

Review the whole repo across product, prompts, AI visibility, admin, SDK, performance, and SEO.

Only used when the target feature is set to `custom`.

Defaults to `workspace-write`. Override only if your Codex setup needs a different mode.

The launcher auto-detects repo root first. This path is only a fallback when launched elsewhere.

This preset supports direct functionality targeting or a custom sweep label.

Script preview

codex-feature-launcher.sh
What the launcher controls
Sweep family and maintained base runner
Codex model, iteration count, sandbox mode, and log volume defaults
Sequential or parallel implementation mode when supported by the runner
Background-agent default and feature targeting for the broader sweep
Base runner

Feature-target sweep uses .scripts/run-codex-feature-target-sweep.sh.

The downloaded launcher stays thin on purpose, so improvements to the maintained repo runner automatically flow into future runs.

How to use it
Place the launcher in the repo root or inside `.scripts` for automatic root detection.
Runtime overrides still work: `CODEX_MODEL=gpt-5.5 ITERATIONS_PER_PASS=2 ./launcher.sh`.
Feature-target sweeps are the right choice when one workflow or functionality needs a broader review and implementation pass.
Launcher coverage

This is built for repeatable repo sweeps, not one-off shell snippets.

The generator keeps the launch script thin and points it at the maintained repo runners. That means model defaults, iterations, targeting, and run mode can be customized without letting the shell template drift away from the real sweep logic.

Configurable runners

Choose from the maintained AI visibility, feature-target, full-product, prompts, or client SDK sweep families.

Downloadable launcher

Export a ready-to-run shell script instead of rebuilding the same environment variables every time.

Feature targeting

Point the broader sweep at one functionality or run across the full product when the pass needs wider coverage.

When to use feature-target mode

Use the feature-target preset when one functionality needs a broader codebase review, a prioritized to-do list, and staged implementation passes instead of a fixed five-flow sweep.

What gets downloaded

The file is a shell launcher. It discovers the repo root when possible, exports your selected defaults, then hands off to the maintained runner inside `.scripts`.

Public Discovery Kit

Show the same product truth to crawlers, AI systems, and evaluators.

Competitive AI visibility products expose public discovery files, evidence-led docs, and a transparent self-check path. prompts-gpt.com should do the same so the market sees the current AI visibility platform, not older prompt-library snapshots or a rerun URL without context.

Stable proof and live diagnostic separatedResearch reviewed 2026-05-16

Use the live checker for diagnostics. Use the published markdown or JSON self-audit exports when you need a stable public proof artifact. The JSON export also includes the Prompts-GPT.com project preset and monitor blueprint used for the ongoing internal follow-up path.

Public Export Matrix

Make each share surface explicit about stability, format, and audience.

The self-test showed that public discovery surfaces are only credible when buyers can tell which link is stable proof, which one is a live rerun, which files are AI-readable discovery aids, and which path requires sign-in for recurring monitoring.

Self-audit markdown export

markdownStable proof

Stable public proof artifact for external sharing, procurement review, and citations.

Audience: buyer

Open Self-audit markdown export: /reports/prompts-gpt-com-self-audit.md

Self-audit JSON export

jsonStable proof

Machine-readable self-audit payload with credibility notes, export context, and project guidance.

Audience: ai-system

Open Self-audit JSON export: /reports/prompts-gpt-com-self-audit.json

Self-audit article

htmlStable proof

Methodology, current limits, and product-claim boundaries for public evaluation.

Audience: buyer

Open Self-audit article: /articles/prompts-gpt-com-ai-visibility-self-audit

Free checker rerun URL

htmlLive diagnostic

Fresh answer snapshot, source list, and recommendations for live inspection.

Audience: operator

Open Free checker rerun URL: /free-tools/ai-brand-visibility-checker?site=prompts-gpt.com&submitted=1

Official llms.txt

txtAI-readable

Canonical AI-readable source map for the current public product story.

Audience: ai-system

Open Official llms.txt: /llms.txt

robots.txt

txtAI-readable

Crawler access policy for public versus private routes.

Audience: crawler

Open robots.txt: /robots.txt

sitemap.xml

xmlAI-readable

Canonical public URL inventory for discovery and recrawl.

Audience: crawler

Open sitemap.xml: /sitemap.xml
Inspection Matrix

Make the public proof boundary explicit for every credibility check.

The self-audit should not force evaluators to infer which parts are inspectable now, which are only directional, which require a saved monitor, and which require sign-in. The matrix below makes that boundary machine-readable and visible on the page.

Answer snapshots

Inspectable now

A credible AI visibility product should show the actual answer snippet instead of summarizing results as a score only.

Inspect by

Run the checker on prompts-gpt.com, read the answer snippet and full-answer evidence, then preserve that run with the checker markdown or JSON download.

Share policy

Use checker-run markdown or JSON when sharing one diagnostic run. Use the self-audit export and article for durable product-level proof.

Citations and source evidence

Inspectable now

Buyers need to see whether owned pages, third-party reviews, or comparison pages are actually supporting the answer.

Inspect by

Inspect detected source URLs, provider-verified flags, top industry sources, and source labels in the live checker result or the checker-run exports.

Share policy

Treat citation lists from one checker run as directional evidence. Use the self-audit export to explain what the product can prove publicly today.

Recommendations

Inspectable now

Discovery tooling is only useful when the answer evidence turns into concrete next actions instead of a generic score.

Inspect by

Review the checker recommendations below the answer snapshot and compare them with the documented workflow in the public docs.

Share policy

Recommendations from one run are acceptable to share as tactical diagnostics, but not as recurring trend proof.

Opportunity backlog

Inspectable now

Operators need a clear backlog that connects missing mentions or weak citations to pages, briefs, FAQs, or source fixes.

Inspect by

Inspect the opportunity cards and exported opportunity payload from the live checker run for prompts-gpt.com.

Share policy

Use checker-run exports for run-specific opportunities. Use the self-audit export to explain the proof boundary between one run and recurring monitoring.

Scoring and confidence

Directional only

Single-run scores are easy to overclaim. Public proof should state that a preview score is directional until repeated prompt tracking exists.

Inspect by

Compare the preview score against the benchmark prompt pack and the scoring limitation notes before treating it as a durable benchmark.

Share policy

Do not use a single rerun score as a long-term performance claim. Use repeated monitoring for trend or share-of-voice claims.

Crawler-to-citation logic

Monitor only

Connecting crawler events to later citations is valuable, but the free checker should not imply that capability without a saved monitor.

Inspect by

Review the public feature and docs pages for the workflow description, then confirm the actual crawler-to-citation evidence only inside a saved monitor.

Share policy

Keep crawler-to-citation claims at the workflow and methodology level in public materials. Do not present them as checker output.

Project guidance and recurring setup

Requires auth

A public self-check should hand off cleanly into a real recurring project when the platform's own prompt set is worth monitoring.

Inspect by

Use the built-in Prompts-GPT.com project preset and baseline monitor prompts after sign-in, then rerun the benchmark prompt pack as a saved project.

Share policy

Public proof can link to the authenticated handoff path, but project creation and recurring monitor evidence remain account-bound.

Benchmark Prompt Pack

A credible self-test needs more than one branded rerun prompt.

The branded checker prompt is useful for a first-pass diagnostic, but recurring self-evaluation should also test category, competitor, recommendation, local, and buying-intent prompts. Those benchmark prompts are exported with the self-audit so the proof path stays reproducible.

Analyze AI visibility for prompts-gpt.com. Is the brand understood, mentioned, cited, and positioned accurately in AI answers?

diagnostic

Useful as a first-pass branded diagnostic, but still biased because the prompt names the brand directly.

best AI search visibility platforms for marketing teams

category

Expands self-evaluation beyond the branded diagnostic into category-level AI visibility prompts.

tools for monitoring brand visibility in ChatGPT and Perplexity

category

Expands self-evaluation beyond the branded diagnostic into category-level AI visibility prompts.

prompts-gpt.com alternatives for AI visibility monitoring

competitor

Checks whether prompts-gpt.com is explainable in comparison and alternative workflows instead of only branded prompts.

prompts-gpt.com vs PromptWatch for answer engine optimization

competitor

Checks whether prompts-gpt.com is explainable in comparison and alternative workflows instead of only branded prompts.

which AI visibility tools should an SEO agency compare

recommendation

Tests whether the platform appears in shortlist and buyer-guidance prompts where evaluators expect independent recommendations.

recommend software for tracking AI answer citations and brand mentions

recommendation

Tests whether the platform appears in shortlist and buyer-guidance prompts where evaluators expect independent recommendations.

AI search visibility tools for teams in the United States

local

Verifies whether region-specific discovery claims hold outside the core branded story.

AI visibility software pricing and features for a growing brand

buying_intent

Checks whether pricing and feature pages support commercial AI answers with enough proof.

which AI brand monitoring platform should I buy

buying_intent

Checks whether pricing and feature pages support commercial AI answers with enough proof.

Competitive Bar

Public AI visibility products are judged on proof, not only feature copy.

Competitor research consistently raises the same bar: exportable evidence, shareable reporting, public methodology, and a clean handoff into recurring monitoring. This kit exists so prompts-gpt.com reads like an AI Search visibility platform rather than a generic dashboard with AI-themed marketing.

Stable public proof export

supported publicly

Buyers need a shareable artifact that does not mutate when answer providers change.

Machine-readable evidence export

supported publicly

AI systems, evaluators, and internal tools need a structured payload instead of screenshot-only proof.

Live diagnostic rerun

supported with limits

Operators need a fresh answer snapshot to inspect citations, entities, and recommendations on demand.

Public methodology and claim boundaries

supported publicly

Trust depends on explaining what scoring, citations, and crawler context can prove versus what requires saved monitoring.

Recurring reporting handoff

monitor only

A credible platform must convert a public diagnostic into a saved project when visibility work becomes ongoing.

PDF and CSV export with citation detail

supported with limits

Stakeholders expect downloadable brand reports and citation spreadsheets with full and summary modes.

Scheduled report delivery

supported with limits

Recurring automated email reports eliminate manual export steps for teams that report AI visibility to leadership.

Shareable online dashboards

monitor only

Agencies and teams need live dashboard links that stakeholders can access without account setup.

BI connector (Looker Studio)

monitor only

Data teams need live structured feeds into existing dashboards and reporting pipelines.

Content briefs from AI insights

supported with limits

Visibility data should produce concrete content recommendations, not just metrics.

Prompt gap and competitor gap analysis

supported with limits

Buyers need to identify exactly which prompts competitors own and where the brand is absent.

Geo distribution tracking

supported with limits

Global brands need to understand which countries and regions provide their highest AI visibility.

Research Standard

Public proof expectations were checked against live category examples.

This self-audit did not invent a reporting standard. The research pass checked how adjacent AI visibility products explain citations, documentation, and exports in public before setting the proof policy for prompts-gpt.com.

Semrush AI Visibility Toolkit

Recurring reporting, prompt research, and shareable AI visibility dashboardsReviewed 2026-05-16

Semrush publicly documents prompt research, visibility overview, brand performance reporting, PDF exports, CSV exports, and shareable online dashboards inside the AI Visibility Toolkit.

Why it matters: Buyers now expect AI visibility tools to move from one-off scans into recurring reporting and stakeholder-ready sharing.

Semrush KB - Getting Started with the AI Visibility Toolkit

Peec AI

AI search analytics, source influence metrics, and BI-friendly reportingReviewed 2026-05-16

Peec documents visibility, position, sentiment, source-used metrics, a documentation llms.txt index, and a Looker Studio connector for reporting.

Why it matters: Clear metric definitions, machine-readable docs, and reporting connectors make public product claims easier to trust.

Peec AI Docs - Welcome and Looker Studio connector

Profound

Answer-engine tracking plus actions and query fanout analysisReviewed 2026-05-16

Profound publicly documents visibility tracking across major answer engines, query fanouts, actions, and customer workflows tied to AI search outcomes.

Why it matters: Public product stories in this category increasingly connect measurement to concrete action workflows instead of isolated scoring.

Profound - Introducing Actions and Query Fanouts

Scrunch

Citation drill-down and source-level influence reviewReviewed 2026-05-16

Scrunch publicly documents citation grouping by domain or URL, platform filtering, and citation-level influence analysis in its help center.

Why it matters: Answer evidence without source drill-down is weak; buyers want to inspect which domains influence mentions and citations.

Scrunch Help Center - Understanding the Citations Tab in Scrunch

OtterlyAI

Export-oriented reporting and GEO audit artifactsReviewed 2026-05-16

Otterly publicly documents CSV exports for prompts and citations, a GEO Audit PDF export, a Looker Studio connector, and explicit boundaries for what is not exportable yet.

Why it matters: Claim boundaries matter in this category; precise public export disclosures are more trustworthy than broad unsupported promises.

Otterly Help - Can I export my data and reports?

Ahrefs Brand Radar

Search-backed prompt database with free AI visibility checker and cited domain analysisReviewed 2026-05-16

Ahrefs publicly provides a free no-signup AI visibility checker with mentions by platform, top 5 topics, top 5 cited domains, and top 5 cited pages. Brand Radar tracks 400M+ monthly search-backed prompts derived from real search behavior across ChatGPT, Gemini, Perplexity, Copilot, AI Overviews, AI Mode, and Grok.

Why it matters: A free checker with no signup creates a strong top-of-funnel acquisition loop. Search-backed prompts from real user behavior are more trustworthy than synthetic question sets. Cited domain and cited page analysis gives actionable source intelligence.

Ahrefs - Free AI Visibility Checker and Brand Radar
Self-Evaluation Risks

Public proof should include what is still weak.

A credible AI visibility platform should publish risks discovered during self-testing, not only positive feature claims. These risks are mirrored in the self-audit markdown and JSON exports for external review.

Legacy prompt-library snippets still outrank AI visibility positioning for some queries

high

Evidence: Public search results still surface the /prompts listing with a legacy 'Best AI Prompts Library' snippet while the homepage describes prompts-gpt.com as an AI visibility platform.

Impact: Evaluators can misclassify prompts-gpt.com as a generic prompt library instead of an AI Search Visibility platform.

Action: Keep /, /features, /pricing, /resources, /docs, /articles, and self-audit exports internally consistent; keep canonical AI visibility positioning in llms.txt and avoid reintroducing prompt-library-first metadata.

Live rerun links are easy to treat as permanent proof

medium

Evidence: The free checker URL is public and shareable, but the output can change as providers, citations, and model behavior shift.

Impact: External stakeholders may treat mutable diagnostics as stable evidence and misread score changes as regressions.

Action: Use markdown/JSON exports and the self-audit article as the default proof artifacts in public sharing and documentation.