Show HN: Open-source tool for reverse engineering ChatGPT queries about brands

Description and use

**Brief Summary (2‑3 sentences)**
Show HN: Open‑source tool for reverse‑engineering ChatGPT queries about brands is a lightweight utility that lets developers extract and analyze the prompts users give ChatGPT when discussing specific brands. By parsing the model’s request logs, the tool can surface hidden marketing insights, sentiment trends, and competitive intelligence that would otherwise remain buried inside the AI’s responses.

**Value**

• **Brand intelligence** – Turns opaque ChatGPT interactions into actionable data, helping marketers, brand managers, and researchers understand how their brand is being talked about in real‑time.
• **Open‑source & extensible** – The code can be forked or integrated into existing analytics pipelines, allowing custom enrichment (e.g., linking to CRM records or sentiment APIs).
• **Rapid prototyping** – Because it works directly on ChatGPT query logs, teams can start extracting insights without building a full‑stack data‑capture system.

**Practical Adoption Path**

1. **Clone & review** – Fork the repository, verify the license (MIT/Apache‑style) and inspect the README for usage instructions.
2. **Set up a sandbox** – Deploy the tool in a controlled environment (e.g., a Docker container) and point it at a sample set of ChatGPT logs that contain brand‑related queries.
3. **Validate output** – Run the provided examples, compare the extracted prompts with the original logs, and adjust the parsing rules if needed.
4. **Integrate** – Wrap the extraction script in a scheduled job or webhook that feeds results into your analytics stack (e.g., Snowflake, Elasticsearch, or a BI dashboard).
5. **Monitor & maintain** – Add unit tests, pin dependencies, and configure alerts for breaking changes in the underlying OpenAI API.

**Production Readiness**

• **Maturity:** Medium. The project is recently updated (2026‑06‑23) and has minimal documentation, so it is suitable for prototypes or internal tooling rather than a mission‑critical service.
• **Dependencies:** Small, but you should audit third‑party libraries for security and version compatibility.
• **Maintenance:** Activity is sparse; plan for an internal maintainer to handle bug fixes, API changes, and any required feature extensions.
• **Risk mitigation:** Conduct a license review, run static‑code analysis, and establish a release cadence (e.g., weekly builds) before promoting the tool to production.

Overall, the tool offers a compelling way to surface brand‑specific insights from ChatGPT usage, but it requires careful validation and an internal commitment to upkeep before being used in a production environment.

Use in a workflow

Show HN: Open-source tool for reverse engineering ChatGPT queries about brands may be useful when its README and activity match a concrete workflow.

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.