Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #2,798 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
The description states that auditooor is a tool for auditing web3 projects. It uses agents to drive audits through a pipeline and produce findings for project fixes. The author describes it as being built with tools like Foundry, Echidna, GitHub, and Python.
What changed
No evidence of prior versions or evolution is provided. This appears to be a self-reported, unverified, early-stage concept submitted to a hackathon.
Single most important open question
Is there any evidence of actual usage, traction, or revenue generation from this tool? The description does not indicate whether auditooor has been used by any web3 projects, nor does it show adoption or feedback from users.
What The Product Actually Is
The description states that auditooor is a tool for auditing web3 projects. It claims to use agents to drive audits through a pipeline and produce findings for projects to fix. The author notes that it was built with tools such as Foundry, Echidna, GitHub, and Python.
Evidence
- The description states: “Audit any web3 project. Drop scope, impacts, previous audits, github repo. Agents drive it to the pipeline and produce findings for projects to fix.”
- The author lists technologies used: echidna, foundry, github, mcp, python, solidity.
Inference It is inferred that auditooor may be an automated or semi-automated auditing tool for smart contracts in web3 environments, using agent-based workflows and leveraging existing tools like Foundry and Echidna.
Positioning & Claim Evolution
The description states that auditooor audits web3 projects and uses agents to drive the audit process. It claims to produce findings for projects to fix.
Evidence
- The tagline: “Audit any web3 project. Drop scope, impacts, previous audits, github repo. Agents drive it to the pipeline and produce findings for projects to fix.”
Inference It is inferred that auditooor positions itself as an automated or semi-automated auditing solution for web3 developers, possibly targeting smart contract security. The claim of using “agents” suggests a move toward autonomous or AI-assisted auditing.
Target Customer & ICP
The description does not state who the target customer is or what the ideal customer profile (ICP) might be.
Evidence
- No mention of specific customer segments, use cases, or personas.
Inference It is inferred that the target customers may be web3 developers or teams working on smart contracts, but this is not confirmed in the description.
Business Model & Pricing Evidence
The description does not state anything about a business model or pricing structure.
Evidence
- No mention of monetization, pricing tiers, or revenue streams.
Inference It is inferred that auditooor may be a tool for developers to use, but no evidence exists regarding how it would be monetized or whether it has a commercial offering.
Technical & Delivery Signals
The description states that auditooor uses tools like Foundry, Echidna, GitHub, and Python. It also mentions that agents drive audits through a pipeline.
Evidence
- Built with: echidna, foundry, github, mcp, python, solidity.
- The author states: “Agents drive it to the pipeline and produce findings for projects to fix.”
Inference It is inferred that auditooor may be built on a stack that supports smart contract testing and automation. The use of agents suggests an integration with AI or workflow automation tools.
Traction & Maturity Signals
The description does not provide any evidence of traction, adoption, or maturity.
Evidence
- No mention of users, customers, or adoption.
- No revenue or ARR figures.
- No product usage data.
Inference It is inferred that auditooor is likely in an early stage (e.g., hackathon submission) and lacks evidence of real-world use or traction.
Competitive Context
The description does not provide any information about the competitive landscape.
Evidence
- No mention of competitors, market positioning, or differentiation.
Inference It is inferred that auditooor may be entering a space with existing players in smart contract auditing (e.g., MythX, Slither, CertiK), but no evidence supports this claim.
Key Risks & Red Flags
- No traction or adoption: The tool appears to be unproven in real-world use.
- Unverified claims: All statements are self-reported and unverified.
- Lack of commercial clarity: No indication of how the product will generate revenue.
- Early-stage concept: Submitted to a hackathon, suggesting it is not yet mature.
Diligence Questions To Ask The Founders
- What specific web3 projects have you used auditooor on?
- How does auditooor differ from existing tools like MythX or Slither?
- Is there any feedback or usage data from developers who tried the tool?
- What is your plan for monetizing auditooor?
- How do you intend to scale the agent-based auditing system?
Investment/Partnership Verdict
Not evidenced.
The description does not provide sufficient evidence of traction, revenue, or customer adoption to support an investment or partnership decision. The tool appears to be a self-reported concept submitted to a hackathon and lacks any indication of commercial viability or market validation.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.