Bounty Operator

Make your strongest case before you submit.

Check your proof, make the impact explicit, and catch missing evidence before the triager has to ask.

Give Bounty Operator the draft and the code it cites. It checks every claim against the supplied evidence, confirms supported claims and identifies gaps and counterarguments to address. You get one verdict: submit, rewrite-then-submit, prove-first, hold-duplicate or drop. It runs on your own model: in the browser, in your coding agent over MCP, or from the command line.

Start a free review at bountyoperator.com

• Every claim tied to file and line. Findings cite input-1/src/Vault.sol:142-158. A reference to a file or line you did not supply is flagged. Each review downloads as a packet with a SHA-256 manifest of exactly what was reviewed.

• Counterarguments checked against evidence. Each finding carries its strongest counterargument, whether the source resolves it, and the one missing artifact that would settle it.

• Your model and your key. Nothing is stored. OpenRouter, Anthropic, OpenAI, Gemini, xAI, DeepSeek, Mistral, Groq, Novita AI, Nebius Token Factory, Infron, OrcaRouter or Bounty Operator with your own key. A hosted review passes your files and your key through bountyoperator.com to that provider. Source files, keys and review text are never stored.

Quick start

Browser

Open bountyoperator.com and run the built-in example. No account, no API key.

Have a Novita account? Select Novita AI and bring your Novita key. It bills model usage separately; Bounty Operator's hosted-review account and quota requirements still apply. The Novita setup guide separates optional affiliate signup from direct key management and discloses our commission. Local MCP uses NOVITA_API_KEY; remote MCP uses provider: "novita" with the key in X-Provider-Key.

Have a Nebius Token Factory account? Select Nebius Token Factory and bring your Nebius key. It bills model usage separately, and we receive no commission; Bounty Operator's hosted-review account and quota requirements still apply. See the Nebius setup guide. Local MCP uses NEBIUS_API_KEY; remote MCP uses provider: "nebius" with the key in X-Provider-Key.

Hosted model API

Use the cyber-model API pilot from Chat Completions clients at https://bountyoperator.com/v1. Sign in, add prepaid credit, and create an expiring key with its own spending limit. The live catalog lists the model, exact revision, rates and limits. The monthly Operator plan includes US$2 API credit per paid month; you can also buy prepaid credit without a subscription. Auto top-ups are off by default and require opt-in.

Deployment and payment verification: hosted API operations.

MCP

Run reviews from Claude Code, Codex, Cursor, OpenCode or any MCP client. The remote endpoint needs nothing installed.

Claude Code:

claude mcp add --transport http bounty-operator https://bountyoperator.com/api/mcp

Codex:

codex mcp add bounty-operator --url https://bountyoperator.com/api/mcp

Cursor, in ~/.cursor/mcp.json:

{ "mcpServers": { "bounty-operator": { "url": "https://bountyoperator.com/api/mcp" } } }

OpenCode v1, merged into the project's opencode.json:

{ "mcp": { "bounty-operator": { "type": "remote", "url": "https://bountyoperator.com/api/mcp", "oauth": false } } }

OpenCode v2 puts the server entry under mcp.servers instead of directly under mcp. Run opencode --version before choosing the config, then opencode mcp list to check the connection. The core workflow uses your agent's model and needs no Bounty Operator account. Versioned configurations and a first-review request include local setup and optional hosted-review credentials.

The local server reads files by path and keeps review preparation on your machine. It needs Node 22 or later:

npx -y https://bountyoperator.com/dl/bounty-operator-mcp.tgz

claude mcp add --transport stdio bounty-operator -- npx -y https://bountyoperator.com/dl/bounty-operator-mcp.tgz
codex mcp add bounty-operator -- npx -y https://bountyoperator.com/dl/bounty-operator-mcp.tgz

The package is coming to npm as bounty-operator-mcp. Until then the tarball above is the install, and its SHA-256 is at bountyoperator.com/dl/SHA256SUMS.txt.

The remote endpoint has five tools and the local server six. list_profiles, prepare_review and build_packet need no account, and neither does the local server's run_gauntlet_plan. prepare_review takes the three core profiles and your agent's own model writes the review. A connection token from the account panel adds run_review and account. run_review runs any profile as a hosted review and is the only way to run a hosted one: prepare_review refuses it with hosted_profile. Token setup for each client: bountyoperator.com/mcp and mcp/README.md.

CLI

Python 3.10 or newer. No dependencies.

pip install "git+https://github.com/bountyoperator/bounty-operator@v0.9.21"
bounty-kit agent-pack ./agent-pack.md --target "Example Protocol" --program "Example bounty"

Hand agent-pack.md to your AI agent with the scope, the known issues and the current commit. The CLI reference covers the other commands.

Related MCP server: leveret

Review profiles

Pick one profile per review. Each returns the same structure: verdict, findings with references, counterarguments, evidence gaps, and what was checked and found safe.

The three core profiles, the free tools, the CLI and the MCP server are MIT and in this repository. The gauntlet and the panel run on the hosted service, on your own AI provider API key.

• Core profiles run anywhere: hosted, exported as a prompt to a chat subscription or a local model with paste-back, or prepared over MCP for your agent's own model.

• Hosted profiles run at bountyoperator.com or through run_review over MCP. Your files and your key go through the service to your provider, and the service adds the method. Free runs 1 review a day (reset at 00:00 UTC), any profile. The method is not in this repository: a checkout runs the hosted profiles on the short stand-in instructions in web/src/operator-profiles.stub.mjs.

Two runs chain the profiles on Operator:

• Gauntlet. One run through eight stages, in the order that ends a weak report early: scope, provenance, prior art, proof, severity, triager, report, verdict. It returns one verdict, one blocker, the cheapest action that removes it and a filing deadline.

• Panel review. Two to four models review the same files in parallel. A cross-examination pass keeps the findings the cited lines prove.

Why reports get closed, check by check: bountyoperator.com/method.

Free tools

No account. Everything runs in your browser at bountyoperator.com/tools. The CLI column is the same check on your own machine.

Report templates for Immunefi, Sherlock, Cantina, HackerOne and GitHub private vulnerability reports, and a Foundry PoC scaffold that ends on the impact assertion.

Skills and Claude Code plugin

Five skills for coding agents. In Claude Code and omp the plugin also adds the Bounty Operator MCP server.

Claude Code:

claude plugin marketplace add bountyoperator/bounty-operator
claude plugin install bounty-operator@bounty-operator

Codex, Cursor and other agents:

npx skills add bountyoperator/bounty-operator

omp:

omp plugin marketplace add bountyoperator/bounty-operator
omp plugin install --scope user bounty-operator@bounty-operator

The gauntlet needs a connection token and a provider key. Set BOUNTY_OPERATOR_TOKEN and BOUNTY_OPERATOR_PROVIDER_KEY before you start the agent. Other clients add the server with the commands at bountyoperator.com/mcp.

The three review skills carry their full method and run on your own model with no account. The hosted stages run on the Bounty Operator server and are not in this repository.

On an Immunefi programme, add Immunefi Studio's official MCP server next to this one. The report challenge and gauntlet resolve the programme with list_programs, which returns names and slugs—not scope or rules. They collect the official programme terms separately. With existing Blueprint access, they read the relevant report sections and cited source files for the finding's chain and keep them as evidence, not proof. The gauntlet also reads Instascope's captured state and proxy history when available, preserving stale or missing-data warnings. The setup guide explains the tools and access limits. If Studio is not already connected, create a personal access token at studio.immunefi.com/agents, then:

claude mcp add --transport http immunefi-studio https://studio.immunefi.com/api/mcp --header "Authorization: Bearer $IMMUNEFI_STUDIO_TOKEN"

Restart your agent and ask “Who am I in Studio?” to check the account through whoami. The token goes from your agent to Immunefi; Bounty Operator never receives it. Keep it out of review files. The gauntlet writes to your Studio workspace only when you say yes; neither workflow starts a trial, unlocks a programme or changes a subscription.

Paydirt benchmark

Paydirt measures which model finds the planted bug in a held Solidity or TypeScript case, leaves the fixed twin alone and catches an overclaimed draft report, and what a run costs. Every model runs through one agent harness at its highest reasoning effort, and a pair counts only when both twins are right.

Leaderboard: bountyoperator.com/benchmark. Method: bench/METHOD.md. After cloning this repository, check the published numbers with Node 22 or later:

node bench/bench.mjs verify --results web/public/bench/latest.json --archive web/public/bench/paydirt-2026-10-public.tar.gz

Pricing

Free: 1 review a day, any review type. Operator: US$10/month for unlimited reviews, the Gauntlet, Panel review and 4 reviews at once, including US$2 API credit per paid month. Other model providers bill usage separately. Existing weekly subscriptions retain their Stripe billing interval and do not receive the monthly credit. The CLI, the core profiles' prompt export and MCP prepare, and the free tools cost nothing.

Built by Tradi3

77th on Immunefi's all-time leaderboard on 10 October 2026, where the profile is marked Top Ranked.

2nd of 133 in Immunefi's Firelight competition, 8th of 65 in Quantus and 15th of 186 in ENS, with 17 valid Critical submissions, the most on the board.

ENS credits 23 valid submissions: 1 Chief and 22 duplicates, plus 1 separate Insight. 17 of the 23 are rated Critical, 1 High, 4 Medium and 1 Low.

Sherlock profile · Audit portfolio

CLI reference

scope -> commit -> tools -> triage -> PoC -> prior art -> report -> sanitize

Install from a clone:

git clone https://github.com/bountyoperator/bounty-operator.git
cd bounty-operator
python -m pip install .
bounty-kit --help

Agent pack

bounty-kit agent-pack ./agent-pack.md \
  --target "Example Protocol" \
  --program "Example Contest" \
  --tool git \
  --tool rg \
  --tool Foundry \
  --tool Slither \
  --tool Aderyn

The pack tells the agent:

• what it verifies before a lead becomes a finding

• how to use scanners for leads only

• how to prove impact with a clean PoC

• how to check known issues and duplicates

• what stays out of a report

agent-brief writes the shorter version for one surface. init-ledger writes the ledger the agent keeps: target, deployed address, hard gates, attack surface, tool runs, leads, duplicate-risk checks, the strongest likely rejection and the report-readiness gate. Both refuse to overwrite an existing file without --force. See docs/agent-pack.md.

Prior art

bounty-kit prior-art ./finding-summary.md \
  --target "Vault" \
  --repo "https://github.com/example/protocol"

Prints the rg and git log commands, the web searches and a comparison table for the finding. A prior source with the same root cause, or a prior fix that would also fix the candidate, sets the disposition to HOLD — known/duplicate risk until specific evidence shows a distinct issue.

AI review with your own key

ai-review is the only command that makes a network request. It sends the files you name on the command line and nothing else.

export BOUNTY_KIT_AI_API_KEY="..."
bounty-kit ai-review ./src/Vault.sol ./finding.md --prompt ./prompts/poc-reviewer.md

PowerShell:

$env:BOUNTY_KIT_AI_API_KEY = "..."
bounty-kit ai-review .\src\Vault.sol .\finding.md --prompt .\prompts\poc-reviewer.md

The default endpoint is OpenAI with gpt-6.1-sol. Any OpenAI-compatible endpoint works, including a local one:

bounty-kit ai-review ./src/Vault.sol \
  --base-url "https://openrouter.ai/api/v1" \
  --model "anthropic/claude-sonnet-5.5"

See the request without sending it. --dry-run needs no key and prints the file labels, sizes, line counts, SHA-256 hashes, endpoint and model:

bounty-kit ai-review ./src/Vault.sol --dry-run

What happens before and during the request:

• The sanitizer runs on every file, every filename and the prompt. A match stops the request. --allow-sensitive overrides that for material you have read and mean to send.

• Binary and non-UTF-8 input is rejected. Absolute paths are stripped from the labels. Each file is read once, and that snapshot is what gets hashed and sent.

• Up to 50 files, 120 KB per file, 240 KB in total.

• Files go out with line numbers, so the review cites input-1/Vault.sol:42.

• HTTPS is required for every host except loopback. Redirects are refused. Nothing is retried.

• Output is capped at 16,000 tokens (--max-output-tokens). If the model stops at the cap, the review prints and a warning on stderr says it was cut short.

• A failed request names the cause: rejected key, unknown model, rate limit or timeout. The provider's own message is printed on one line after it passes the sanitizer.

Settings can also come from BOUNTY_KIT_AI_BASE_URL and BOUNTY_KIT_AI_MODEL.

Acceptance history

bounty-kit pattern-stats
bounty-kit pattern-stats rounding access-control
bounty-kit pattern-stats oracle-manipulation --json

Twelve pattern tags with accepted and rejected counts from 1,032 findings across 10 public Sherlock contests. Reentrancy: 40 of 51 accepted. Oracle manipulation: 47 of 131. The data is the CC0 snapshot from holistis/bug-bounty-intelligence-mcp, bundled for offline use. Tags are keyword-matched, and one finding can carry several.

Sanitize

bounty-kit sanitize .
bounty-kit sanitize ./selected-files --json-report
bounty-kit sanitize ./selected-files --strict

Catches API keys, private keys, wallet keys, tokens, credentials in URLs, private Immunefi, Cantina and Sherlock report links, browser-profile paths, raw IP addresses, email addresses and credential files. Every text file is read, whatever its suffix. Generated directories are skipped and listed. --strict fails on any exclusion and ignores inline allow comments. Run it before publishing a repository, a gist or a PoC.

Example session

bounty-kit agent-pack ./agent-pack.md --target "Vault" --program "Immunefi"
bounty-kit init-ledger ./ledger.md --target "Vault" --program "Immunefi"

slither . --json slither.json
bounty-kit slither-focus slither.json --markdown > slither-focus.md

bounty-kit prior-art ./finding-summary.md \
  --target "Vault" \
  --repo "https://github.com/example/protocol"

bounty-kit sanitize .

Prompt library

prompts/ holds one prompt per review pass. Run each in a fresh session, or pass it to bounty-kit ai-review --prompt.

scripts/install-web3-security-references.sh clones nine public web3 security reference collections for an agent to search offline.

Help and feedback

• A question: Q&A

• An idea: Ideas, where others can vote on it

• A bug: open a bug report

• Your account or a payment: by email, see bountyoperator.com/support

• A vulnerability in Bounty Operator: the security policy

The forum and the tracker are public. Keep unreported findings, report drafts and keys out of them.

Docs

• Workflow

• Agent pack

• AI agent flow

• Recommended tools

• MCP server

• Web app: architecture, local development, tests, deploy

• Public results

• Changelog

• Contributing and security policy

License

MIT, for everything in this repository. The method behind the hosted profiles, the gauntlet and the panel is not in the repository and is not licensed. The licence does not grant the name Bounty Operator or its mark.