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security-bounty-hunter

Hunt for exploitable, bounty-worthy security issues in repositories. Focuses on remotely reachable vulnerabilities that qualify for real reports instead of noisy local-only findings. Use when hunting reportable, remotely reachable vulnerabilities in a repository.

Qu'est-ce que security-bounty-hunter ?

security-bounty-hunter is a Claude Code agent skill that hunt for exploitable, bounty-worthy security issues in repositories. Focuses on remotely reachable vulnerabilities that qualify for real reports instead of noisy local-only findings. Use when hunting reportable, remotely reachable vulnerabilities in a repository.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/security-bounty-hunter

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Documentation

Security Bounty Hunter

Use this when the goal is practical vulnerability discovery for responsible disclosure or bounty submission, not a broad best-practices review.

When to Use

  • Scanning a repository for exploitable vulnerabilities
  • Preparing a Huntr, HackerOne, or similar bounty submission
  • Triage where the question is "does this actually pay?" rather than "is this theoretically unsafe?"

How It Works

Bias toward remotely reachable, user-controlled attack paths and throw away patterns that platforms routinely reject as informative or out of scope.

In-Scope Patterns

These are the kinds of issues that consistently matter:

PatternCWETypical impact
SSRF through user-controlled URLsCWE-918internal network access, cloud metadata theft
Auth bypass in middleware or API guardsCWE-287unauthorized account or data access
Remote deserialization or upload-to-RCE pathsCWE-502code execution
SQL injection in reachable endpointsCWE-89data exfiltration, auth bypass, data destruction
Command injection in request handlersCWE-78code execution
Path traversal in file-serving pathsCWE-22arbitrary file read or write
Auto-triggered XSSCWE-79session theft, admin compromise

Skip These

These are usually low-signal or out of bounty scope unless the program says otherwise:

  • Local-only pickle.loads, torch.load, or equivalent with no remote path
  • eval() or exec() in CLI-only tooling
  • shell=True on fully hardcoded commands
  • Missing security headers by themselves
  • Generic rate-limiting complaints without exploit impact
  • Self-XSS requiring the victim to paste code manually
  • CI/CD injection that is not part of the target program scope
  • Demo, example, or test-only code

Workflow

  1. Check scope first: program rules, SECURITY.md, disclosure channel, and exclusions.
  2. Find real entrypoints: HTTP handlers, uploads, background jobs, webhooks, parsers, and integration endpoints.
  3. Run static tooling where it helps, but treat it as triage input only.
  4. Read the real code path end to end.
  5. Prove user control reaches a meaningful sink.
  6. Confirm exploitability and impact with the smallest safe PoC possible.
  7. Check for duplicates before drafting a report.

Example Triage Loop

semgrep --config=auto --severity=ERROR --severity=WARNING --json

Then manually filter:

  • drop tests, demos, fixtures, vendored code
  • drop local-only or non-reachable paths
  • keep only findings with a clear network or user-controlled route

Report Structure

## Description
[What the vulnerability is and why it matters]

## Vulnerable Code
[File path, line range, and a small snippet]

## Proof of Concept
[Minimal working request or script]

## Impact
[What the attacker can achieve]

## Affected Version
[Version, commit, or deployment target tested]

Quality Gate

Before submitting:

  • The code path is reachable from a real user or network boundary
  • The input is genuinely user-controlled
  • The sink is meaningful and exploitable
  • The PoC works
  • The issue is not already covered by an advisory, CVE, or open ticket
  • The target is actually in scope for the bounty program

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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