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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.

What is 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.

Works with~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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