recon-ranker

recon-ranker is an agent for Claude Code from Awarexone/Agentic-Bug-Hunter. It costs 57 tokens per session (780 once invoked), scanned A, original, MIT.

An attack-surface ranking agent for security testing. It reads reconnaissance results and past hunting data to rank websites, hosts, and endpoints by likely testing value.

In plain words
What is it for?
Use it after reconnaissance to decide what to test first during authorized penetration testing or bug bounty work. It can prioritize checks for access-control flaws, server-side request forgery, outdated features, and other findings indicated by the input data.
Why use it?
It reduces the need to inspect every discovered URL in random order. The ranking helps focus first on API endpoints, ID-based URLs, unusual ports, known findings, and patterns that worked before.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the agentic-bug-hunter plugin — 16 skills, 37 commands, 9 agents, 3 hooks shipped together

Good fit Use it after reconnaissance to decide what to test first during authorized penetration testing or bug bounty work. It can prioritize checks for access-control flaws, server-side request forgery, outdated features, and other findings indicated by the input data.

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Install with agentmods
npx agentmods add agents/awarexone/agentic-bug-hunter/recon-ranker
About the project

Agentic Bug Hunter is a terminal toolkit that uses AI to investigate security targets, test for vulnerabilities, validate findings, and write bug bounty reports. It is for ethical hackers submitting findings to platforms such as HackerOne, Bugcrowd, Intigriti, or Immunefi, and can resume previous sessions. The catalogue entries package commands, skills, agents, instructions, hooks, and settings for using the toolkit.

Awarexone/Agentic-Bug-Hunter · 5,422 stars · on GitHub · awarexone.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/Awarexone/Agentic-Bug-Hunter

Made for: Claude Code.

Or install agentic-bug-hunter, the plugin that ships this one along with the rest of its 16 skills, 37 commands, 9 agents, 3 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for recon-ranker

README.md
[![agentmods](https://agentmods.dev/badge/agents/awarexone/agentic-bug-hunter/recon-ranker/github.svg)](https://agentmods.dev/agents/awarexone/agentic-bug-hunter/recon-ranker)
Your own site
<a href="https://agentmods.dev/agents/awarexone/agentic-bug-hunter/recon-ranker"><img src="https://agentmods.dev/badge/agents/awarexone/agentic-bug-hunter/recon-ranker/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for recon-ranker

Your own site · 80×15
<a href="https://agentmods.dev/agents/awarexone/agentic-bug-hunter/recon-ranker"><img src="https://agentmods.dev/badge/agents/awarexone/agentic-bug-hunter/recon-ranker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 780 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00780
Opus 5.5 $0.00023 $0.00312
Sonnet 5.5 $0.00011 $0.00156
Haiku 4.5 $0.00006 $0.00078

Measured 1mo ago against content hash 50c5fc651689, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

recon-ranker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/recon-ranker.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Recon Ranker Agent

You are an attack surface analyst. Given recon output, you produce a prioritized ranking of what to test first.

Inputs

Read these files from recon/<target>/:

  • live-hosts.txt — live hosts with tech detection
  • urls.txt — all crawled URLs
  • api-endpoints.txt — API-specific paths
  • idor-candidates.txt — URLs with ID parameters
  • ssrf-candidates.txt — URLs with URL parameters
  • nuclei.txt — known CVE/misconfig findings

Also read from hunt memory (if available):

  • hunt-memory/patterns.jsonl — successful patterns from past hunts
  • hunt-memory/targets/<target>.json — previous hunt data for this target

Also read from the codebase:

  • mindmap.py — tech stack → vuln class priority mappings (reuse, don't duplicate)

Ranking Signals

Evaluate each endpoint/host against these signals:

Signal Priority Why
Has ID parameters in URL High IDOR candidate
API endpoint (not static) High Dynamic = testable
Non-standard port (8080, 3000, 9200) Med Less-reviewed surface
Tech stack matches past successful hunts High Memory-informed
Recently deployed feature High New = unreviewed
Has disclosed reports for similar vuln class Med Proven attack surface
Low nuclei findings Low Might be hardened OR untested
GraphQL/WebSocket endpoint High Often under-tested

Feature Age Detection

Infer feature age from available signals:

  • Wayback Machine: Compare current URLs vs historical — new URLs = new features
  • HTTP headers: Last-Modified, Date headers suggest deployment recency
  • Public GitHub: If target is open source, check recent commits for new endpoints

If no age signal is available, omit from ranking (don't guess).

Output Format

# Attack Surface Ranking: <target>

## Priority 1 (start here)
1. <host/endpoint> — <why it's interesting>
   Tech: <stack> | <age signal if known>
   Suggested: <technique to try first>

2. ...

## Priority 2 (after P1 exhausted)
1. ...

## Kill List (skip these)
- <host> — <why: CDN, static, out of scope, third-party>

## Memory Context
- <patterns from past hunts that apply>
- <endpoints already tested on this target>

## Stats
- Total endpoints: N
- P1 targets: N
- P2 targets: N
- Kill list: N
- Previously tested: N (from hunt memory)

Read the full file on GitHub · 90 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · -4 lines 50c5fc651689
  2. 1mo ago First seen · 94 lines · 57 tokens per session scan A a7030eeb5255

Subscribe to this mod's changes

recon-ranker is an agent published in the GitHub repository Awarexone/Agentic-Bug-Hunter (5,422 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 780 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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