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Sector Recon Methodology

CommunityPopular
uphiago
sector-recon-methodology

Pick sectors, compile targets, batch recon for campaigns.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namesector-recon-methodology
Stars
1.3K
Forks
213
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by uphiago on GitHub. Read the source before you install it.

Installation

Install the Sector Recon Methodology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/meta/sector-recon-methodology .claude/skills/sector-recon-methodology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sector Recon Methodology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sector Recon Methodology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sector Recon Methodology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sector Recon Methodology Skill

Methodology for selecting non-regulated industry sectors with the highest WordPress vulnerability rates, compiling company domain lists, and running batch reconnaissance within authorized engagements. Distilled from surveying 600+ US company domains across 28 sectors under bug bounty and authorized testing programs.

When to Use

  • Planning a new recon campaign and need to choose sectors.
  • Expanding from tested sectors into new ones.
  • Building target lists from sector keywords via crt.sh.
  • After recon-playbook Phase 0 — this skill provides the sector intelligence.
  • Comparing your findings against baseline vulnerability rates per sector.

Prerequisites

  • curl, jq, httpx, and subfinder.
  • Understanding of the US regulatory landscape (HIPAA, GLBA, PCI-DSS) — regulated sectors have near-zero vulnerability rates.
  • An execution environment with explicit concurrency and rate limits.

How to Run

bash
# Generate targets for a sector
SECTOR="landscaping"
curl --max-time 30 --connect-timeout 10 -sk "https://crt.sh/?q=%25.${SECTOR}%25&output=json" | jq -r '.[].name_value' | \
  sed 's/\*\.//g' | sed 's/^www\.//' | sort -u > ${SECTOR}_targets.txt

Script: scripts/parallel_sector_probe.py — OPSEC-controlled batch probe with random delays, generates per-domain findings files. Run python3 scripts/parallel_sector_probe.py targets.txt output_dir/ for a full WP/CORS/XMLRPC/leak scan.

Quick Reference

TierSectorsTypical WP RateBest PatternNotes
1Law, Landscaping, Pools, Pest, Roofing, Dental, Gyms, Real Estate, HVAC, Property, Auto Repair, Photography30-50%CORS + WP usersMinimal WAF, GoDaddy/Bluehost
2Cleaning, Moving, Accounting, Septic, Window, Car Wash, Bakery, Locksmith, Solar, Chimney, Fire, Pet Grooming20-40%Source leaks, CORSMixed hosting, some WAF
3Car Dealers, Insurance, Travel, Banks, Healthcare0-5%N/AEnterprise WAF, regulated, no WP

Sector Vulnerability Rankings

Tier 1 — High Yield (15-25% vulnerability rate)

SectorVuln RateTop PatternWordPress RateWAF ProtectionBest Targets
Law Firms25%P-06 (CORS auth)~30%MinimalSolo/small firm, GoDaddy-hosted
Landscaping20%WP-01 (user enum)~50%MinimalLocal SMB, franchise model
Pool Services20%WP-01 (user enum)~45%MinimalSummer-seasonal businesses
Pest Control20%P-02 (CORS)~40%MinimalFranchise-heavy
Roofing15%WP-01, P-17~45%MinimalLocal contractors
Dental Clinics15%WP-01 (user enum)~35%SomeSingle-dentist practices
Gyms/Fitness15%WP-01 (user enum)~40%SomeCrossFit, yoga, martial arts
Real Estate15%P-02 (CORS)~40%SomeIndependent brokerages
HVAC/Plumbing14%WP-01, P-02~35%SomeFranchise-heavy, staging common
Property Management15%P-02 (CORS)~30%SomePII-heavy sector
Photography10%WP-01 (user enum)~50%SomePortfolio sites, often WP
Funeral Homes10%WP-01 (user enum)~33%MinimalWordPress + user enum found on 2 targets (funeral.example.com, memorial.example.com)
Senior Living17%P-02 (CORS) + XMLRPC~33%Cloudflaresenior-living.example.com (80 XMLRPC methods + CORS + multicall), senior-living2.example.com (CORS) — 2/12 tested = CRITICAL findings

Tier 2 — Medium Yield (5-14% vulnerability rate)

SectorVuln RateNotes
Cleaning Services13%Carpet, window, mold remediation
Moving Companies6%Fewer WP, more SaaS platforms
Accounting/CPA5%Some regulated, some not — financial data at risk
Septic Services25% source leaksMassive source leak rate (.env, .git, wp-config)
Window Cleaning25% CORSSmall operations, DIY WP
Car Washes20% source leaksDockerfile, swagger, actuator endpoints common
Bakeries18% CORS wildcard28 leaked files on one target
Locksmiths20% WP users + XMLRPC38 subdomains on one target
Solar InstallersMinimalMajor brands use enterprise platforms
Chimney Sweeps~10% CORS + XMLRPCSmall family businesses, WP common
Fire Restoration~10%Franchise model (Servpro, Belfor)
Pet Grooming20% WP usersDogtopia, Camp Bow Wow — WP detected

Critical Finding Example (from 248-target new sector expansion)

pool-company.example.com — Critical (Score 9):

  • 3 users exposed via REST API (wpadmin ID=1 — default admin account)
  • CORS credential reflection confirmed
  • XMLRPC system.multicall active
  • Attack chain: CORS phishing → exfiltrate user data → brute force wpadmin via XMLRPC multicall → full site takeover

Tier 3 — Zero/Low Yield (0-3% vulnerability rate)

SectorVuln RateWhy
Car Dealerships0%Enterprise CDK/Dealertrack platforms, not WordPress
Travel Agencies0%SaaS (Sabre, Amadeus), not self-hosted
Banks/Credit Unions0%GLBA regulated, mandatory security
Major Healthcare0%HIPAA regulated, HITRUST certified
Home Services0%Angi/Thumbtack platforms, not self-hosted

Skip these sectors unless you have specific intelligence suggesting WordPress usage.

Procedure

Step 1 — Sector Selection

bash
OUTDIR="$OUTDIR/sectors"
mkdir -p "$OUTDIR"

echo "[*] Sector vulnerability potential assessment:"
echo ""

# Run quick sector probe: crt.sh → httpx → WP detection → user count
probe_sector() {
  local sector="$1"
  local file="$OUTDIR/${sector}_probe.txt"

  echo "[*] Probing sector: $sector"

  # Get domains from crt.sh
  curl -sk --max-time 20 --connect-timeout 10 "https://crt.sh/?q=%25.${sector}%25&output=json" 2>/dev/null | \
    jq -r '.[].name_value' 2>/dev/null | sed 's/\*\.//g' | sed 's/^www\.//' | \
    grep -E '^[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$' | sort -u | head -50 > "$OUTDIR/${sector}_domains.txt"

  # Option C: Manual known-company list (when crt.sh/subfinder time out)
  if [[ ! -s "$OUTDIR/${sector}_domains.txt" ]]; then
    echo "  crt.sh empty — using manual known-company list"
    # Generate known US companies in this sector
    # Example: for "vet" or "pet_care" sector, known chains include:
    cat > "$OUTDIR/${sector}_domains.txt" << 'EOF'
[VET_CLINIC]
[VET_CHAIN]
[PET_RETAILER]
[VET_CORPORATE]
[VET_HOSPITAL]
[VET_SPECIALIST]
[ANIMAL_CLINIC]
[VET_CLINIC]
[TRADE_PUBLICATION]
EOF
  fi

  local total=$(wc -l < "$OUTDIR/${sector}_domains.txt")
  echo "  Domains: $total"

  # Probe with httpx
  httpx -silent -l "$OUTDIR/${sector}_domains.txt" -threads 30 -status-code -tech-detect \
    -o "$OUTDIR/${sector}_alive.txt" 2>/dev/null

  local alive=$(wc -l < "$OUTDIR/${sector}_alive.txt")
  echo "  Live: $alive"

  # WordPress count
  local wp=$(grep -ci 'wordpress' "$OUTDIR/${sector}_alive.txt" 2>/dev/null || echo 0)
  echo "  WordPress: $wp"

  # Quick user enumeration on WP targets
  local users=0
  grep -i 'wordpress' "$OUTDIR/${sector}_alive.txt" 2>/dev/null | awk '{print $1}' | head -10 | while read -r url; do
    ucount=$(curl -sk --max-time 5 --connect-timeout 5 "$url/wp-json/wp/v2/users" 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(len(d) if isinstance(d,list) else 0)" 2>/dev/null || echo 0)
    [[ "$ucount" -gt 0 ]] && echo "    $url: $ucount users" >> "$OUTDIR/${sector}_users.txt"
  done

  local user_targets=$(wc -l < "$OUTDIR/${sector}_users.txt" 2>/dev/null || echo 0)
  echo "  User leaks: $user_targets"

  # Score the sector
  echo "$sector | domains=$total | alive=$alive | wp=$wp | user_leaks=$user_targets" >> "$OUTDIR/sector_scores.txt"
}

# Probe top candidate sectors
for sector in "landscaping" "roofing" "hvac" "pools" "plumbing" "lawn-care" \
  "pest-control" "law-firm" "dentist" "gym" "real-estate" "auto-repair" \
  "moving-company" "photography" "cleaning-service" "church"; do
  probe_sector "$sector"
  sleep 3  # Rate limit
done

echo ""
echo "[*] Sector scores:"
sort -t'|' -k5 -rn "$OUTDIR/sector_scores.txt" 2>/dev/null | head -15

Step 2 — Target List Compilation

bash
OUTDIR="$OUTDIR/sectors"

# For selected high-yield sectors, compile full target lists
SELECTED_SECTORS=("landscaping" "roofing" "pools" "plumbing" "pest-control" "law-firm")

echo "[*] Compiling target lists for selected sectors..."

> "$OUTDIR/all_targets.txt"

for sector in "${SELECTED_SECTORS[@]}"; do
  echo "  Sector: $sector"

  # crt.sh wildcard search
  curl -sk --max-time 20 --connect-timeout 10 "https://crt.sh/?q=%25.${sector}%25&output=json" 2>/dev/null | \
    jq -r '.[].name_value' 2>/dev/null | sed 's/\*\.//g' | sed 's/^www\.//' | \
    grep -E '^[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$' | sort -u > "$OUTDIR/${sector}_all.txt"

  # Also try subfinder
  subfinder -d "${sector}.com" -silent 2>/dev/null >> "$OUTDIR/${sector}_all.txt"

  # Add to master list with sector label
  while read -r domain; do
    echo "$domain|$sector" >> "$OUTDIR/all_targets.txt"
  done < "$OUTDIR/${sector}_all.txt"

  sleep 2
done

# Clean: remove CDN domains, parking pages, known false positives
echo "[*] Cleaning target list..."

# Remove common false positives
grep -viE 'cloudflare|akamai|fastly|awsdns|googleusercontent|azurewebsites' "$OUTDIR/all_targets.txt" | \
  sort -u > "$OUTDIR/all_targets_clean.txt"

total=$(wc -l < "$OUTDIR/all_targets_clean.txt")
echo "[+] $total clean targets across ${#SELECTED_SECTORS[@]} sectors"

Step 3 — Sector Baseline Statistics

After batch recon (using recon-playbook), compute per-sector stats:

bash
OUTDIR="$OUTDIR/sectors"
FINDINGS_DIR="$OUTDIR/playbook/phase2_findings"

echo "[*] Computing sector statistics..."

# Extract sector from target list
declare -A SECTOR_WP
declare -A SECTOR_CORS
declare -A SECTOR_XMLRPC
declare -A SECTOR_LEAKS
declare -A SECTOR_TOTAL

# Read target list
while IFS='|' read -r domain sector; do
  [[ -z "$sector" ]] && continue
  ((SECTOR_TOTAL[$sector]++))

  findings_file="$FINDINGS_DIR/${domain}_p2.md"
  if [[ -f "$findings_file" ]]; then
    grep -q "WordPress:" "$findings_file" && ((SECTOR_WP[$sector]++))
    grep -q "CORS:" "$findings_file" && ((SECTOR_CORS[$sector]++))
    grep -q "XMLRPC:" "$findings_file" && ((SECTOR_XMLRPC[$sector]++))
    grep -q "Source leak:" "$findings_file" && ((SECTOR_LEAKS[$sector]++))
  fi
done < "$OUTDIR/all_targets_clean.txt"

# Print sector report
echo ""
echo "## Sector Vulnerability Report"
echo ""
echo "| Sector | Targets | WP | CORS | XMLRPC | Leaks | Rate |"
echo "|--------|---------|-----|------|--------|-------|------|"

for sector in "${!SECTOR_TOTAL[@]}"; do
  total=${SECTOR_TOTAL[$sector]}
  wp=${SECTOR_WP[$sector]:-0}
  cors=${SECTOR_CORS[$sector]:-0}
  xmlrpc=${SECTOR_XMLRPC[$sector]:-0}
  leaks=${SECTOR_LEAKS[$sector]:-0}
  vulns=$((cors + xmlrpc + leaks))
  rate=$(python3 -c "print(f'{$vulns/$total*100:.1f}%')" 2>/dev/null || echo "0%")

  echo "| $sector | $total | $wp | $cors | $xmlrpc | $leaks | $rate |"
done

Step 4 — Sector Recon Skill Template

When creating a new sector-specific recon skill, use this template:

markdown
---
name: recon-SECTORNAME
description: Reconnaissance workflow for SECTOR NAME companies.
version: 1.1.0
revision_date: 2026-07-25
license: MIT
platforms: [linux]
tags: [recon, sector, SECTORNAME]
category: recon
---

# SECTOR NAME Recon Skill

[2-3 sentence description of sector-specific attack surface]

## When to Use

- Targeting SECTOR NAME companies for recon.
- [Sector-specific trigger]

## Quick Reference

| Attack Surface | Expected Prevalence | Top Pattern |
|----------------|---------------------|-------------|
| WordPress | XX% | WP-01 |
| CORS | XX% | V1 |
| XMLRPC | XX% | WP-05 |
| Source leaks | XX% | P-17 |

## Attack Surface Signals

- [Sector-specific CMS/platform signals]
- [Common third-party integrations]
- [Typical infrastructure patterns]
- [PII/regulated data vectors]

## Sector-Specific Bypasses

- [WAF/CDN patterns for this sector]
- [Rate limiting characteristics]
- [Common security gaps]

## Real Examples

- [Target name]: [finding] (severity)
- [Target name]: [finding] (severity)

## Related Skills

- wp-mass-recon
- cors-credential-wordpress
- xmlrpc-exploitation
- source-leak-hunt

Pitfalls

  • Sector keyword overlap. pest control may return pest-control.example.com (the SaaS, not pest control companies). Filter by domain patterns typical of SMBs.
  • crt.sh noise from CDN/cloud. Domains like *.cloudfront.net or *.awsdns-*.org appear in sector crt.sh queries. Filter aggressively.
  • crt.sh / subfinder timeouts. Both tools frequently hang or return empty for low-traffic sectors or during high-demand windows. When they fail, fall back to manually compiling known US companies in the sector: use top-ranked national chains, franchise directories, and industry association member lists. Known-company lists are often more productive than sparse API results for long-tail sectors.
  • Sector saturation. After scanning 50+ targets per sector, you'll see the same patterns. Move to new sectors once the baseline is established.
  • Corporate vs. franchise. Some sectors (HVAC, pest control) have both corporate parent domains and individual franchise domains. The franchise domains are softer targets.

Verification

  • Sector vulnerability rates should be based on at least 20 scanned targets.
  • Every sector should have at least one WordPress detection, CORS finding, or source leak to be considered "productive."
  • Zero-yield sectors should be re-verified with a different methodology before being fully written off.
  • Sector reports should include: total targets, alive hosts, WP detected, vulnerabilities found, top patterns, and representative examples.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sector Recon Methodology AI skill do?

Pick sectors, compile targets, batch recon for campaigns.

Why use Sector Recon Methodology on TypingMind?

Because you install it once and use it with any model. Sector Recon Methodology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sector Recon Methodology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/meta/sector-recon-methodology. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sector Recon Methodology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sector Recon Methodology?

As many as you like. As long as a model supports skills, you can use Sector Recon Methodology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sector Recon Methodology AI skill free?

Yes. It is published on GitHub by uphiago under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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