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Recon

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PentesterFlow
recon

External recon playbook for a web target — subdomain enumeration, live-host probing, tech fingerprinting, and a first pass at content discovery. Use when the user gives you a root domain or apex and wants attack surface mapping.

Overview

PublisherPentesterFlow
Repositoryagent
Skill namerecon
Stars
1.4K
Forks
248
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Recon 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/PentesterFlow/agent.git /tmp/agent
mkdir -p .claude/skills
cp -r /tmp/agent/skills/recon .claude/skills/recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon 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 Recon 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 Recon 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.

Recon playbook

You have been asked to map the attack surface of a domain the user is authorized to test. Stay surgical — do not scan IP ranges or third-party assets.

Default to curl and the built-in http tool. Do not pull in specialized scanners (subfinder, httpx, ffuf, gobuster, etc.) unless the user explicitly asks for them.

Execution rule: substitute the real apex/host into commands before running them. Never write literal placeholders such as <APEX>, <HOST>, or <subdomains> to files. If the apex is unclear, ask once before running commands.

1. Confirm scope

Before running anything, restate the apex domain and ask the user to confirm it is in scope (only ask if scope was not already explicit in the conversation). Note any explicit out-of-scope subdomains or paths.

2. Passive subdomain enumeration with curl

Pull from public CT logs — no extra tooling required. Note: crt.sh is flaky and frequently answers with a 502/HTML page or an empty body instead of JSON. Piping that straight into jq is what throws jq: parse error: Invalid numeric literal. Validate the body is JSON before parsing, and retry with backoff:

# Robust crt.sh pull — quiet retries, parse only valid JSON.
APEX="example.com" # replace with the scoped apex before running
mkdir -p "recon/$APEX"
: > subs.txt
for attempt in 1 2 3; do
  resp=$(curl -fsS --max-time 30 -H 'Accept: application/json' \
    "https://crt.sh/?q=%25.$APEX&output=json" 2>/dev/null || true)
  if printf '%s' "$resp" | jq -e 'type == "array"' >/dev/null 2>&1; then
    printf '%s' "$resp" \
      | jq -r '.[].name_value' \
      | sed 's/^\*\.//' \
      | tr 'A-Z' 'a-z' | tr -d '\r' \
      | sort -u > subs.txt
    break
  fi
  sleep 3   # crt.sh is rate-limited / returns 502 under load
done
[ -s subs.txt ] || printf 'warning: crt.sh unavailable or returned non-JSON; try OTX or another source\n' >&2

name_value is newline-separated and may include wildcard (*.) entries; the sed/sort -u above normalizes and dedupes them. If /target is pinned, derive the real apex from that target before running.

For a second source, layer on AlienVault OTX (also guard the JSON):

APEX="example.com" # replace with the scoped apex before running
otx=$(curl -fsS --max-time 30 "https://otx.alienvault.com/api/v1/indicators/domain/$APEX/passive_dns" 2>/dev/null)
printf '%s' "$otx" | jq -e . >/dev/null 2>&1 \
  && printf '%s' "$otx" | jq -r '.passive_dns[].hostname' | sort -u >> subs.txt
sort -u -o subs.txt subs.txt

Save the deduped list with file_write to recon/$APEX/subs.txt.

Only reach for subfinder / amass / assetfinder if the user names them or the apex is large enough that crt.sh paging starts to drop results.

3. Liveness + tech fingerprinting with curl

For each candidate, send a single GET and capture status, title, and key headers. Tight bash loop:

while read h; do
  curl -ksS -o /tmp/body -w "%{http_code}\t%{url_effective}\t%header{server}\t%header{x-powered-by}\n" \
    --max-time 8 "https://$h/" 2>/dev/null \
    | awk -F'\t' -v host="$h" '{title=""; getline title < "/tmp/body"; sub(/.*<title>/,"",title); sub(/<\/title>.*/,"",title); print $0"\t"title}'
done < subs.txt > httpx.txt

If you need more than that (favicon hashing, full tech fingerprinting on hundreds of hosts), say so and ask the user whether to install/run httpx.

4. Content discovery with curl + a wordlist

For 2-3 hosts that look custom (admin panels, staging, dashboards), do a focused wordlist sweep with curl:

HOST="app.example.com" # replace with an interesting live host before running
WORDLIST=/usr/share/seclists/Discovery/Web-Content/raft-small-words.txt
while read w; do
  code=$(curl -ksS -o /dev/null -w "%{http_code}" --max-time 5 "https://$HOST/$w")
  case "$code" in 200|204|301|302|401|403) echo "$code /$w";; esac
done < "$WORDLIST" | tee "ffuf-$HOST.txt"

Use -w "%{http_code} %{size_download}\n" if you also want to filter by body size. Pick a small wordlist first — escalate to medium only if the small one produces signal.

Only use ffuf or gobuster if the user explicitly asks for them.

5. Summarize

Write a recon/$APEX/summary.md with:

  • Counts: total subdomains, live hosts, by tech stack
  • Top 10 interesting hosts (with one-line reasons)
  • Candidate next steps (auth flows to inspect, admin endpoints, exposed configs, JS files worth diffing)

Frequently asked questions

What does the Recon AI skill do?

External recon playbook for a web target — subdomain enumeration, live-host probing, tech fingerprinting, and a first pass at content discovery. Use when the user gives you a root domain or apex and wants attack surface mapping.

Why use Recon on TypingMind?

Because you install it once and use it with any model. Recon 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 Recon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PentesterFlow/agent/tree/main/skills/recon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recon?

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 Recon?

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

Is the Recon AI skill free?

Yes. It is published on GitHub by PentesterFlow under the Apache-2.0 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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