Wiki Recon logo

Wiki Recon

Community
Encod3d-Sec
wiki-recon

External recon and OSINT pipeline - subdomain enum, live host discovery, URL crawl, JS analysis, nuclei scan. Outputs to Attack-surface.md and scope/. Queries wiki before each phase. Use when starting recon on any target.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namewiki-recon
Stars
334
Forks
48
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by Encod3d-Sec on GitHub. Read the source before you install it.

Installation

Install the Wiki 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.
    https://github.com/Encod3d-Sec/TORCH/tree/main/skills/workflow/wiki-recon
  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/Encod3d-Sec/TORCH.git /tmp/TORCH
mkdir -p .claude/skills
cp -r /tmp/TORCH/skills/workflow/wiki-recon .claude/skills/wiki-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wiki 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 Wiki 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 Wiki 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.

Wiki-Recon: External Recon Pipeline

Phase 0: Wiki Query (MANDATORY)

qmd_query "recon subdomain enumeration" via wiki-search MCP -> read matching pages.
qmd_query "OSINT external attack surface" -> apply known techniques.

If no matching page: proceed. Do not block on missing wiki coverage. Dorks to find exposed/vulnerable assets: wiki/cheatsheets/recon-dorks.md; attack paths once in: wiki/cheatsheets/attack-chains.md.

Scope Check

  • Confirm target domain(s) are in scope
  • Read Attack-surface.md - skip hosts already fully documented
  • Read Deadends.md - skip recon paths already exhausted

Recon Pipeline

Tool-first: subfinder/assetfinder for subdomains, httpx for live-host probing, katana/gau for URLs, ffuf for content discovery, nuclei for templated checks. The crt.sh curl below is the one hand request kept (a passive source with no tool wrapper); everywhere else lean on the tool, not a curl loop.

Stage 1: Subdomain Discovery

bash
TARGET="target.com"
RECON_DIR="poc/recon/$TARGET"
mkdir -p $RECON_DIR

# Passive sources
curl -s "https://crt.sh/?q=%.${TARGET}&output=json" \
  | jq -r '.[].name_value' | sed 's/\*\.//g' | sort -u > $RECON_DIR/subs.txt

subfinder -d $TARGET -silent | tee -a $RECON_DIR/subs.txt
assetfinder --subs-only $TARGET | tee -a $RECON_DIR/subs.txt
sort -u $RECON_DIR/subs.txt -o $RECON_DIR/subs.txt

Stage 2: Live Host Discovery

bash
cat $RECON_DIR/subs.txt | dnsx -silent | \
  httpx -silent -status-code -title -tech-detect | tee $RECON_DIR/live.txt

On any TLS host from Stage 2, dump the cert SANs early; a hidden vhost none of the above discovers can be listed only in the Subject Alternative Name, see [[cdn-waf-bypass]].

Stage 3: URL Crawl + Historical

bash
cat $RECON_DIR/live.txt | awk '{print $1}' | \
  katana -d 3 -jc -kf all -silent | tee $RECON_DIR/urls.txt
echo $TARGET | waybackurls | tee -a $RECON_DIR/urls.txt
gau $TARGET --subs | tee -a $RECON_DIR/urls.txt
sort -u $RECON_DIR/urls.txt -o $RECON_DIR/urls.txt

Stage 4: Nuclei Scan

bash
nuclei -l $RECON_DIR/live.txt -t ~/nuclei-templates/ \
  -severity critical,high,medium -o $RECON_DIR/nuclei.txt

Stage 5: Attack Surface Triage

bash
# Content discovery on live hosts: OUR high-signal list first (non-obvious routes the crawl missed)
ffuf -c -u https://HOST/FUZZ -w scripts/wordlists/harness-paths.txt -e .php,.py -mc 200,301,302,401,403 -ac

# High-value URL patterns
cat $RECON_DIR/urls.txt | grep -E "\?.*=" | grep -E "url=|redirect=|src=|dest=|fetch=" > $RECON_DIR/ssrf_candidates.txt
cat $RECON_DIR/urls.txt | grep -E "\?.*=" | grep -E "id=|user_id=|order_id=|doc_id=" > $RECON_DIR/idor_candidates.txt
cat $RECON_DIR/urls.txt | grep -E "graphql|/gql|/graph" > $RECON_DIR/graphql_candidates.txt
cat $RECON_DIR/urls.txt | grep -E "upload|import|parse|convert|preview|render" > $RECON_DIR/upload_candidates.txt

# JS secret scanning
cat $RECON_DIR/urls.txt | grep "\.js$" | \
  xargs -I {} curl -sk {} | grep -E "(api_key|apikey|secret|token|password|credential).*['\"][A-Za-z0-9+/]{20,}" \
  > $RECON_DIR/js_secrets.txt

READ each app .js / inline <script> / button onclick / href END-TO-END, do not stop at the grep. The secret-scan above only surfaces hardcoded keys; the initial attack vector (an AJAX handler POSTing to an undocumented endpoint, a commented route, a hidden param) hides in code the grep filters out. Open every first-party bundle and read it top to bottom, grep only to LOCATE inside a large file, then read the block. A page that looks like a static template is often a dynamic app whose whole endpoint map lives in one JS file.

Run each scan in its own tmux tab on the VM (root, persistent), one tab per target: bash scripts/vm-scan.sh <eng> <target> '<scan>' (multi-web target -> <target>-web-<ip-or-domain>). Capture a live/finished tab with Skill(screenshot) --tmux <eng>:<tab> (use the @NN id or sanitized tab name it prints). Capture standalone tool output (nmap service surface, ffuf/feroxbuster hits, nuclei findings) as terminal-card PNGs via Skill(screenshot) --term for the Attack-surface evidence.

Output to Attack-surface.md

For each discovered live host, add a row to the target's Attack-surface.md:

markdown
| sub.target.com | 1.2.3.4 | [status] | - | [finding or notes] |

Add newly discovered hosts to scope/ IP/domain lists.

Record recon progress in targets/<eng>/Approach.md Phase 1 items.

If nuclei finds CRITICAL or HIGH severity issues: create a FIND-XXX entry immediately.

Distill to wiki (when confirmed): if a novel subdomain takeover or recon-bypass technique is found, stage a GENERIC wiki candidate now (no client host): python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/osint/web-attack-surface.md. Promote later via scripts/wiki-promote.py.

Context tools

  • [[amass]]
  • [[subfinder]]
  • [[dnsx]]
  • [[gau]]
  • [[gowitness]]
  • [[wiki/tools/httpx]]
  • [[katana]]

Frequently asked questions

What does the Wiki Recon AI skill do?

External recon and OSINT pipeline - subdomain enum, live host discovery, URL crawl, JS analysis, nuclei scan. Outputs to Attack-surface.md and scope/. Queries wiki before each phase. Use when starting recon on any target.

Why use Wiki Recon on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Encod3d-Sec/TORCH/tree/main/skills/workflow/wiki-recon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wiki 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 Wiki Recon?

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

Is the Wiki Recon AI skill free?

Yes. It is published on GitHub by Encod3d-Sec 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇