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Recon Playbook

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uphiago
recon-playbook

Use when starting or restructuring an authorized external web and API assessment.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namerecon-playbook
Stars
1.3K
Forks
213
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 uphiago on GitHub. Read the source before you install it.

Installation

Install the Recon Playbook 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/recon-playbook .claude/skills/recon-playbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon Playbook 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 Playbook 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 Playbook 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.

External Web Recon Playbook

Use this playbook to turn an authorized root domain or asset list into a prioritized map of web applications, APIs, authentication boundaries, and testable security hypotheses.

text
scope
  -> assets
  -> DNS and services
  -> routes and client code
  -> APIs and identities
  -> hypotheses
  -> focused validation
  -> evidence and reporting

When to Use

  • Beginning an external web, API, or bug bounty assessment.
  • Recon output exists but lacks normalization, provenance, or prioritization.
  • The target spans multiple applications, subdomains, or identity boundaries.
  • A broad scan needs to be converted into focused manual validation.

Do not use this playbook to justify activity outside the agreed scope or to run every available tool against every asset.

Prerequisites

  • Explicit authorization, target boundaries, exclusions, rate limits, and stop conditions.
  • curl, jq, subfinder, dnsx, httpx, and katana.
  • nmap only when IP or port discovery is in scope.
  • Approved test identities for authorization and session testing.
  • A writable evidence directory.

How to Run

bash
export TARGET="example.test"
export OUTPUT_DIR="${OUTPUT_DIR:-./output/$TARGET}"

mkdir -p \
  "$OUTPUT_DIR/assets" \
  "$OUTPUT_DIR/http" \
  "$OUTPUT_DIR/urls" \
  "$OUTPUT_DIR/evidence"

Run each phase only after reviewing the preceding output. Keep raw source files so every hostname, URL, and hypothesis has provenance.

Procedure

1. Record Scope

Keep a short operator-readable scope record beside the output:

text
allowed: *.example.test
excluded: status.example.test
identities: anonymous, test-user-a, test-user-b
request rate: 2 requests/second/host
state changes: synthetic records only

The scope must answer what may be tested, which identities may be used, how much traffic is acceptable, and whether a state-changing test is allowed.

2. Discover and Normalize Assets

bash
subfinder -d "$TARGET" -silent \
  > "$OUTPUT_DIR/assets/subfinder.txt"

curl -sS --max-time 30 \
  "https://crt.sh/?q=%25.${TARGET}&output=json" \
  | jq -r '.[].name_value' \
  | sed 's/^\*\.//' \
  > "$OUTPUT_DIR/assets/crtsh.txt"

cat "$OUTPUT_DIR/assets/subfinder.txt" \
    "$OUTPUT_DIR/assets/crtsh.txt" \
  | tr '[:upper:]' '[:lower:]' \
  | grep -E '^[a-z0-9.-]+\.[a-z]{2,}$' \
  | sort -u \
  > "$OUTPUT_DIR/assets/hostnames.txt"

Review wildcard results, certificate SANs, third-party CNAMEs, and excluded assets before active probing.

3. Resolve and Identify HTTP Services

bash
dnsx \
  -l "$OUTPUT_DIR/assets/hostnames.txt" \
  -silent -a -aaaa -cname -json \
  -o "$OUTPUT_DIR/assets/dns.jsonl"

httpx \
  -l "$OUTPUT_DIR/assets/hostnames.txt" \
  -threads 10 \
  -rate-limit 2 \
  -status-code -title -tech-detect -server -ip -cname \
  -json \
  -o "$OUTPUT_DIR/http/services.jsonl"

jq -r '.url // empty' "$OUTPUT_DIR/http/services.jsonl" \
  | sort -u \
  > "$OUTPUT_DIR/http/live-urls.txt"

Treat technology labels and banners as routing signals. Confirm important components from more than one source before associating a vulnerability.

4. Map Routes, Parameters, and Client Code

bash
katana \
  -list "$OUTPUT_DIR/http/live-urls.txt" \
  -silent -jc -kf all \
  -c 2 -p 2 -rl 2 \
  -o "$OUTPUT_DIR/urls/katana.txt"

grep -Ei '/api/|/graphql|swagger|openapi|/rest/' \
  "$OUTPUT_DIR/urls/katana.txt" \
  > "$OUTPUT_DIR/urls/api-candidates.txt"

grep -Ei 'login|logout|register|reset|oauth|saml|callback|session|token' \
  "$OUTPUT_DIR/urls/katana.txt" \
  > "$OUTPUT_DIR/urls/auth-candidates.txt"

grep -Ei '\.js([?#].*)?$' "$OUTPUT_DIR/urls/katana.txt" \
  > "$OUTPUT_DIR/urls/javascript.txt"

Map current routes before adding archive sources. Historical URLs are useful for discovery, but they do not prove that an endpoint remains reachable.

5. Map APIs and Identity Boundaries

For each application, record:

  • API base URLs, descriptions, versions, operations, and object identifiers;
  • anonymous, user, tenant, manager, and administrator boundaries;
  • login, logout, refresh, password reset, MFA, OAuth, and SAML flows;
  • cookie, token, CORS, CSRF, and WebSocket behavior;
  • state-changing methods and the synthetic objects approved for testing.

Authorization testing needs at least two approved identities when the claim depends on cross-user or cross-tenant access.

6. Select Focused Skills

Use attack-patterns-reference to classify observations, then open only the skills supported by current evidence. Common routes include:

ObservationFollow-up skill
JavaScript bundles or source mapsjs-secrets-extraction, source-leak-hunt
REST objects and identifiershunt-api-misconfig, hunt-idor
GraphQL endpointhunt-graphql
OAuth or SAML flowhunt-oauth, hunt-saml
WordPress surfacehunt-wordpress, wordpress-plugin-hunt
Public service portport-service-discovery
Candidate multi-step pathcross-attack-chains

7. Validate One Hypothesis at a Time

Write the hypothesis before the probe:

text
Expected: user A cannot read user B's synthetic object.
Test: repeat the same object request with both approved sessions.
Positive evidence: user A receives user B's object and its non-public fields.
Negative control: an unknown object returns the documented not-found response.
Stop condition: any access to non-synthetic or out-of-scope data.

Use triage-validation before reporting. A status code, scanner label, version string, or permissive-looking header is not sufficient by itself.

8. Preserve Evidence

For every material result, retain:

  • target, identity, UTC timestamp, and scope context;
  • sanitized request and response;
  • expected and observed behavior;
  • negative control;
  • side effects, cleanup, and testing limits;
  • tool and relevant version.

Use evidence-hygiene while testing and report-writing after validation.

Pitfalls

  • Catch-all applications return 200 for nonexistent sensitive paths.
  • CDN IPs and headers do not identify an origin or application version alone.
  • Archived endpoints may be dead, redirected, or reassigned.
  • Public client identifiers are not automatically secrets.
  • Broad crawling can cross into third-party or excluded domains.
  • Pagination can turn a bounded proof into unnecessary data collection.
  • State-changing validation requires explicit authorization immediately before the command and must use synthetic records.

Verification

  • Every active asset traces back to an allowed scope entry and discovery source.
  • Live services have normalized URLs and DNS context.
  • Application maps include routes, APIs, authentication, and identity roles.
  • Every promoted finding includes semantic evidence and a negative control.
  • Candidate attack paths label unverified steps as inferred or not tested.
  • Evidence is sanitized, reproducible, and stored beneath OUTPUT_DIR.

Frequently asked questions

What does the Recon Playbook AI skill do?

Use when starting or restructuring an authorized external web and API assessment.

Why use Recon Playbook on TypingMind?

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

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

Which AI models can use Recon Playbook?

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 Playbook?

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

Is the Recon Playbook 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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