Agent skill

Zz Code Recon

by sendaifun in sendaifun/skills

Deep architectural context building for security audits. An agent skill from sendaifun/skills.

Apache-2.0Auto-check: notesSecurity

Install Zz Code Recon

skills CLI
$ npx skills add sendaifun/skills --skill zz-code-recon -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sendaifun/skills zz-code-recon --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sendaifun/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/zz-code-recon .claude/skills/zz-code-recon && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zz-code-recon
GitHub stars
130
Token cost
~3.3k tokens
SKILL.md length
287 words
Files
6
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deep architectural context building for security audits. An agent skill from sendaifun/skills.

  • Works in 5 steps: Overview Reconnaissance → Architecture Mapping → Module Deep Dive → …
  • Conducting security reviews
  • SKILL.md covers Overview, The Recon Pyramid, Phase 1: Overview Reconnaissance and Phase 2: Architecture Mapping, plus 6 more sections
  • Calls jq

What it does

Zz Code Recon is an agent skill from sendaifun/skills. Deep architectural context building for security audits. Use when conducting security reviews, building codebase understanding, mapping trust boundaries, or preparing for vulnerability analysis. Inspired by Trail of Bits methodology.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files (for example `docs/advanced-techniques.md`, `examples/web-app-recon/fastapi-example.md` and `resources/question-bank.md`).

It sits in Security, covering Security review. The repository describes itself as: a public marketplace of all solana-related skills for agents to learn from! The licence is Apache-2.0.

When your agent uses it

  • Conducting security reviews
  • Building codebase understanding
  • Mapping trust boundaries
  • Preparing for vulnerability analysis

Example prompts

  • “/zz-code-recon”

Requirements

  • Python 3
  • Node.js
  • Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Overview Reconnaissance
  2. Architecture Mapping
  3. Module Deep Dive
  4. Function-Level Analysis
  5. Detail Reconnaissance

What it can do on your machine

Read from SKILL.md and the folder at commit 06b36b8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zz Code Recon loads about 3.3k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 287 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:133
    "config*" -o -name "settings*" -o -name ".env*"

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sendaifun/skills at commit 06b36b8, republished under its Apache-2.0 licence (© sendaifun). 287 words, ~3,268 tokens.

Download SKILL.mdSave it as .claude/skills/zz-code-recon/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
zz-code-recon
description
Deep architectural context building for security audits. Use when conducting security reviews, building codebase understanding, mapping trust boundaries, or preparing for vulnerability analysis. Inspired by Trail of Bits methodology.

CodeRecon - Deep Architectural Context Building

Build comprehensive architectural understanding through ultra-granular code analysis. Designed for security auditors, code reviewers, and developers who need to rapidly understand unfamiliar codebases before diving deep.

Overview

CodeRecon is a systematic approach to codebase reconnaissance that builds layered understanding from high-level architecture down to implementation details. Inspired by Trail of Bits' audit-context-building methodology.

Why CodeRecon?

Before you can find vulnerabilities, you need to understand:

  • How the system is architected
  • Where data flows
  • What the trust boundaries are
  • Where security-critical logic lives

This skill provides a structured methodology for building that context efficiently.

The Recon Pyramid

                    ┌─────────────┐
                    │   DETAILS   │  ← Implementation specifics
                   ─┼─────────────┼─
                  / │  FUNCTIONS  │  ← Key function analysis
                 /  ─┼─────────────┼─
                /   │   MODULES   │  ← Component relationships
               /    ─┼─────────────┼─
              /     │ ARCHITECTURE│  ← System structure
             /      ─┼─────────────┼─
            /       │   OVERVIEW  │  ← High-level understanding
           ─────────┴─────────────┴─────────

Start broad, go deep systematically.

Phase 1: Overview Reconnaissance

1.1 Project Identification

Gather basic project information:

bash
# Check for documentation
ls -la README* ARCHITECTURE* SECURITY* CHANGELOG* docs/

# Identify build system
ls package.json Cargo.toml go.mod pyproject.toml Makefile

# Check for tests
ls -la test* spec* *_test* __tests__/

# Identify CI/CD
ls -la .github/workflows/ .gitlab-ci.yml Jenkinsfile .circleci/
1.2 Technology Stack Detection
bash
# Language distribution
find . -type f -name "*.py" | wc -l
find . -type f -name "*.js" -o -name "*.ts" | wc -l
find . -type f -name "*.go" | wc -l
find . -type f -name "*.rs" | wc -l
find . -type f -name "*.sol" | wc -l

# Framework indicators
grep -r "from flask" --include="*.py" | head -1
grep -r "from django" --include="*.py" | head -1
grep -r "express\|fastify" --include="*.js" | head -1
grep -r "anchor_lang" --include="*.rs" | head -1
1.3 Dependency Analysis
bash
# Python dependencies
cat requirements.txt pyproject.toml setup.py 2>/dev/null | grep -E "^\s*[a-zA-Z]"

# Node.js dependencies
cat package.json | jq '.dependencies, .devDependencies'

# Rust dependencies
cat Cargo.toml | grep -A 100 "\[dependencies\]"

# Go dependencies
cat go.mod | grep -E "^\s+[a-z]"
1.4 Create Technology Map
markdown
## Technology Map: [PROJECT NAME]

### Languages
| Language | Files | Lines | Primary Use |
|----------|-------|-------|-------------|
| Python | 150 | 25K | Backend API |
| TypeScript | 80 | 12K | Frontend |
| Solidity | 12 | 2K | Smart Contracts |

### Key Dependencies
| Package | Version | Purpose | Security Notes |
|---------|---------|---------|----------------|
| fastapi | 0.100.0 | Web framework | Recent CVEs: None |
| web3.py | 6.0.0 | Blockchain client | Check signing |
| pyjwt | 2.8.0 | JWT handling | Verify alg checks |

### Infrastructure
- Database: PostgreSQL 15
- Cache: Redis 7
- Message Queue: RabbitMQ
- Container: Docker + K8s

Phase 2: Architecture Mapping

2.1 Directory Structure Analysis
bash
# Top-level structure
tree -L 2 -d

# Identify entry points
find . -name "main.py" -o -name "app.py" -o -name "index.ts" -o -name "main.go"

# Identify config
find . -name "config*" -o -name "settings*" -o -name ".env*"
2.2 Component Identification

Look for common patterns:

project/
├── api/           # HTTP endpoints
├── auth/          # Authentication
├── core/          # Business logic
├── db/            # Database layer
├── models/        # Data models
├── services/      # External services
├── utils/         # Utilities
├── workers/       # Background jobs
└── tests/         # Test suite
2.3 Create Architecture Diagram
┌─────────────────────────────────────────────────────────────┐
│                        CLIENTS                              │
│              (Web, Mobile, API Consumers)                   │
└─────────────────────────┬───────────────────────────────────┘
                          │ HTTPS
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                      API GATEWAY                            │
│                   (Rate Limiting, Auth)                     │
└─────────────────────────┬───────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          ▼               ▼               ▼
    ┌──────────┐   ┌──────────┐   ┌──────────┐
    │  Auth    │   │  Core    │   │  Admin   │
    │ Service  │   │  API     │   │  API     │
    └────┬─────┘   └────┬─────┘   └────┬─────┘
         │              │              │
         └──────────────┼──────────────┘
                        │
          ┌─────────────┼─────────────┐
          ▼             ▼             ▼
    ┌──────────┐  ┌──────────┐  ┌──────────┐
    │ Database │  │  Cache   │  │ External │
    │ (Postgres)│  │ (Redis)  │  │  APIs    │
    └──────────┘  └──────────┘  └──────────┘
2.4 Trust Boundary Identification

Map where trust levels change:

markdown
## Trust Boundaries

### Boundary 1: Internet → API Gateway
- **Type:** Network boundary
- **Controls:** TLS, Rate limiting, WAF
- **Risks:** DDoS, Injection, Auth bypass

### Boundary 2: API Gateway → Services
- **Type:** Authentication boundary
- **Controls:** JWT validation, Role checks
- **Risks:** Token forgery, Privilege escalation

### Boundary 3: Services → Database
- **Type:** Data access boundary
- **Controls:** Query parameterization, Connection pooling
- **Risks:** SQL injection, Data leakage

### Boundary 4: Services → External APIs
- **Type:** Third-party integration
- **Controls:** API keys, Request signing
- **Risks:** SSRF, Secret exposure

Phase 3: Module Deep Dive

3.1 Entry Point Analysis

For each entry point type:

python
# HTTP Routes - map all endpoints
grep -rn "@app.route\|@router\|@api_view" --include="*.py"
grep -rn "app.(get|post|put|delete)\|router.(get|post)" --include="*.ts"

# CLI Commands
grep -rn "@click.command\|argparse\|clap" --include="*.py" --include="*.rs"

# Event Handlers
grep -rn "@consumer\|@handler\|on_message" --include="*.py"
3.2 Create Entry Point Map
markdown
## Entry Points

### HTTP API
| Method | Path | Handler | Auth | Input |
|--------|------|---------|------|-------|
| POST | /api/login | auth.login | None | JSON body |
| GET | /api/users | users.list | JWT | Query params |
| POST | /api/transfer | tx.transfer | JWT + 2FA | JSON body |
| GET | /admin/logs | admin.logs | Admin JWT | Query params |

### WebSocket
| Event | Handler | Auth | Data |
|-------|---------|------|------|
| connect | ws.connect | JWT | None |
| message | ws.message | Session | JSON |

### Background Jobs
| Queue | Handler | Trigger | Data Source |
|-------|---------|---------|-------------|
| emails | email.send | API call | Database |
| reports | report.gen | Cron | Database |
3.3 Data Flow Tracing

For each critical endpoint, trace data flow:

POST /api/transfer
       │
       ▼
┌──────────────────┐
│ Request Parser   │ ← Validate JSON schema
│ (validation.py)  │
└────────┬─────────┘
         │ TransferRequest
         ▼
┌──────────────────┐
│ Auth Middleware  │ ← Verify JWT, extract user
│ (middleware.py)  │
└────────┬─────────┘
         │ User context
         ▼
┌──────────────────┐
│ Transfer Service │ ← Business logic
│ (transfer.py)    │
└────────┬─────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐ ┌────────┐
│ DB     │ │External│
│ Write  │ │ API    │
└────────┘ └────────┘

Phase 4: Function-Level Analysis

4.1 Security-Critical Function Identification

Search for security-sensitive operations:

bash
# Authentication
grep -rn "def login\|def authenticate\|def verify_token" --include="*.py"
grep -rn "function login\|authenticate\|verifyToken" --include="*.ts"

# Authorization
grep -rn "def is_authorized\|def check_permission\|@requires_role" --include="*.py"

# Cryptography
grep -rn "encrypt\|decrypt\|hash\|sign\|verify" --include="*.py"
grep -rn "crypto\.\|bcrypt\|argon2" --include="*.py"

# Database
grep -rn "execute\|query\|cursor" --include="*.py"
grep -rn "\.query\|\.execute\|\.raw" --include="*.ts"

# File Operations
grep -rn "open\(.*\)\|read\|write\|unlink" --include="*.py"
4.2 Function Documentation Template

For each critical function:

markdown
### Function: `transfer_funds()`

**Location:** `services/transfer.py:45`

**Purpose:** Execute fund transfer between accounts

**Parameters:**
| Name | Type | Source | Validation |
|------|------|--------|------------|
| from_account | str | JWT claim | UUID format |
| to_account | str | Request body | UUID format, exists check |
| amount | Decimal | Request body | > 0, <= balance |

**Returns:** TransferResult

**Side Effects:**
- Writes to `transactions` table
- Calls external payment API
- Emits `transfer_completed` event

**Security Considerations:**
- Requires authenticated user
- Rate limited to 10/minute
- Amount validated against balance
- Audit logged

**Potential Risks:**
- Race condition if concurrent transfers?
- What if external API fails mid-transfer?
4.3 Call Graph Analysis
transfer_funds()
├── validate_request()
│   └── check_uuid_format()
├── get_user_balance()
│   └── db.query()
├── check_rate_limit()
│   └── redis.get()
├── execute_transfer()     ← CRITICAL
│   ├── db.begin_transaction()
│   ├── update_balance()   ← State change
│   ├── external_api.send() ← External call
│   └── db.commit()
└── emit_event()

Phase 5: Detail Reconnaissance

5.1 Configuration Analysis
bash
# Find all config loading
grep -rn "os.environ\|getenv\|config\." --include="*.py"
grep -rn "process.env\|config\." --include="*.ts"

# Check for hardcoded secrets
grep -rn "password\s*=\|secret\s*=\|api_key\s*=" --include="*.py"
grep -rn "-----BEGIN\|sk-\|pk_live_" .
5.2 Error Handling Review
bash
# Find exception handling
grep -rn "except.*:" --include="*.py" -A 2
grep -rn "catch\s*(" --include="*.ts" -A 2

# Find error responses
grep -rn "return.*error\|raise.*Error" --include="*.py"
5.3 Logging Analysis
bash
# Find logging statements
grep -rn "logger\.\|logging\.\|console\.log" --include="*.py" --include="*.ts"

# Check what's being logged
grep -rn "log.*password\|log.*token\|log.*secret" --include="*.py"

Output: Context Document

Template
markdown
# [PROJECT NAME] - Security Context Document

## Executive Summary
[2-3 sentences on what this system does]

## Technology Stack
[From Phase 1]

## Architecture
[Diagram from Phase 2]

## Trust Boundaries
[From Phase 2.4]

## Entry Points
[Table from Phase 3.2]

## Critical Functions
[Analysis from Phase 4]

## Data Flows
[Diagrams from Phase 3.3]

## Security Controls
| Control | Implementation | Location | Notes |
|---------|----------------|----------|-------|
| Authentication | JWT | middleware/auth.py | RS256 signing |
| Authorization | RBAC | decorators/auth.py | Role-based |
| Input Validation | Pydantic | schemas/*.py | Type checking |
| Encryption | AES-256-GCM | utils/crypto.py | At-rest |

## Areas Requiring Focus
1. [High-risk area 1]
2. [High-risk area 2]
3. [High-risk area 3]

## Open Questions
- [ ] How is X handled when Y?
- [ ] What happens if Z fails?

Quick Start Commands

bash
# Full recon script
./scripts/recon.sh /path/to/project

# Generate entry point map
./scripts/map-endpoints.sh /path/to/project

# Create call graph
./scripts/callgraph.sh /path/to/project

Skill Files

code-recon/
├── SKILL.md                        # This file
├── resources/
│   ├── recon-checklist.md          # Comprehensive checklist
│   └── question-bank.md            # Questions to answer
├── examples/
│   ├── web-app-recon/              # Web application example
│   └── smart-contract-recon/       # Smart contract example
├── templates/
│   └── context-document.md         # Output template
└── docs/
    └── advanced-techniques.md      # Deep dive techniques

Guidelines

  1. Top-down approach - Start broad, go narrow
  2. Document everything - Your notes are the deliverable
  3. Question assumptions - Verify what docs say vs. what code does
  4. Focus on trust boundaries - That's where bugs live
  5. Time-box phases - Don't get stuck in the weeds early
  6. Iterate - Revisit earlier phases as you learn more

© sendaifun, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files in skills/zz-code-recon of sendaifun/skills.

  • SKILL.md
  • docs/advanced-techniques.md
  • examples/web-app-recon/fastapi-example.md
  • resources/question-bank.md
  • resources/recon-checklist.md
  • templates/context-document.md

Open the folder on GitHubat commit 06b36b8

Compare with similar skills

Zz Code Recon next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Zz Code Recon compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Zz Code Recon this skillsendaifun/skills130—~3.3kAutomated safety check: NotesApache-2.0
Deepsec Documentation Guidevercel-labs/deepsec8.1k—~956Automated safety check: PassApache-2.0
Kubernetes Network Security Auditkubeshark/kubeshark12k—~7.3kAutomated safety check: NotesApache-2.0
Native Dependency Updatemono/SkiaSharp5.6k—~4.1kAutomated safety check: PassMIT
Semgrep Security Scantrailofbits/skills7.4k—~3.7kAutomated safety check: NotesCC-BY-SA-4.0
Skillward AuditFangcun-AI/SkillWard143—~2.9kAutomated safety check: PassCustom licence

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Categories

Questions about Zz Code Recon

What does Zz Code Recon do?

Deep architectural context building for security audits. An agent skill from sendaifun/skills. Zz Code Recon is an agent skill from sendaifun/skills. Deep architectural context building for security audits.

When should I use Zz Code Recon?

Zz Code Recon fits situations like: conducting security reviews; building codebase understanding; mapping trust boundaries; preparing for vulnerability analysis.

How do I install Zz Code Recon in Claude Code?

Run `npx skills add sendaifun/skills --skill zz-code-recon -a claude-code`. Or copy the skill folder (skills/zz-code-recon in sendaifun/skills) into .claude/skills/zz-code-recon in your project. Claude Code loads it when a task matches its description.

How do I install Zz Code Recon in Codex?

Run `npx skills add sendaifun/skills --skill zz-code-recon -a codex`. Or copy the skill folder (skills/zz-code-recon in sendaifun/skills) into .agents/skills/zz-code-recon in your project. Codex loads it when a task matches its description.

Can I use Zz Code Recon in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sendaifun/skills --skill zz-code-recon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zz-code-recon, .gemini/skills/zz-code-recon, .github/skills/zz-code-recon and .opencode/skills/zz-code-recon in your project.

What does Zz Code Recon need to run?

Going by SKILL.md and its folder, Zz Code Recon needs the command-line tools its instructions call (jq). Our summary lists: Python 3; Node.js; Docker.

Does Zz Code Recon access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Zz Code Recon safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Zz Code Recon use?

Zz Code Recon is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zz Code Recon use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Zz Code Recon?

Skills that share tags, products or a category with Zz Code Recon: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), Native Dependency Update (mono/SkiaSharp, 5.6k stars) and Semgrep Security Scan (trailofbits/skills, 7.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zz Code Recon?

sendaifun (a GitHub organization) maintains it in sendaifun/skills, which has 130 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on July 31, 2026.

Source: sendaifun/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.