Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/eugenepyvovarov/mcpbundler-agent-skills-marketplacenpx agentmods add skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-reconWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-recon)<a href="https://agentmods.dev/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-recon"><img src="https://agentmods.dev/badge/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-recon/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-recon"><img src="https://agentmods.dev/badge/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/code-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03503 |
| Opus 5.5 | $0.00000 | $0.01401 |
| Sonnet 5.5 | $0.00000 | $0.00701 |
| Haiku 4.5 | $0.00000 | $0.00350 |
Grade A, and why
code-recon scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to zz-code-recon — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
# 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
# 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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 483 lines · 0 tokens per session scan A 5d4bd1aa630c
code-recon is a skill published in the GitHub repository eugenepyvovarov/mcpbundler-agent-skills-marketplace (11 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,503 tokens. A static security scan graded it A with 0 findings. It is 91% identical to zz-code-recon, differing in 5 lines, and is treated as a copy.
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