environment-discovery

environment-discovery is a skill for Claude Code, Codex from vstorm-co/pydantic-deepagents. It costs 13 tokens per session (422 once invoked), scanned A, original, MIT.

A checklist for exploring an unfamiliar software environment before changing it, including its files, data formats, available tools, and existing code.

In plain words
What is it for?
Use it at the start of a coding task to inspect a workspace, understand its build system, examine data files, and identify usable programs or libraries.
Why use it?
It prevents work based on incorrect assumptions about the project structure, input data, or installed tools.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it at the start of a coding task to inspect a workspace, understand its build system, examine data files, and identify usable programs or libraries.

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Install with agentmods
npx agentmods add skills/vstorm-co/pydantic-deepagents/environment-discovery
About the project

Pydantic Deep Agents is a self-hosted terminal AI assistant and Python framework for building coding, research, and other AI agents. It gives agents tools such as file access, shell commands, planning, memory, sub-agents, sandboxed execution, and MCP connections, and supports different models.

vstorm-co/pydantic-deepagents · 1,059 stars · on GitHub · vstorm-co.github.io

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add vstorm-co/pydantic-deepagents --skill environment-discovery
Clone the repo
git clone --depth 1 https://github.com/vstorm-co/pydantic-deepagents

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for environment-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/environment-discovery/github.svg)](https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/environment-discovery)
Your own site
<a href="https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/environment-discovery"><img src="https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/environment-discovery/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.

agentmods 80×15 button for environment-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/environment-discovery"><img src="https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/environment-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 422 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00013 $0.00422
Opus 5 $0.00006 $0.00211
Sonnet 5 $0.00003 $0.00084
Haiku 4.5 $0.00001 $0.00042

Measured 9d ago against content hash af3f39242613, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

environment-discovery 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 9d 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.

apps/cli/skills/environment-discovery/SKILL.md · 59 lines

What it actually says

Environment Discovery

When dropped into an unfamiliar environment, ALWAYS explore before acting.

Step 1: Understand the workspace

ls -la /app/          # or the working directory
find . -type f | head -50
  • What files exist? What are their sizes?
  • Are there READMEs, Makefiles, config files?
  • What languages/frameworks are involved?

Step 2: Inspect data files

Before writing any code that reads data, understand the format:

  • file <filename> — detect file type (binary, text, encoding)
  • head -20 <file> — first lines of text files
  • xxd <file> | head -20 — hex dump for binary files
  • wc -l <file> — line count for text files
  • stat <file> — exact file size in bytes
  • python3 -c "import struct; ..." — parse binary headers

Step 3: Check available tools

which python3 gcc g++ make cmake node npm cargo rustc java go
pip list 2>/dev/null | head -20
  • What compilers/interpreters are installed?
  • What libraries are available?
  • What package managers can you use?

Step 4: Read existing code

If there are existing source files:

  • Read them FULLY before modifying
  • Understand the build system (Makefile, CMakeLists.txt, pyproject.toml)
  • Check for existing tests

Key Principles

  • NEVER assume file formats — always inspect first
  • NEVER assume tools are installed — always check
  • A 500MB file is NOT a "small file" — plan for it
  • Binary files need byte-level inspection, not cat
  • Spend 30 seconds exploring to save 5 minutes debugging
Changes

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.

  1. 9d ago First seen · 59 lines · 13 tokens per session scan A af3f39242613

Subscribe to this mod's changes

environment-discovery is a skill published in the GitHub repository vstorm-co/pydantic-deepagents (1,059 stars, last pushed 18d ago), licensed MIT. It adds 13 tokens to every session and 422 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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