Agent skill

Environment Discovery

by vstorm-co in vstorm-co/pydantic-deepagents

Systematic exploration of unknown environments before starting work

MITAuto-check passed

Install Environment Discovery

skills CLI
$ npx skills add vstorm-co/pydantic-deepagents --skill environment-discovery -a claude-code

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

GitHub CLI
$ gh skill install vstorm-co/pydantic-deepagents environment-discovery --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/vstorm-co/pydantic-deepagents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/apps/cli/skills/environment-discovery .claude/skills/environment-discovery && 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
environment-discovery
GitHub stars
1.1k
Token cost
~419 tokens
SKILL.md length
185 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Systematic exploration of unknown environments before starting work

  • Works in 4 steps: Understand the workspace → Inspect data files → Check available tools → …
  • SKILL.md covers Step 1: Understand the workspace, Step 2: Inspect data files, Step 3: Check available tools and Step 4: Read existing code, plus 1 more section
  • Calls pip and python3

What it does

Environment Discovery is an agent skill from vstorm-co/pydantic-deepagents. Systematic exploration of unknown environments before starting work

Its SKILL.md is about 420 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Python. The repository describes itself as: Open-source, self-hosted Claude Code - a terminal AI assistant and the Python framework behind it. Tool-calling, sandboxed execution, multi-agent teams, skills, checkpoints… The licence is MIT.

Example prompts

  • “/environment-discovery”

Requirements

  • Python 3

Workflow steps

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

  1. Understand the workspace
  2. Inspect data files
  3. Check available tools
  4. Read existing code

What it can do on your machine

Read from SKILL.md and the folder at commit 650b592. 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:

    • pip
    • python3

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Environment Discovery loads about 419 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 185 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~419

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 passed

The automated check found no risky patterns in SKILL.md.

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 vstorm-co/pydantic-deepagents at commit 650b592, republished under its MIT licence (© vstorm-co). 185 words, ~419 tokens.

Download SKILL.mdSave it as .claude/skills/environment-discovery/SKILL.md (or your agent's skills folder).
name
environment-discovery
description
Systematic exploration of unknown environments before starting work
tags
exploration, setup, benchmark
version
1.0.0

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

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

Files

Just SKILL.md in apps/cli/skills/environment-discovery of vstorm-co/pydantic-deepagents.

Open the folder on GitHubat commit 650b592

Compare with similar skills

Environment Discovery 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.

Environment Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Environment Discovery this skillvstorm-co/pydantic-deepagents1.1k—~419Automated safety check: PassMIT
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
PDF Processinganthropics/skills180k48 repos~2kAutomated safety check: PassProprietary
NotebookLM Research AssistantPleasePrompto/notebooklm-skill7.8k14 repos~2.4kAutomated safety check: NotesMIT
Manim Video Productionbrowser-use/video-use28k6 repos~3kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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Works with

Questions about Environment Discovery

What does Environment Discovery do?

Systematic exploration of unknown environments before starting work. Environment Discovery is an agent skill from vstorm-co/pydantic-deepagents.

How do I install Environment Discovery in Claude Code?

Run `npx skills add vstorm-co/pydantic-deepagents --skill environment-discovery -a claude-code`. Or copy the skill folder (apps/cli/skills/environment-discovery in vstorm-co/pydantic-deepagents) into .claude/skills/environment-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Environment Discovery in Codex?

Run `npx skills add vstorm-co/pydantic-deepagents --skill environment-discovery -a codex`. Or copy the skill folder (apps/cli/skills/environment-discovery in vstorm-co/pydantic-deepagents) into .agents/skills/environment-discovery in your project. Codex loads it when a task matches its description.

Can I use Environment Discovery 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 vstorm-co/pydantic-deepagents --skill environment-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/environment-discovery, .gemini/skills/environment-discovery, .github/skills/environment-discovery and .opencode/skills/environment-discovery in your project.

What does Environment Discovery need to run?

Going by SKILL.md and its folder, Environment Discovery needs the command-line tools its instructions call (pip and python3). Our summary lists: Python 3.

Does Environment Discovery access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Environment Discovery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Environment Discovery use?

Environment Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Environment Discovery use?

About 419 tokens (SKILL.md is roughly 1.7k 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 Environment Discovery?

Skills that share tags, products or a category with Environment Discovery: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Environment Discovery?

vstorm-co (a GitHub organization) maintains it in vstorm-co/pydantic-deepagents, which has 1,077 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 6, 2026.

Source: vstorm-co/pydantic-deepagents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.