Extract Python Environment Variables

Extract Python Environment Variables

Use this skill to ensure a Python recipe properly declares and loads all environment variables it uses.

What This Skill Does

Runs scripts/extract_env_vars.py against a recipe directory. The script:

1. Scans all .py files (excluding tests/ and common cache/venv directories) for environment variable accesses:

   • os.environ["VAR"]
   • os.environ.get("VAR") / os.environ.get("VAR", "default")
   • os.getenv("VAR") / os.getenv("VAR", "default")
   • os.environ.setdefault("VAR", "default")   (v2)

   Only names matching ^[A-Z_][A-Z0-9_]*$ (UPPER_SNAKE_CASE) are captured; a lowercase name like os.getenv("my_api_key") is skipped and a [WARN] line lists any that were dropped. Rename such vars to uppercase in source, or add them to .env.example by hand.

2. Updates .env.example — appends any variables not already declared AND upgrades stale TODO entries in place. Existing user-authored lines are always preserved (see Rule 1 below).

   • Creates .env.example from scratch if it does not exist.
   • Each new/upgraded line has the shape:

     VAR=<value>  # extracted-by:extract-env-vars; <provenance>
     

   • <value> is the resolved default extracted from source when the author committed to one — with os.environ.setdefault(...) beating os.getenv("V", "d") / os.environ.get("V", "d") (setdefault is a stronger commitment: it mutates the process environment, while getenv fallback is per-read). Alphabetical file order breaks ties.
   • Values that look like stubs are downgraded to <TODO: update-this-value> — e.g. my-project-id, your-api-key, changeme, <...>, anything containing example.com. The source string is preserved in the marker comment so the maintainer sees what was found and can fix the source too.
   • When no source supplied a string-literal default, the value is <TODO: update-this-value> with a no default in source note.
   • Stale TODO upgrade (v2.1): if an existing entry is either a skill-authored TODO (has the marker) or a bare v1-era TODO (exact value <TODO: update-this-value>, no marker, no inline comment) AND source can now supply a real default, the line is rewritten in place. This closes the discoverability loop for recipes originally processed by v1 (which always wrote TODOs regardless of what source said).

3. Injects load_dotenv() into the package __init__.py (the first subdirectory inside the recipe that contains an __init__.py, skipping tests/ and hidden directories). The snippet injected is:

   from dotenv import load_dotenv
   
   # Load variables from .env if present. In production the environment is
   # already populated by the platform (Cloud Run, GKE, etc.), so a missing
   # .env is expected and not an error.
   load_dotenv()
   

   If load_dotenv is already present the injection is skipped.

   If no package __init__.py exists (common in vertical skills under skills/, where the code lives in a plain scripts/ directory rather than an importable package) the injection is skipped with a [WARN], and the step reports that no bootstrap is in place. Step 4 below depends on that answer.

   Additionally — always, regardless of whether we injected — appends # noqa: E402 -- must come after load_dotenv() to any top-level relative import (from .x import y) that sits AFTER a non-import module-level statement. Two cases this covers:

   • Fresh injection. The injected load_dotenv() call pushes pre-existing trailing relative imports below a non-import statement, so they'd trigger Ruff E402 ("module-level import not at top of file") when Phase 4 (ruff) of prepare-python-recipe runs.

   • Author-written bootstrap. The recipe author already wrote load_dotenv() + os.environ.setdefault(...) calls followed by a trailing from . import agent, but never marked the trailing import. The skill did NOT inject anything (load_dotenv was already present) but still adds the noqa suffix so the file is lint-clean on the pipeline's next ruff pass.

   The suppression pass is precise — a relative import at the very TOP of the file (before any non-import statement) is fine and left untouched. Idempotent: a line that already carries # noqa: E402 is skipped.

4. Replaces hardcoded model names in source (e.g. model="gemini-3.5-flash" in agent.py) with an os.getenv(...) call.

   Position decides eligibility. A model string is only promoted when it is a configurable constant. Three positions mean it is something else, and are left untouched:

   • Collection-literal entries (dict keys and values, list/set/tuple items) — lookup tables and enumerations of supported models:

     IMAGE_MODELS = {
         "flash": "gemini-2.5-flash-image",
         "pro":   "gemini-2.5-pro-image",
     }
     

     Rewriting the values collapses the table onto one env var; rewriting the keys is worse, because IMAGE_MODELS.get("flash") then never matches anything. A dict key must stay a static literal.

   • Subscript indices — IMAGE_MODELS["gemini-3.1"] is a key into such a table, so replacing it looks up a different entry.

   • Comparison operands — if "gemini-3.1" in model_id tests a value rather than configuring one.

   Skipping a legitimate extraction is cheap (lift it by hand); silently breaking a lookup table is not, so this errs towards skipping. Every skipped literal is listed in an [INFO] block, so nothing is hidden — and because these strings never reach the naming step, a dict target like IMAGE_MODELS can no longer leak into .env.example as a variable name.

   Variable name. The assignment target that holds the literal is used when it names a model, since it carries far more meaning than a generic fallback — DEFAULT_EMBEDDING_MODEL = "gemini-embedding-001" and embedding_model = cfg.get("embedding_model", "gemini-embedding-001") both yield EMBEDDING_MODEL. This matters in recipes that already read a different model var (say GEMINI_MODEL for the LLM), where a second bare MODEL_NAME would be actively misleading. When a string is assigned to conflicting target names, the most frequent wins, ties broken alphabetically. Otherwise:

   • A single unnamed model → MODEL_NAME
   • The rest → MODEL_NAME_GENERATED_1, MODEL_NAME_GENERATED_2, … (sorted alphabetically for determinism)

   Fallback argument. Normally the emitted call is bare — os.getenv("EMBEDDING_MODEL"), no default — because default values are the maintainer's decision, not the skill's. That is only safe when the load_dotenv() bootstrap from step 3 is in place to populate the environment.

   When step 3 could not install a bootstrap (no package __init__.py), nothing reads .env, so a bare lookup would evaluate to None at runtime and silently break the recipe. In that case the original literal is preserved as the fallback — os.getenv("EMBEDDING_MODEL", "gemini-embedding-001") — which keeps behaviour identical to before the rewrite while still lifting the value into the environment. The step logs an [INFO] line explaining the choice.

   The actual model string is written as the value in .env.example (e.g. EMBEDDING_MODEL=gemini-embedding-001) with a comment prompting the maintainer to rename the variable if the derived name isn't right.

5. Updates pyproject.toml — adds python-dotenv>=1.0.0 to [project] dependencies if it is not already there.

Hard rules the skill NEVER breaks

Rule 1 — User-edit safety for .env.example. The writer classifies every existing entry before touching anything, and only rewrites lines it can prove it authored. Concretely:

Golden rule underneath all of this: fail closed. If the classifier can't confidently prove a line was skill-authored or is a bare v1 TODO, it's USER_OWNED and untouchable. If the rewriter finds a line whose structure it doesn't understand (quoted value, backslash continuation, duplicate declarations), it refuses and warns rather than guessing. And before any write, the assembled file is re-parsed to verify every planned upgrade re-parses as a skill-authored real value — a bug in the rewriter cannot silently corrupt .env.example.

To force regeneration of a line the skill would otherwise skip, delete the line and re-run.

Rationale for extracting defaults (v2) and upgrading stale TODOs (v2.1): a value hidden inside a Python file that a user must know about but has no reason to look at is, in practice, undocumented. .env.example is the documented configuration surface. Surfacing what the author already committed to in code — and updating a stale TODO once source can supply a real default — makes the configuration discoverable without inventing anything new. The value in source WAS the value; we're only lifting it into view.

Rule 2 — Additive-only for Python files. The skill never writes new os.environ.setdefault(...) bootstrap lines into any Python file. Pre-existing os.environ.setdefault(...) or os.getenv("VAR", "default") calls that a recipe author wrote by hand are LEFT UNTOUCHED. The skill's only writes to Python files are:

• Adding the from dotenv import load_dotenv + load_dotenv() snippet (once, only if not already present).
• Appending # noqa: E402 to trailing relative imports that would otherwise trip Ruff after the env-bootstrap block.
• Replacing hardcoded model literals with os.getenv(...) calls — bare when a load_dotenv() bootstrap is in place, otherwise retaining the original literal as the fallback (see step 4 above).

Note that scanning os.environ.setdefault(...) and lifting its value into .env.example (v2) does NOT violate Rule 2 — the skill READS from Python source (unchanged behaviour) and WRITES only to .env.example.

Rules for the Agent

1. Always use the script — never manually edit .env.example, __init__.py, or pyproject.toml to perform these changes.
2. Ask for the recipe directory if the user has not provided one. Do not assume a path.
3. After the script succeeds, remind the user to:
   • Copy .env.example → .env and fill in real values before running locally.
   • Run uv sync to pick up the python-dotenv dependency if it was newly added.

Input

If the user has not specified the recipe directory, ask for it before proceeding.

Run

Run it through uv so it always executes on a Python 3.11+ interpreter — the script uses the stdlib tomllib, which only exists from 3.11 onward. A bare python3 that resolves to 3.9/3.10 fails with ModuleNotFoundError: tomllib. No --with packages are needed (the script is stdlib-only).

uv run --no-project python3 \
  .agents/skills/extract-python-environment-variables/scripts/extract_env_vars.py \
  --recipe-dir <RECIPE_DIR>

Preview first (optional)

Add --dry-run to report exactly what would change without modifying any files. Nothing is written to .env.example, __init__.py, pyproject.toml, or any source file. Useful for inspecting a recipe before committing to the edits:

uv run --no-project python3 \
  .agents/skills/extract-python-environment-variables/scripts/extract_env_vars.py \
  --recipe-dir <RECIPE_DIR> --dry-run

In dry-run output, actions are prefixed with [DRY-RUN] and phrased as "Would add" / "Would inject" / "Would replace".

Respond

Do not show the script's raw stdout. Reformat its results into clear Markdown tables (variables to add, files to update, model replacements) so they are easy to read — this is especially important for --dry-run output. For each variable or model string, include a column with the source file where it was found (locate it in the recipe's Python source; ignore .env and .env.example).

For added variables, surface the resolved default and its provenance so the maintainer knows which value the skill chose and where it came from:

If the script reports upgrades (stale TODO entries rewritten in place with real defaults from source), surface those in a separate table so the maintainer can eyeball each one:

If the script logged [WARN] Refused to upgrade … for any variable, surface that too — the line's structure was ambiguous and was left untouched deliberately.

Once the script finishes successfully, summarise what changed:

• Which variables were added to .env.example (with resolved value + source) or confirm it was already up to date.
• Which existing TODOs were upgraded in place (v2.1) with real defaults from source.
• Any values that were downgraded to <TODO> because they looked like placeholders — the maintainer should fix the source too.
• Whether load_dotenv() was injected or was already present.
• Whether python-dotenv was added to pyproject.toml or was already there.

Then remind the user of the next steps:

Next steps:
  cp <RECIPE_DIR>/.env.example <RECIPE_DIR>/.env   # then fill in real values
  cd <RECIPE_DIR> && uv sync                        # install python-dotenv if newly added

Do not make any further changes. End your turn.