Scans a Python recipe to find every place an environment variable is accessed — including `os.environ.setdefault("V", "d")`, whose "d" would otherwise be a hidden default a user editing .env.example has no way to discover — then ensures all variables are declared in `.env.example`, that `load_dotenv()` is bootstrapped in the package `__init__.py`, and that `python-dotenv>=1.0.0` is listed in `pyproject.toml`. When a new entry is added to `.env.example`, the extracted default from source is written as the value (with a `# extracted-by:extract-env-vars` marker and provenance comment); values that look like stubs (`"my-project-id"`, `"changeme"`, `"<...>"`) are downgraded to the TODO placeholder but the source string is preserved in the marker comment. Also detects hardcoded model-name string literals (e.g. `"gemini-3.5-flash"` in `agent.py`) and rewrites them to an `os.getenv(...)` call. The variable name is derived from the assignment target when it names a model (`DEFAULT_EMBEDDING_MODEL` → `EMBEDDING_MODEL`)
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SKILL.md
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name
extract-python-environment-variables
description
Scans a Python recipe to find every place an environment variable is accessed — including `os.environ.setdefault("V", "d")`, whose "d" would otherwise be a hidden default a user editing .env.example has no way to discover — then ensures all variables are declared in `.env.example`, that `load_dotenv()` is bootstrapped in the package `__init__.py`, and that `python-dotenv>=1.0.0` is listed in `pyproject.toml`. When a new entry is added to `.env.example`, the extracted default from source is written as the value (with a `# extracted-by:extract-env-vars` marker and provenance comment); values that look like stubs (`"my-project-id"`, `"changeme"`, `"<...>"`) are downgraded to the TODO placeholder but the source string is preserved in the marker comment. Also detects hardcoded model-name string literals (e.g. `"gemini-3.5-flash"` in `agent.py`) and rewrites them to an `os.getenv(...)` call. The variable name is derived from the assignment target when it names a model (`DEFAULT_EMBEDDING_MODEL` → `EMBEDDING_MODEL`), else `MODEL_NAME` (single model) or `MODEL_NAME_GENERATED_1` / `MODEL_NAME_GENERATED_2`, … (multiple models). Normally no fallback default is written into the Python source; the one exception is when no `load_dotenv()` bootstrap could be installed (no package `__init__.py`), where the original literal is kept as the fallback so the lookup cannot evaluate to `None` at runtime. The model string is written as the value in `.env.example` with a comment prompting a rename. When re-run against a recipe whose `.env.example` already has entries, the writer classifies each entry (skill-authored vs. user-authored, TODO vs. real value) and only rewrites lines it can prove it authored; user-authored lines are always preserved. A stale TODO (either skill-authored or a bare v1-era `<TODO: update-this-value>` with no inline comment) is upgraded in place when source can supply a real default. IMPORTANT — two hard rules the skill NEVER breaks: (1) USER-EDIT SAFETY. Any `.env.example` line the skill cannot prove it authored is USER_OWNED and is never modified. The rewriter fails closed on any structural ambiguity (quoted values, backslash continuation, duplicate declarations) — a stale TODO left in place is cheap; a clobbered user edit is not. (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 the recipe author wrote by hand are LEFT UNTOUCHED — the skill's only writes to Python files are (a) the `load_dotenv()` bootstrap; (b) `# noqa: E402` on trailing relative imports that would otherwise trip Ruff; (c) hardcoded model-literal replacement. Use when the user wants to "extract env vars", "update .env.example", "add load_dotenv", "surface setdefault defaults", "upgrade stale TODOs in .env.example", "replace hardcoded model names", or "fix environment variables" in a Python recipe.
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.
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.
<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
with a note.
<TODO: update-this-value>
no default in source
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).
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.
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:
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.
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:
Existing entry looks like
Source now provides
Action
Missing
any
append with resolved default (or TODO)
A line the skill wrote (has # extracted-by:extract-env-vars marker) with <TODO: update-this-value> value
a REAL default (not placeholder-shape)
upgrade in place
Same as above
no default OR a placeholder-shape default
skip (would be TODO → TODO churn)
Bare VAR=<TODO: update-this-value> line with no marker AND no inline comment — a v1-era TODO
a REAL default
upgrade in place (v1 → v2 migration)
A line the skill wrote with a REAL value
source has any value (even different)
skip (never overwrite skill-authored values on drift)
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
Always use the script — never manually edit .env.example, __init__.py,
or pyproject.toml to perform these changes.
Ask for the recipe directory if the user has not provided one. Do not
assume a path.
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
Field
Required
Description
Recipe directory
Yes
Path to the recipe root (e.g. contrib/python/my-recipe, core/python/my-recipe, or skills/retail/store-ops)
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:
Variable
Value written to .env.example
Source
GOOGLE_CLOUD_PROJECT
<TODO: update-this-value>(downgraded — source had "my-project-id")
agent.py (setdefault)
GOOGLE_GENAI_USE_VERTEXAI
TRUE
__init__.py (setdefault)
LOG_LEVEL
INFO
logging_setup.py (getenv fallback)
API_KEY
<TODO: update-this-value>(no default in source)
client.py (getenv, no fallback)
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:
Variable
Was
Now
Source
USE_VERTEX
<TODO: update-this-value>
TRUE
__init__.py (setdefault)
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
