Read a ***plain project — its `.plain` files, `test_scripts/`, `config.yaml`(s), and `resources/` — and determine every command-line tool, runtime, package manager, and external service the project needs on the host machine. Probe the host for each one, then emit a `PASS` / `FAIL` report listing what's installed (with versions), what's missing, and concrete OS-specific install commands for the gaps. Run this any time someone is about to render, test, or onboard onto a ***plain project for the first time.
설치
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
Read a ***plain project — its `.plain` files, `test_scripts/`, `config.yaml`(s), and `resources/` — and determine every command-line tool, runtime, package manager, and external service the project needs on the host machine. Probe the host for each one, then emit a `PASS` / `FAIL` report listing what's installed (with versions), what's missing, and concrete OS-specific install commands for the gaps. Run this any time someone is about to render, test, or onboard onto a ***plain project for the first time.
Check Plain Env
Always use the skill load-plain-reference to retrieve the ***plain syntax rules — but only if you haven't done so yet.
This skill answers one question: **does the host machine have everything the *plain project needs to render and test? It reads the project, derives the requirement list, probes the machine, and returns a report. It never installs anything itself — that decision belongs to the user.
When to run
**First time you open a *plain project on a new machine — before the first codeplain <module>.plain or ./test_scripts/run_unittests*.sh invocation. This is the most common case.
At the start of forge-plain Phase 3 environment verification — forge-plain historically did this inline; delegate to this skill instead so the same check runs the same way everywhere.
After add-feature — when the new feature brought in a new dependency (a new framework, a new service, a new package manager) the project didn't need before.
Before a real render — alongside plain-healthcheck. plain-healthcheck answers "are the specs renderable?"; this skill answers "can this machine render them?". Both should pass.
Onboarding — when a teammate (or CI runner, or fresh dev container) is about to start using the project.
Debugging a confusing failure — when a test script exits with "command not found", "module not found", "could not connect", or similar before any test runs.
On demand — whenever the user asks "what do I need to install for this project?".
This skill is read-only and observational. It does not edit .plain files, scripts, or configs, and it never invokes install commands on the user's behalf.
What this skill does NOT do
It does not install anything. It only suggests install commands and lets the user run them.
It does not generate scripts, modify config.yaml, or change project files. Use implement-*-testing-script, init-config-file, etc. for that.
It does not start services (databases, brokers, Docker daemons). It only checks whether the right binaries and (optionally) running endpoints are reachable.
It does not validate the specs themselves (syntax, dry-run, complexity). Use plain-healthcheck for that — the two skills are complementary.
It does not print secrets. Check whether an env var is set (e.g. printenv FOO >/dev/null && echo set || echo missing), never echo its value.
Workflow
The skill is a derive → probe → report pass. It does not stop at the first missing tool; it surfaces every gap so the user gets a single complete shopping list.
Step 1 — Inventory the project
Detect the host OS first. Run uname -s 2>/dev/null || echo "$OS". Use the result to (a) pick the right script extensions to inspect (.sh vs .ps1) and (b) pick the right install commands later (Homebrew, apt, dnf, pacman, choco, scoop, winget).
List every .plain file in the repo root and in any subdirectory that contains them. Note which are top modules (not requires-ed by anything) and which are import modules (under template/).
List every config.yaml in the repo and read each one. Note the *-script keys it sets and the values they point at.
List every script under test_scripts/ (both .sh and .ps1 — projects can ship both).
List every file under resources/ if the directory exists. Schemas and protocol files there often imply running services (e.g. a Postgres .sql schema implies a Postgres server).
Print a one-line inventory summary, e.g. Detected: 2 top modules (backend/api.plain, frontend/web.plain), 2 config.yaml(s), 4 test_scripts, 3 resources. Host: Darwin.
Step 2 — Derive the requirement list (at runtime, from what the project actually says)
The requirement list is not pre-baked into this skill. There is no catalog of "things to always check". The list is derived for each project from the project itself, by reading the specs and scripts in front of you and classifying every signal into one of the categories below.
What to check vs. what not to check
The single most important rule:
Check only the layers a package manager cannot install. Anything pip install -r requirements.txt, npm ci, mvn install, cargo fetch, go mod download, bundle install, composer install, etc. would resolve is out of scope for this skill. It will be installed by the project's own scripts the moment they run. Checking it here is wasted work and creates false-negative reports the moment the manifest changes.
Concretely:
❌ Do NOT check individual language packages — not torch, not requests, not numpy, not FastAPI, not express, not react, not a specific JAR in pom.xml, not a specific gem in Gemfile, not a specific crate in Cargo.toml. The language's package manager handles all of it. The same rule applies in every language — Python, Node.js, Java, Go, Rust, Ruby, PHP, .NET, Dart, etc.
✅ Do check the layers below the package manager, which fall into four categories. Build the requirement list at runtime by walking through each category and adding only what the project actually needs.
Category 1 — Language toolchains and their package managers
For each language the project uses, the toolchain must be on the host — the package manager cannot install itself. Decide which toolchains apply by reading the specs and scripts:
Signals in .plain files: phrases like should be in Python, should be in Java, should be in Go in ***implementation reqs***. Find the language; ignore the framework (the framework is a package, not a toolchain).
Signals in test_scripts/ (more reliable, because scripts are the executable contract): which interpreter / build tool is invoked at the top level. python3 / pip → Python toolchain. node / npm / pnpm / yarn → Node.js toolchain. mvn / gradle → JDK + Maven/Gradle. go → Go toolchain. cargo → Rust toolchain. dotnet → .NET SDK. ruby / bundle → Ruby + Bundler. php / composer → PHP + Composer. dart / flutter → Dart/Flutter SDK.
For each detected toolchain, probe both the interpreter/compiler and its package manager:
Python → python3 --version + pip --version.
Node.js → node --version + npm --version (or pnpm/yarn if the scripts use them).
That's where the language-package check stops. Once python3 and pip are on the host, pip install -r requirements.txt will handle torch, numpy, requests, etc. when the test script runs. Don't probe for them here.
Category 2 — External services
Services that run as separate processes / daemons and cannot be installed by a language package manager. Identify them at runtime by reading the specs and config:
Signals: a .plain file names a service the implementation talks to (database, cache, queue, broker, orchestrator). A resources/*.sql schema implies a database. A docker-compose.yml (if present) lists services explicitly.
For each service found, probe both that its CLI is available and — when possible — that the service is reachable. "Binary present but service not running" should report as WARN, not FAIL; the user may want to start it on demand.
No catalog — if the spec says "uses PostgreSQL" you check Postgres, if it says "uses Redis" you check Redis, if it says "uses some-niche-service" you look up its CLI on the fly and probe that. Don't pretend the list is closed.
Category 3 — System binaries that language packages wrap
Some language packages are thin wrappers around a system binary that pip install / npm install cannot install for you. The package manager installs the wrapper; the wrapped binary still has to be on the host separately. This is the most commonly missed category.
Decide at runtime by reading the workflow described in the specs. If the workflow can't physically run without an external tool, that tool needs to be checked even though the language wrapper around it is in requirements.txt. Examples of the pattern (not an exhaustive list — derive yours from the actual project):
The spec describes OCR → a wrapper like pytesseract is in requirements.txt, but the host still needs the tesseract binary and the language data files.
The spec describes video/audio processing → ffmpeg-python is in the manifest, but the ffmpeg binary itself must be on the host.
The spec describes PDF extraction → a Python wrapper is in the manifest, but the pdftotext / pdftoppm binaries from poppler-utils are still required.
The spec describes headless-browser e2e testing → the npm package is in package.json, but the actual browser binaries (Chromium, Firefox, WebKit) must be downloaded onto the host separately.
The spec describes shelling out to any external CLI from generated code → that CLI must be on the host.
Heuristic for spotting Category 3: if removing the package from requirements.txt would still leave a binary on the host that the project depends on, the binary belongs in Category 3. If the package is the only thing the project needs, it's package-manager territory and out of scope.
Whenever the test scripts themselves invoke a non-language binary (ffmpeg, psql, docker, tesseract, ...), that binary is automatically Category 3 — the script is the executable contract. Always reconcile the spec-derived list against the scripts before probing.
Category 4 — Hardware, drivers, accelerators
Things the OS — not any package manager — must provide. The only category where the chain runs deeper than "is this binary on PATH?".
Signals: a spec mentions GPU acceleration, local model training/fine-tuning, real-time inference, CUDA, MPS / Apple Silicon, hardware-accelerated video encoding, etc.
The check is layered, and each layer fails independently. Probe each one and surface each result separately, in order, so the user sees exactly where the chain breaks:
Accelerator SDK installed? (e.g. nvcc --version for the CUDA toolkit.)
Acceleration libraries discoverable? (e.g. cuDNN via ldconfig -p \| grep libcudnn on Linux.)
The runtime can actually see the accelerator? (e.g. a one-line Python probe through whatever framework the spec named: python -c "import torch; print(torch.cuda.is_available(), torch.version.cuda)".) This particular layer does require the language package, so run it after the test scripts have had a chance to install dependencies — or surface it as "can be verified after pip install" if dependencies aren't installed yet.
Version compatibility. If the runtime reports a different CUDA major version than the toolkit, surface a WARN. Driver / toolkit / runtime version mismatches are the most common silent GPU bug.
The same shape applies to other accelerators: Apple Silicon / MPS (uname -m, sw_vers, framework probe), AMD ROCm (rocminfo), Intel oneAPI, etc. Look up the relevant probe at runtime from the spec's terminology.
Worked example — the GPU case:
A .plain file declares: :Implementation: should fine-tune :BaseModel: on the user's local hardware using PyTorch. Training should use available GPUs when present.
What the package manager handles (out of scope for this skill): torch itself. pip install -r requirements.txt will fetch the right wheel.
What this skill must probe (in scope):
The Python toolchain (Category 1): python3 --version, pip --version.
The NVIDIA driver (Category 4, layer 1): nvidia-smi.
At least one GPU visible (Category 4, layer 2): nvidia-smi -L lists ≥ 1 device.
CUDA toolkit (Category 4, layer 3): nvcc --version.
cuDNN discoverable (Category 4, layer 4): ldconfig -p \| grep libcudnn on Linux; skip on macOS.
PyTorch actually sees the GPU (Category 4, layer 5): python -c "import torch; print(torch.cuda.is_available(), torch.version.cuda)" — noting this requires torch to be installed (i.e. requires the test scripts to have run their install step at least once).
Version match (Category 4, layer 6): cross-check torch.version.cuda against nvcc --version. Mismatch → WARN.
The skill never asks "is torch installed?". That belongs to pip.
Category 5 — Always required
Add these to every requirement list regardless of project:
codeplain CLI on PATH — codeplain --version.
CODEPLAIN_API_KEY env var set — printenv CODEPLAIN_API_KEY >/dev/null && echo set || echo missing (never echo the value).
A shell matching the testing scripts' extension (Bash for .sh, PowerShell for .ps1).
git (the renderer uses it; almost every plain project tracks itself in git).
Step 3 — Probe the host
For every requirement the project produced in Step 2, run a check using the terminal tool. Capture stdout/stderr and exit code. Classify each result:
PASS — present and (where a version was specified) at or above the required version.
WARN — present but at a version that doesn't match what the project explicitly asks for (e.g. spec says Java 17, host has Java 21), or service binary present but the service itself isn't running, or a Category 4 sub-layer is mismatched against another (e.g. CUDA toolkit version doesn't match what the framework expects).
FAIL — missing, unreachable, or below the minimum version.
Probe in the same category order Step 2 produced them, so the report reads top-down:
Category 1 — Language toolchains. Run the toolchain + package-manager probes listed in Step 2's Category 1 for the languages this project actually uses. Do not probe individual language packages.
Category 5 — Codeplain and git. Always required, independent of the project. codeplain --version, printenv CODEPLAIN_API_KEY >/dev/null, git --version. Verify the shell flavor matches the scripts' extension; a .sh-only project on native Windows is a FAIL (suggest WSL).
Category 2 — External services. For each service identified at runtime, probe the CLI's presence first, then — if the CLI exists — check whether the service itself is reachable. Service binary present but daemon down → WARN, not FAIL.
Category 3 — System binaries that language packages wrap. For each one identified in Step 2, check the system binary is on PATH (which <bin> / <bin> --version). The wrapper package itself isn't probed.
Category 4 — Hardware, drivers, accelerators. Walk the layered probe from Step 2 in order (driver → device visibility → SDK → acceleration libs → framework-sees-it → version match), reporting each layer's result separately. Never collapse a multi-layer failure into a single "GPU not available" message — each layer has a different fix.
Credentials and config holes. For every env var, dotfile, or cloud CLI login state the project implies, probe its presence without printing its value: printenv VAR >/dev/null && echo set || echo missing, [ -f <path> ] && echo present || echo missing, aws sts get-caller-identity / gcloud auth list / az account show for cloud SDKs.
Step 4 — Report
Emit a single report with the verdict on the first line so callers can pattern-match without parsing the body:
PASS — every required item is present at an acceptable version. Follow with a one-paragraph summary listing what was checked.
WARN — everything required is present but at least one item triggered a soft warning (version mismatch, service not currently running). Follow with the numbered list of warnings.
FAIL — at least one required item is missing or below minimum version. Follow with a numbered list of every gap.
For every FAIL and WARN item, the report must include four columns:
Column
Content
What
The missing tool/service/credential.
Why
Which spec, script, or library introduced this requirement (e.g. "backend/api.plain says using FastAPI", "test_scripts/run_unittests_python.sh calls pytest").
Status
"missing", "outdated (found X, need Y)", "binary present but service not running", or "env var not set".
Group the report into the same six probe groups used in Step 3 (toolchains, codeplain/git, services, system binaries, hardware, credentials), so the user can fix one whole layer at a time.
End the report with a one-liner reminder: Re-run check-plain-env after installing missing items to confirm.
Install suggestions
When emitting an install command, match the host OS detected in Step 1. Prefer the OS's first-party package manager. Don't suggest curl | sh style installs unless there is no alternative.
The tables below are a starter set of well-known install commands for the things projects most often need. They are not a list of "things to check" — that list is derived per project in Step 2. When the requirement list contains something not in these tables (e.g. a niche service, a specialty CLI), look up the canonical install command for the detected OS at the time of report generation rather than skipping the row or inventing a command.
macOS (Darwin)
Requirement
Suggested install
Python 3
brew install python@3.12
Node.js
brew install node (or brew install nvm for multi-version)
Go
brew install go
Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh (no Homebrew equivalent that stays current)
Java
brew install openjdk@21 then follow the post-install sudo ln -sfn ... step
Maven
brew install maven
Gradle
brew install gradle
PostgreSQL
brew install postgresql@16 then brew services start postgresql@16
Equivalent commands with sudo dnf install -y ... instead of apt. Surface them when the host is detected as Fedora/RHEL (e.g. presence of /etc/redhat-release or dnf --version succeeding).
Windows (native PowerShell)
Requirement
Suggested install
Python 3
winget install Python.Python.3.12 or choco install python
Node.js
winget install OpenJS.NodeJS.LTS or choco install nodejs-lts
If the project's test_scripts/ are .sh only and the host is native Windows, suggest WSL (wsl --install) rather than trying to make Bash scripts run under PowerShell.
When you can't pick
When the host OS detection in Step 1 was inconclusive (e.g. an unrecognized Linux distro), include the macOS and Debian/Ubuntu commands and tell the user to adapt for their package manager. Never invent install commands for a distro you can't identify.
Anti-patterns
(Hard mistake) Don't probe individual language packages. Anything pip install -r requirements.txt, npm ci, mvn install, cargo fetch, go mod download, bundle install, composer install, etc. would resolve is out of scope. torch, requests, numpy, FastAPI, express, react, JARs, gems, crates — all package-manager territory. Check the toolchain (python3, pip, node, npm, mvn, cargo, ...) and stop there. Probing the package itself creates false negatives the moment requirements.txt changes and duplicates work the test scripts will do anyway.
Don't stop after the directly named technologies. A spec that says "fine-tune on local hardware using PyTorch" never says "CUDA", but the workflow can't run without CUDA. Always work through Category 3 (system binaries that language packages wrap) and Category 4 (hardware/drivers) — not only the directly named runtimes. These categories are where the workflow's hidden requirements live.
Don't merge a chain of failures into one generic message. "GPU not available" is not a useful report. The chain is driver → device visibility → toolkit → acceleration libs → framework-sees-it → version match; each layer has a different fix. Surface each layer separately.
Don't work only from .plain files. Always cross-reference with test_scripts/ — the scripts are the executable contract. If a script invokes a non-language binary the specs never mention, that binary is automatically a Category 3 requirement.
Don't pre-bake a catalog. Derive the requirement list at runtime from the project in front of you. A hard-coded "things to always check" table becomes wrong the moment any project deviates from the assumed shape — and every project does.
Don't probe testing framework binaries as if they were independent.pytest, jest, vitest, phpunit, junit-console, etc. are installed by the package manager via requirements.txt / package.json / pom.xml. The toolchain check (Category 1) is enough — the framework binary itself is out of scope.
Don't probe in silence. Use the terminal tool and capture the actual command output (version strings, exit codes). Telling the user "looks like Python is installed" without running python3 --version is guessing.
Don't print secret values. Check whether CODEPLAIN_API_KEY, DATABASE_URL, etc. are set, not what they contain. Use printenv VAR >/dev/null not printenv VAR.
Don't install anything on the user's behalf. Even when the install command is obvious. The user needs to opt in.
Don't stop at the first FAIL. Run the full sweep so the report is complete in one pass.
Don't suggest curl | sh installs when a package manager works. Reserve the upstream installer fallback for cases where no package-manager option exists (e.g. rustup, sometimes Go).
Don't ignore version mismatches. If a spec says Java 17 and the host has Java 21, surface that as a WARN so the user can decide — don't silently let it pass.
Don't duplicate this check inline elsewhere. Other skills (forge-plain, add-feature, plain-healthcheck) that need an env check should delegate to check-plain-env rather than re-implementing the probe themselves. This skill is the single source of truth.
Validation Checklist
Host OS detected (uname -s / $OS) before any other action.
Requirement list derived at runtime from this project's.plain files, test_scripts/, config.yaml(s), and resources/ — no pre-baked catalog used.
No individual language packages probed. Only the toolchain + package manager for each detected language.
Every script under test_scripts/ opened and read; every non-language binary it invokes added to the Category 3 list.
Every external service named in the specs is in the Category 2 list, with both a binary probe and (when feasible) a reachability probe.
Every hardware / accelerator signal ("GPU", "fine-tune", "local inference", "CUDA", "MPS", etc.) triggered the layered Category 4 probe.
Every requirement probed via the terminal tool with an actual version / availability command (no "looks installed" guesses).
GPU / accelerator chains probed layer-by-layer (driver → device visibility → toolkit → acceleration libs → framework-sees-it → version match), with each layer reported separately.
Each result classified as PASS / WARN / FAIL.
Report has the verdict on the first line.
Every FAIL and WARN row has all four columns: What / Why / Status / How to install.
Install suggestions match the detected host OS (no Debian commands on macOS, no brew on Linux, etc.).
No secret values were printed at any point.
Nothing was installed; no project files were modified.
