skill-to-sdk

# Skill to SDK Convert an Anthropic Agent Skill into a self-contained Python program that loads the skill's instructions as a system prompt and runs them against the Claude API. The output is a small command-line app the user runs with `python app.py "their question"` (or interactively) that prints the answer to the console. ## Why this exists A skill normally lives inside Claude Code / Claude.ai and triggers when its description matches. People often want to take a finished skill and run it as an ordinary program — to share it, schedule it, embed it, or call it from other code. This skill produces that program. The skill's instruction body becomes the **system prompt**; the user's question becomes the user message; the model's reply is printed to the console. The wrapper is deliberately simple: one API call per turn, text in and text out, no tools. That is exactly what an instruction-only skill needs — a skill that just tells the model how to think, reason, format, or respond reproduces faithfully this way (e.g. `market-sizing`, `okr-writer`, `exec-summary`). ## Input: two cases The user will either hand you a SKILL.md or just describe a skill. Detect which and proceed accordingly. 1. **They provide a SKILL.md** (a file, a path, or pasted text): use it directly. Strip the YAML frontmatter — only the markdown body becomes the system prompt. Keep the `name` from the frontmatter to name the output folder and files. 2. **They only give a name or description** (e.g. "a skill that summarizes legal contracts"): first write a short, well-formed SKILL.md for it yourself — frontmatter with `name` and `description`, then a clear instruction body in the imperative voice describing how the assistant should behave. Show it to the user briefly, then wrap it. Keep it focused; a single-purpose instruction set is better than a sprawling one. If anything essential is ambiguous (what the skill should actually do, what its name is), ask one concise question before generating. Otherwise proceed. ## What to generate Create a folder named after the skill (kebab-case, from the skill's `name`), containing exactly these files. Use the templates in `assets/` as the starting point and fill in the skill-specific pieces. ``` <skill-name>-sdk/ ├── skill/ │ └── SKILL.md # the source skill (provided or generated) ├── app.py # CLI: loads SKILL.md, calls the API, prints the reply ├── requirements.txt # anthropic, python-dotenv ├── .env.example # ANTHROPIC_API_KEY placeholder └── README.md # setup + run instructions, tailored to this skill ``` Copy `assets/app_template.py` to `app.py` **verbatim** — all the skill-specific content lives in `skill/SKILL.md`, so the runner itself never needs to change. Copy `assets/requirements.txt` and `assets/.env.example` as-is. Only edit `app.py` if the user explicitly asks for behavior the template doesn't cover (a different model, JSON output, or streaming). ### What app.py does The template runner: - Loads `skill/SKILL.md`, strips the frontmatter (everything up to the second `---`), and uses the remaining body as the `system` prompt. - Handles input in three modes, in this order: (1) a command-line argument (`python app.py "question"`) runs one-shot; (2) if there's no argument but input is piped in and there's no interactive terminal (`sys.stdin.isatty()` is false), it reads all of stdin as a single question — this is what makes `echo "..." | python app.py` and CI/non-interactive shells work; (3) only when a real terminal is attached, it drops into an interactive loop that keeps conversation history. This ordering matters: never enter the `input()` loop without a TTY, or it exits immediately and no conversation happens. When there's no argument, no TTY, and no piped input, it prints clear usage rather than silently doing nothing. - Calls `client.messages.create` with `model="claude-sonnet-4-6"`, `max_tokens=2000`, the system prompt, and the message history. - Extracts the text blocks from the response and prints them to the console. - Fails clearly if `ANTHROPIC_API_KEY` is missing, pointing the user to `.env.example`. ### README.md Base it on `assets/README_template.md`, then customize the title and the one-line description to match the specific skill, and add 2-3 realistic example invocations relevant to what this skill does. Keep the "Notes & limits" section — it sets correct expectations about what a text-only wrapper can do. ## Scope: instruction-only skills This wrapper is a plain text-in / text-out API call. It cannot run bundled scripts, read local files, or execute bash — so it reproduces **instruction-only** skills, the ones that just guide how the model thinks, reasons, formats, or responds. That covers the large majority of skills. If the source skill genuinely needs to *act* — write files, read files, or run shell commands as part of its core job — say so plainly. The text-only wrapper will reproduce the skill's reasoning and describe the actions, but it won't perform them; making those real would require adding tool use (function calling) to `app.py`, which is out of scope here. Never silently imply a file-producing skill will write files when it won't. ## Steps 1. Determine the input case (provided SKILL.md vs. name/description). If the latter, draft the SKILL.md and show it. 2. Note for the user whether the skill is fully instruction-only (reproduces faithfully) or relies on actions the text-only wrapper can't perform. 3. Create the folder structure from the `assets/` templates. 4. Write the source skill into `skill/SKILL.md`. 5. Tailor `README.md` (title, description, examples) to this skill. 6. If a file-creation/output mechanism is available, save everything to the outputs location, zip it, and present it for download. Otherwise show the files inline. 7. Give the user the run instructions: create and activate a virtual environment (`python -m venv .venv` then `source .venv/bin/activate`, or `.venv\Scripts\activate` on Windows), install deps, copy `.env.example` to `.env` and add their key, then `python app.py "a question"`. ## Quick sanity check before delivering - Frontmatter stripping works (the system prompt should not start with `---`). - The folder name and the skill `name` agree. - `requirements.txt` lists `anthropic` and `python-dotenv`. - The README's examples actually match what the skill does. - You told the user whether the skill is fully instruction-only.