dojo.md
University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
Verify and write proofs using the Acorn theorem prover for mathematical and cryptographic formalization. Use when working with Acorn proof files (.ac), verifying theorems, formalizing mathematical or cryptographic protocols, or writing proofs in the Acorn language. Triggers on: (1) Creating or editing .ac files, (2) Running acorn verify commands, (3) Formalizing math or crypto proofs, (4) Questions about Acorn syntax or standard library.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install flyingnobita/acorn-proverInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
If config.env does not exist in the skill directory:
Ask the user for the following paths:
ACORN_LIB - Path to acornlib (e.g., /path/to/acornprover/acornlib)ACORN_PROJECT - Path to project directory for .ac files (e.g., /path/to/acorn-playground)Verify the paths exist using list_dir or equivalent. If a path is invalid, inform the user and ask again.
Run setup.sh with the validated paths:
bash skills/acorn-prover/scripts/setup.sh "<ACORN_LIB>" "<ACORN_PROJECT>"
ACORN_LIB, ACORN_PROJECT, and USE_MISE variables:source skills/acorn-prover/config.env
If any of the above are blank / not set, inform the user to set the variable manually. If any of the above are changed, ask the user for new paths and run setup again.
Config values are stored in skills/acorn-prover/config.env:
| Variable | Description |
|---|---|
ACORN_LIB |
Path to acornlib |
ACORN_PROJECT |
Project directory for .ac files |
USE_MISE |
true if mise is available |
If USE_MISE=true:
mise run acorn verify <filename>.ac
Otherwise, use the direct CLI:
acorn --lib "$ACORN_LIB" verify <filename>.ac
Check that all proofs are cached with no AI searches required:
# With mise
mise run acorn reverify
# Or direct CLI
acorn --lib "$ACORN_LIB" reverify
Use for CI pipelines to ensure all proofs are complete.
Generate training data (problem-proof pairs) for AI model development:
# With mise
mise run acorn training ./training_data
# Or direct CLI
acorn --lib "$ACORN_LIB" training ./training_data
Argument: DIR - Directory to output training data.
Genera...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Acorn Prover ## Setup (MUST DO WHEN RUNNING FIRST TIME) If `config.env` does not exist in the skill directory: 1. **Ask the user** for the following paths: - `ACORN_LIB` - Path to acornlib (e.g., `/path/to/acornprover/acornlib`) - `ACORN_PROJECT` - Path to project directory for `.ac` files (e.g., `/path/to/acorn-playground`) 2. **Verify** the paths exist using `list_dir` or equivalent. If a path is invalid, inform the user and ask again. 3. **Run setup.sh** with the validated paths: ```bash bash skills/acorn-prover/scripts/setup.sh "<ACORN_LIB>" "<ACORN_PROJECT>" ``` 4. **Source the config** to get `ACORN_LIB`, `ACORN_PROJECT`, and `USE_MISE` variables: ```bash source skills/acorn-prover/config.env ``` If any of the above are blank / not set, inform the user to set the variable manually. If any of the above are changed, ask the user for new paths and run setup again. ## Configuration Config values are stored in `skills/acorn-prover/config.env`: | Variable | Description | | --------------- | ------------------------------- | | `ACORN_LIB` | Path to acornlib | | `ACORN_PROJECT` | Project directory for .ac files | | `USE_MISE` | `true` if mise is available | ## Verify Proofs If `USE_MISE=true`: ```bash mise run acorn verify <filename>.ac ``` Otherwise, use the direct CLI: ```bash acorn --lib "$ACORN_LIB" verify <filename>.ac ``` ## Reverify Proofs (CI/CD) Check that all proofs are cached with no AI searches required: ```bash # With mise mise run acorn reverify # Or direct CLI acorn --lib "$ACORN_LIB" reverify ``` Use for CI pipelines to ensure all proofs are complete. ## Training Data Generation Generate training data (problem-proof pairs) for AI model development: ```bash # With mise mise run acorn training ./training_data # Or direct CLI acorn --lib "$ACORN_LIB" training ./training_data ``` Argument: `DIR` - Directory to output training data. ## Documentation Generation Genera...
University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
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