huggingface/upskill

Dernière activité source enregistrée: 23 mars 2026 à 16:51

Catalogue SkillsMP mis à jour: 5 novembre 2026 à 19:57

skills collectés: 1

Étoiles GitHub: 761
Forks GitHub: 92

Installer avec un assistant IA

Copiez ce prompt dans l’assistant IA que vous utilisez.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/huggingface/upskill.

README du dépôt

Fichier source du README : README.md

Lire le README source

Afficher le README complet

<img width="1920" height="1080" alt="upskill_banner" src="https://github.com/user-attachments/assets/b71fd417-7d23-4f5d-aa89-06ea6b284d1b" /> # UPskill Generate and evaluate agent skills based on traces with agents. Create skills with teacher models (expensive/slow) that student models (cheap/fast) can use to perform harder tasks reliably. > [!TIP] > > UPskill v2 - recommended default config file now runs evaluations on Hugging Face Jobs. Make sure > to set your `HF_TOKEN` and use `--artifact-repo <dataset-name>` for job creation and result capture ## Quick Start Install upskill: ```bash uv pip install upskill # or just use uv uvx upskill ``` Create a new skill ```bash upskill generate "write good git commit messages" # or based on previous agent traces upskill generate "document the pattern" --from ./trace.md # Skills are saved to ./skills/{skill-name}/ by default ``` Generate a skill with a teaching model and evaluate it on a student model. ```bash upskill generate "write good git commit messages" --model sonnet --eval-model haiku ``` Benchmark a set of models against a skill. ```bash upskill eval ./skills/git-commit-messages/ -m haiku -m sonnet # logs pretty printed to the terminal ``` View the results later. ```bash upskill runs --skill git-commit-messages ``` ## Development checks This repo uses a CI flow inspired by `fast-agent` with separate format, lint, typecheck, and test stages. Install dev dependencies: ```bash uv sync --extra dev ``` Run the quality gates locally: ```bash uv run scripts/format.py uv run scripts/lint.py uv run scripts/typecheck.py uv run scripts/cpd.py --check uv run --extra dev pytest -v ``` Or use the helper script to run the whole sequence: ```bash uv run scripts/check.py ``` Add `--sync` to include `uv sync --extra dev`, or `--skip-tests` for a faster static-only pass. To auto-format before re-running checks: ```bash uv run --extra dev scripts/format.py --write ``` Current enforced standards: - `ruff format --check` for formatting - `ruff check` for style, imports, modernization, bugbear, simplify, and import-hygiene rules - cyclomatic complexity via Ruff `C90` with `max-complexity = 15` - `ty check` across `src`, `tests`, and `scripts` - `pmd cpd` via `scripts/cpd.py --check` to flag duplicated code in `src/` - `pytest` for the test suite CI enforcement lives in `.github/workflows/ci.yml` and runs on pushes and pull requests targeting `main`. ## Model Handling Overview upskill uses distinct phases with explicit model roles: - **Skill generation**: create/refine `SKILL.md` - **Test generation**: create synthetic evaluation cases - **Evaluation**: run tests against evaluator model(s) - **Benchmark**: repeated evaluation across multiple runs/models Model flags by command: | Command | Flag | Meaning | |---|---|---| | `generate` | `--model` | Skill generation/refinement model | | `generate` | `--test-gen-model` | Test generation model override | | `generate` | `--eval-model` | Optional extra cross-model eval pass | | `eval` | `-m/--model` | Evaluation model(s) (repeatable) | | `eval` | `--test-gen-model` | Test generation model override (when tests are generated) | | `benchmark` | `-m/--model` | Evaluation model(s) to benchmark | | `benchmark` | `--test-gen-model` | Test generation model override (when tests are generated) | | `runs` / `plot` | `-m/--model` | Historical results filter only | `upskill eval` enters **benchmark mode** whenever you pass multiple `-m` values or `--runs > 1`. In benchmark mode, baseline comparison is always off; `--no-baseline` is redundant. ## Commands ### `upskill generate` Generate a skill from a task description with automatic evaluation and refinement. ```bash upskill generate TASK [OPTIONS] ``` **Arguments:** - `TASK` - Description of what the skill should teach **Options:** - `-e, --example` - Input -> output example (can be repeated) - `-f, --from PATH` - Improve from existing skill dir or agent trace file (auto-detected) - `-m, --model MODEL` - Skill generation model (e.g., 'sonnet', 'haiku', 'anthropic.claude-sonnet-4-20250514') - `--test-gen-model MODEL` - Override test generation model for this run - `-o, --output PATH` - Output directory for skill - `--no-eval` - Skip evaluation and refinement - `--eval-model MODEL` - Different model to evaluate skill on - `--executor [local|jobs]` - Execution backend for evaluation/refinement; overrides config - `--artifact-repo TEXT` - Dataset repo for remote fast-agent job artifacts (required with `--executor jobs`) - `--max-parallel N` - Max concurrent evaluation executions; overrides config - `--runs-dir PATH` - Directory for run logs (default: ./runs) - `--log-runs / --no-log-runs` - Log run data (default: enabled) **Examples:** ```bash # Basic usage upskill generate "parse JSON Schema files" # Make and evaluate skills for less powerful models upskill generate "write git commits" --model sonnet --eval-model haiku # Remote execution on Hugging Face Jobs upskill generate "parse invoices" --executor jobs --artifact-repo <user>/upskill-tests # Improve an existing skill (auto-detected as directory) upskill generate "add more error handling examples" --from ./skills/api-errors/ # Generate from an agent trace file (auto-detected as file) upskill generate "document the pattern" --from ./trace.json # Skip evaluation during generation (evaluate separately with upskill eval) upskill generate "parse YAML" --no-eval ``` **Output:** ``` Generating skill with sonnet... Generating test cases... Evaluating on sonnet... (attempt 1) 60% -> 100% (+40%) OK git-commit-messages Write clear, conventional commit messages that follow best practices. SKILL.md ~450 tokens baseline ████████████░░░░░░░░ 60% with skill ████████████████████ 100% (+40%) tokens: 1200 → 800 (-33%) Saved to ./skills/git-commit-messages ``` ### `upskill eval` Evaluate an existing skill against test cases. Supports single-model evaluation with baseline comparison, or multi-model benchmarking. ```bash upskill eval SKILL_PATH [OPTIONS] ``` **Arguments:** - `SKILL_PATH` - Path to skill directory containing SKILL.md **Options:** - `-t, --tests PATH` - Test cases JSON file - `-m, --model MODEL` - Model(s) to evaluate against (repeatable for multi-model benchmarking) - `--test-gen-model MODEL` - Override test generation model when tests must be generated - `--runs N` - Number of runs per model; overrides config - `--no-baseline` - Skip baseline comparison (simple eval mode only; ignored in benchmark mode) - `-v, --verbose` - Show per-test results - `--executor [local|jobs]` - Execution backend for evaluation; overrides config - `--max-parallel N` - Max concurrent evaluation executions; overrides config - `--log-runs / --no-log-runs` - Log run data (default: enabled) - `--runs-dir PATH` - Directory for run logs **Examples:** ```bash # Basic evaluation with baseline comparison upskill eval ./skills/my-skill/ # With verbose output upskill eval ./skills/my-skill/ -v # Custom test cases upskill eval ./skills/my-skill/ --tests ./tests.json # Evaluate on specific model upskill eval ./skills/my-skill/ -m haiku # Multi-model benchmarking (compare models) upskill eval ./skills/my-skill/ -m haiku -m sonnet # Multiple runs per model for statistical significance upskill eval ./skills/my-skill/ -m haiku -m sonnet --runs 5 # Evaluate a local model configured in fast-agent upskill eval ./skills/my-skill/ -m generic.my-model # Skip baseline (just test with skill) upskill eval ./skills/my-skill/ --no-baseline # Benchmark mode is triggered by multiple models OR --runs > 1 upskill eval ./skills/my-skill/ -m haiku --runs 5 # Disable run logging upskill eval ./skills/my-skill/ --no-log-runs ``` **Benchmark output:** ``` Evaluating my-skill across 2 model(s) 3 test case(s), 5 run(s) per model haiku Pass rate: 4/5 (80%) Avg assertions: 2.8/3 sonnet Pass rate: 5/5 (100%) Avg assertions: 3.0/3 ┏━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Model ┃ Pass Rate ┃ Avg Assertions ┃ Avg Tokens ┃ ┡━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ haiku │ 4/5 │ 2.8/3 │ 1250 │ │ sonnet │ 5/5 │ 3.0/3 │ 1890 │ └────────┴───────────┴────────────────┴────────────┘ ``` **Test cases JSON format:** ```json [ {"input": "Write a commit for adding login", "expected": {"contains": ["feat", "login"]}}, {"input": "Fix the null pointer bug", "expected": {"contains": ["fix", "bug"]}} ] ``` ### `upskill list` List all generated skills in a tree view. ```bash upskill list [OPTIONS] ``` **Options:** - `-d, --dir PATH` - Skills directory to list - `-v, --verbose` - Show skill contents preview **Examples:** ```bash # List skills in default directory upskill list # List from custom directory upskill list -d ./my-skills/ # Show preview of skill contents upskill list -v ``` **Output:** ``` ./skills ├── git-commit-messages │ ├── Write clear, conventional commit messages... │ └── files │ └── SKILL.md ├── api-error-handling │ ├── Handle API errors gracefully with proper logging... │ └── files │ ├── SKILL.md │ └── references/error-codes.md └── yaml-parsing ├── Parse YAML files safely with schema validation... └── files ├── SKILL.md └── scripts/validate.py ``` ### `upskill runs` View run results as a plot, or export to CSV. By default, shows a visual comparison of baseline vs with-skill performance. ```bash upskill runs [OPTIONS] ``` **Options:** - `-d, --dir PATH` - Runs directory - `-s, --skill TEXT` - Filter by skill name(s) (repeatable) - `-m, --model TEXT` - Filter historical run data by model(s) (repeatable) - `--metric [success|tokens]` - Metric to display (default: success) - `--csv PATH` - Export to CSV instead of plot **Examples:** ```bash # View results plot (default) upskill runs # Filter by skill and models upskill runs -s my-skill -m haiku -m sonnet # Show token usage instead of success rate upskill runs --metric tokens # Export to CSV upskill runs --csv ./results.csv # Custom runs directory upskill runs -d ./my-runs/ ``` **Plot output:** ``` skill: git-commit-messages haiku baseline ████████████░░░░░░░░ 60% with skill ████████████████░░░░ 80% (+20%) sonnet baseline ████████████░░░░░░░░ 60% with skill ████████████████████ 100% (+40%) ``` **Matrix view (multiple skills and models):** ``` ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓ ┃ skill ┃ haiku ┃ sonnet ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩ │ git-commit-messages │ 60%→80% │ 60%→100% │ │ api-error-handling │ 40%→70% │ 50%→90% │ │ yaml-parsing │ 70%→90% │ 80%→100% │ └─────────────────────┴──────────────┴──────────────┘ ``` ## Skill Output Format Skills are saved in a standard directory format: ``` ./skills/{skill-name}/ ├── SKILL.md # Main skill instructions ├── references/ # Supporting documents (optional) └── scripts/ # Executable scripts (optional) ``` **Example SKILL.md:** ```markdown # git-commit-messages Write clear, conventional commit messages that follow best practices. ## Instructions This skill teaches how to write effective git commit messages following the Conventional Commits specification. ## Format Commit messages should follow this structure: <type>(<scope>): <subject> <body> <footer> ## Types - `feat`: New feature - `fix`: Bug fix - `docs`: Documentation changes ... ## Examples ### Simple feature commit feat(auth): add password reset functionality ### Bug fix with explanation fix(api): handle null response from user service The user service can return null when not found. Added proper null checking to prevent crashes. Closes #123 ``` ## Run Logging By default, upskill logs all runs to `./runs/`. Each run creates: ``` ./runs/ ├── 2025_01_21_15_30/ # Batch folder (timestamp) │ ├── run_1/ │ │ ├── run_metadata.json # Model, task, timing │ │ └── run_result.json # Pass/fail, assertions, tokens │ ├── run_2/ │ │ └── ... │ └── batch_summary.json # Aggregate results └── results.csv # Summary CSV (after `upskill runs`) ``` Disable with `--no-log-runs`. ## Configuration ### upskill config (`./upskill.config.yaml`) ```yaml skill_generation_model: sonnet # Default skill generation model eval_model: haiku # Default evaluation model (optional) test_gen_model: null # Optional test generation model skills_dir: ./skills # Where to save skills runs_dir: ./runs # Where to save run logs max_refine_attempts: 2 # Refinement iterations executor: local # Default execution backend num_runs: 1 # Default eval/benchmark runs when --runs is omitted max_parallel: 5 # Default concurrent evaluation executions jobs_secrets: HF_TOKEN # Comma-separated HF Jobs env var names to forward jobs_image: ghcr.io/astral-sh/uv:python3.13-bookworm # HF Jobs container image # fastagent_config: ./fastagent.config.yaml # Optional FastAgent config override ``` `test_gen_model` fallback behavior: - CLI `--test-gen-model` overrides config for a single run. - If set, test generation uses `test_gen_model`. - If unset, test generation falls back to `skill_generation_model`. - For `eval`/`benchmark`, this intentionally uses `skill_generation_model` (not `eval_model`) so generated tests stay stable when sweeping multiple evaluation models. Backward compatibility: `model` is still accepted in config files as a legacy alias for `skill_generation_model`. CLI flags override config values for execution settings: - `--executor` overrides `executor` - `--runs` overrides `num_runs` - `--max-parallel` overrides `max_parallel` - `--jobs-secrets` overrides `jobs_secrets` If you set `executor: jobs`, you still need the required jobs-specific CLI inputs such as `--artifact-repo`. `jobs_secrets` is a comma-separated list of environment variable names to forward into remote HF Jobs runs. It should contain secret names such as `HF_TOKEN` or `ANTHROPIC_API_KEY`, not literal secret values. `jobs_image` controls which container image HF Jobs uses for remote execution. Config lookup order: 1. `UPSKILL_CONFIG` environment variable (path) 2. `./upskill.config.yaml` (project local) 3. `~/.config/upskill/config.yaml` (legacy fallback) ### FastAgent config (`fastagent.config.yaml`) Place in your project directory to customize FastAgent settings: ```yaml default_model: sonnet logger: progress_display: true show_chat: false streaming: markdown # MCP servers (optional) mcp: servers: fetch: command: "uvx" args: ["mcp-server-fetch"] ``` ## Environment Variables ```bash # Required for Anthropic models ANTHROPIC_API_KEY=sk-ant-... # Required for OpenAI models OPENAI_API_KEY=sk-... # Optional: custom endpoints ANTHROPIC_BASE_URL=http://localhost:8080 OPENAI_API_BASE=http://localhost:11434/v1 # For local models (generic provider) GENERIC_BASE_URL=http://localhost:8080/v1 GENERIC_API_KEY=local # Optional, defaults to "local" ``` ## Python API ```python from upskill import ( generate_skill, generate_tests, evaluate_skill, refine_skill, Config, ) # Load configuration config = Config.load() # Generate a skill skill = await generate_skill( "parse JSON Schema files", model="sonnet", config=config, ) # Generate test cases tests = await generate_tests("parse JSON Schema files") # Evaluate the skill results = await evaluate_skill( skill, tests, model="haiku", config=config, ) print(f"Skill lift: {results.skill_lift:.0%}") print(f"Token savings: {results.token_savings:.0%}") print(f"Is beneficial: {results.is_beneficial}") # Refine based on failures if not results.is_beneficial: from upskill.evaluate import get_failure_descriptions failures = get_failure_descriptions(results) improved_skill = await refine_skill(skill, failures) ``` ## Model Format upskill uses FastAgent model format: ``` <provider>.<model>.<reasoning_effort?> ``` **Examples:** - `sonnet` - Anthropic Claude Sonnet (alias) - `haiku` - Anthropic Claude Haiku (alias) - `opus` - Anthropic Claude Opus (alias) - `anthropic.claude-sonnet-4-20250514` - Full model name - `openai.gpt-4.1` - OpenAI GPT-4.1 - `openai.o3-mini.low` - OpenAI o3-mini with low reasoning effort - `generic.llama3.2:latest` - Local model via Ollama - `generic.my-model` - Local model via llama.cpp or other OpenAI-compatible server ## Local Models upskill supports local models through any OpenAI-compatible endpoint (llama.cpp, vLLM, etc.). **With llama.cpp server:** ```bash # Start llama.cpp server ./llama-server -m model.gguf --port 8080 # Configure endpoint via fast-agent config/env, then evaluate upskill eval ./skills/my-skill/ --model generic.my-model ```

Chargement du README du dépôt…

Skills dans ce dépôt

Développeurs de logiciels

1 catégories métier · 100% classifié

skill

métier

description

mis à jour

example-skill

Développeurs de logiciels

Example fixture skill for action smoke tests

Langue du texte source : Indéterminée

2026-03-23

Affichage de 1 skills collectés sur 1.