Skilldo

Skilldo CLI

Skilldo is a 7-stage LLM pipeline that generates SKILL.md files for software libraries: extract → map → learn → facts → create → review → test

Stages 1-3 gather library metadata (source files, docs, dependencies, version). File collection respects .gitignore — gitignored files are excluded from LLM context. Stage 4 (facts) extracts a compact truth table with negative assertions from stages 1-3. Stage 5 generates the SKILL.md. Stage 6 reviews for accuracy/safety. Stage 7 validates with generated test code.

Installation

# Homebrew
brew install skilldoai/tap/skilldo

# From source
cargo install --path .

Quick Start

# Generate a SKILL.md for the current repo
skilldo generate

# Generate for a specific repo path
skilldo generate /path/to/library

# Specify language explicitly
skilldo generate --language python

# Use a specific config
skilldo generate --config skilldo.toml

Configuration (skilldo.toml)

Skilldo uses TOML config files. Place skilldo.toml in the repo root, or pass --config.

Minimal config (single provider)

[llm]
provider_type = "anthropic"          # anthropic, openai, gemini, openai-compatible, chatgpt, cli
model = "claude-sonnet-4-6"
api_key_env = "ANTHROPIC_API_KEY"  # env var name containing the key

[generation]
max_retries = 10                # retry on lint/test failures
# telemetry = true              # opt-in: log runs to ~/.skilldo/runs.csv

Per-stage model overrides

Different models can be used for individual stages:

[llm]
provider_type = "anthropic"
model = "claude-sonnet-4-6"
api_key_env = "ANTHROPIC_API_KEY"

[generation]
enable_test = true
enable_review = true
test_mode = "thorough"          # thorough, quick, adaptive, minimal

[generation.review_llm]
provider_type = "openai"
model = "gpt-5.2"
api_key_env = "OPENAI_API_KEY"

[generation.test_llm]
provider_type = "openai"
model = "gpt-5.2"
api_key_env = "OPENAI_API_KEY"

# Also available: extract_llm, map_llm, learn_llm, fact_llm, create_llm

Local models (Ollama)

[llm]
provider_type = "openai-compatible"
model = "qwen3-coder:latest"
base_url = "http://localhost:11434/v1"
api_key_env = "none"            # Ollama doesn't need a key

CLI provider (use existing CLI tools as LLM backend)

[llm]
provider_type = "cli"
model = "claude-sonnet-4-6"
cli_command = "claude"
cli_args = ["-p", "--no-session-persistence", "--output-format", "text", "--dangerously-skip-permissions"]
request_timeout_secs = 900

Supported CLI tools: claude, codex, gemini. The prompt is piped via stdin; response captured from stdout.

Optional: declare how the CLI accepts system prompts natively:

cli_system_args = ["--system-prompt-file"]  # for claude (reads from temp file)
# cli_system_args = ["-s"]            # for codex

When cli_system_args is set, instructions go through the native system channel instead of being concatenated into stdin.

Custom base URL (Bedrock, Vertex AI, proxies)

All providers support optional base_url for custom endpoints:

# Anthropic via AWS Bedrock
[llm]
provider_type = "anthropic"
base_url = "https://bedrock-runtime.us-east-1.amazonaws.com"

# Gemini via Google Vertex AI
[llm]
provider_type = "gemini"
base_url = "https://us-central1-aiplatform.googleapis.com"

Container settings (optional)

[generation.container]
runtime = "podman"              # docker or podman
timeout = 120                   # seconds
cleanup = true
# setup_commands = ["apt-get update && apt-get install -y cmake"]  # run before deps/tests

Commands

skilldo generate [PATH]

Generate a SKILL.md for a library repository.

Key flags:

• --language <LANG> — force language (python, javascript, rust, go, java). Auto-detected if omitted
• --config <PATH> — config file path (defaults to ./skilldo.toml or ~/.config/skilldo/config.toml)
• --model <MODEL> — override LLM model
• --provider <PROVIDER> — LLM provider: anthropic, openai, chatgpt, gemini, openai-compatible
• --base-url <URL> — base URL for openai-compatible providers
• -i, --input <PATH> — existing SKILL.md to use as reference for updates
• -o <PATH> — output file (default: SKILL.md)
• --debug-stage-files <DIR> — dump each pipeline stage's raw output for debugging
• --replay-from <DIR> — load cached extract/map/learn outputs from a prior --debug-stage-files run, skipping those LLM calls. Reduces iteration time from ~15 min to ~5 min
• --no-test — skip test validation
• --no-review — skip review validation
• --no-security-scan — skip YARA/unicode/injection scanning
• --no-parallel — run extract/map/learn sequentially (for local models)
• --best-effort — exit 0 even with errors
• --telemetry / --no-telemetry — toggle run logging
• --container — run test agent in container (default: bare-metal with uv/cargo)
• --install-source <MODE> — how the test agent installs the library for code validation: registry (from crates.io/PyPI/npm, default), local-install (mounts local repo and builds via package manager), local-mount (mounts repo and sets module path directly, no build step)
• --source-path <PATH> — local source path for local-install/local-mount modes
• --test-mode <MODE> — thorough tests ALL patterns (default), quick tests up to 3 for fast iteration, minimal tests 1, adaptive starts with 1 and expands
• --review-model <MODEL> / --review-provider <PROVIDER> — override model/provider for the review stage only
• --test-model <MODEL> / --test-provider <PROVIDER> — override model/provider for the test code generation stage only
• --max-retries <N> — max create→validate retries before giving up (default: from config)
• --skill-version <VER> — force a specific library version in frontmatter (e.g., "2.1.0") instead of auto-detecting
• --version-from <STRATEGY> — how to detect version: package (Cargo.toml/pyproject.toml), git-tag, branch, commit
• -q, --quiet — suppress informational output (only warnings/errors)
• -v, --verbose — show detailed debug output (equivalent to RUST_LOG=debug)
• --request-timeout <SECS> — override LLM request timeout in seconds (default: 120)
• --dry-run — use mock LLM client for testing (no API calls)

skilldo lint <PATH>

Lint a SKILL.md for structural errors (frontmatter, sections, code blocks).

skilldo review <PATH>

LLM-powered review of an existing SKILL.md for accuracy and safety.

Key flags:

• --config <PATH> — config with LLM settings

skilldo config check --config <PATH>

Validate a config file for correctness.

skilldo auth login|status|logout

Manage OAuth tokens for providers that use OAuth (e.g., ChatGPT).

Supported Languages

Configuration (skilldo.toml)

Key [generation] fields:

Model Communication

The model reports uncertainty via HTML comments in the output (stripped before final SKILL.md):

• <!-- SKILLDO-CONFLICT: description --> — docs vs code conflicts found
• <!-- SKILLDO-UNVERIFIED: description --> — APIs the model couldn't verify from source

View these with RUST_LOG=info or RUST_LOG=debug. CONFLICT logs at info, UNVERIFIED at warn.

Common Workflows

Generate with Anthropic

source ~/.anthropic
skilldo generate --provider anthropic --model claude-sonnet-4-6

Generate with OpenAI

source ~/.openai
skilldo generate --provider openai --model gpt-5.2

Generate with local Ollama

skilldo generate --provider openai-compatible --model qwen3-coder:latest --base-url http://localhost:11434/v1

Batch generation (multiple repos)

for repo in /path/to/repos/*/; do
  skilldo generate "$repo" --config shared-config.toml --best-effort
done

Generate for an API client SDK

# skilldo.toml — for libraries that discuss API keys/auth
[generation]
security_context = "api-client"
redact_env_vars = ["MY_API_KEY"]

export MY_API_KEY="..."  # needed for test validation
skilldo generate . --config skilldo.toml

Fast prompt iteration with replay

# Capture cache once
skilldo generate /path/to/lib --debug-stage-files /tmp/cache -o /tmp/baseline.md

# Iterate on prompts (only create stage runs, ~5 min vs ~15 min)
skilldo generate /path/to/lib --replay-from /tmp/cache -o /tmp/variant.md

Debug pipeline stages

skilldo generate . --debug-stage-files ./debug-output/
# Writes: 1-extract.md, 2-map.md, 3-learn.md, 4-create-raw.md, 4-create-system.md, facts.md,
#         5-review-*.txt, 6-normalized.md

Review an existing SKILL.md

skilldo review SKILL.md --config skilldo.toml

Telemetry

When telemetry = true, each run appends a row to ~/.skilldo/runs.csv:

• Timestamp, language, library name, version
• Models and providers used (generate, review, test)
• Pass/fail, retry count, duration, failure details

Data is local only — nothing is sent anywhere.

Troubleshooting

• "No test files found" — a warning, not an error. The pipeline continues without test-derived patterns. Use --language to override if detection is wrong.
• Test failures looping — try --test-mode minimal or --no-test for a first pass.
• Rate limited — increase retry_delay in config, or switch to a local model.
• OAuth errors — run skilldo auth status --config <path> to check token state.
• Container local-install on non-Python — falls back to bare-metal execution with a warning; local-mount works in-container for all languages, and bare-metal local-install works for all 5 languages.

Documentation

Full docs — configuration, languages, architecture, authentication, best practices, and telemetry: docs/