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Installing Skill3

Requirements, build, and the one-time setup for SkillSpector and a Brave Search key. For what to run once it is installed, see USAGE.md.

Requirements

  • JDK 25 (compiled with --release 25). Gradle provisions the JDK 25 toolchain automatically (auto-detected or downloaded via the Foojay resolver), so you don't need JDK 25 on JAVA_HOME — any JDK that runs Gradle will do.
  • A local LLM exposed over an OpenAI-compatible API (e.g. Ollama).
  • Python 3.12–3.14 (only for setup; SkillSpector's supported range).
  • A Brave Search API key for discovery.

Build

./gradlew build        # compile + full quality gate (analysis) + tests
./gradlew test         # tests only (JUnit + ArchUnit)
./gradlew run --args="..."   # run the CLI

build runs the complete quality gate — see Development.

Setup

1. Install SkillSpector (one-time)

./gradlew run --args="setup"

This provisions a local Python venv and installs SkillSpector into it. learn runs SkillSpector with --no-llm so vetting stays fully local (static analysis only).

2. Get a Brave Search key

Discovery uses the Brave Search API — the only external service learn needs.

  1. Create an account at https://brave.com/search/api/.
  2. Subscribe to a plan. The Free tier (a few thousand queries/month) is enough to try Skill3; a card may be required for verification even on the free plan.
  3. Create a subscription token (your API key).
  4. Provide it one of two ways:
# Option A — environment variable (picked up automatically)
export BRAVE_SEARCH_API_KEY="your-token"

# Option B — per run
./gradlew run --args="learn mcp --llm-model qwen2.5-coder:7b --brave-key your-token"

The token is sent in the X-Subscription-Token header. If no key is found, learn stops early with a clear message; the key is treated as a secret (@AIPrivacy — never logged).

You don't strictly need a key to evaluate the pipeline: the example in examples/ was produced from seeded source URLs, and the tests stub discovery behind the SearchClient interface. For a real run with no key and no network at all, supply your own sources with --input-file (see Offline discovery).

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