@intentsolutions/jrig-cli

The J-Rig seven-layer binary evaluation CLI for Claude Skills (SKILL.md artifacts) — published as a self-contained npm package so any repo can install the j-rig command and gate skill changes in CI.

Scores every skill change across package integrity, trigger quality, functional quality, regression protection, baseline value, model variance, and rollout safety — all binary yes/no criteria with an evaluator that is always separate from the skill under test.

The published binary is named j-rig (the J-Rig brand and CLI identity are unchanged). Only the npm package scope is @intentsolutions/*.

Install

# Global — gives you the `j-rig` command everywhere
npm install -g @intentsolutions/jrig-cli

# Or per-repo (recommended for CI pinning)
npm install -D @intentsolutions/jrig-cli
pnpm add -D @intentsolutions/jrig-cli

This package is self-contained: the internal eval engine is bundled into the published artifact. It pulls only real npm runtime dependencies (notably the native better-sqlite3 for evidence persistence and the published @intentsolutions/refiner for the j-rig refine loop) — there are no unpublished workspace packages to resolve.

Usage

j-rig --version                      # report the installed CLI version
j-rig --help                         # list all commands

j-rig check <skill-dir>              # deterministic package-integrity checks
j-rig validate <eval-spec.yaml>      # validate an eval spec / contract YAML
j-rig eval <skill-dir> --spec ...    # full 7-layer binary evaluation
j-rig report                         # show results from the SQLite evidence DB
j-rig optimize                       # cluster failures, propose one change
j-rig drift                          # check whether a skill needs reevaluation
j-rig emit-evidence                  # wrap a gate-result into a signed Statement
j-rig migrate <dir>                  # codemod v0.1.0-draft → gate-result/v1
j-rig refine                         # eval-guided SKILL.md improvement loop

j-rig eval <skill-dir> expects an eval-spec.yaml (or --spec <path>) and writes evidence to a local SQLite DB (--db <path>, default j-rig.db).

Providers

The evaluator's judge layer talks to an LLM provider. The provider is auto-detected from environment variables (preferring an OpenAI-compatible endpoint) or forced with --provider:

| Provider | --provider | Env var | Model id | | ------------ | -------------------- | ------------------- | ------------------- | | DeepSeek | deepseek | DEEPSEEK_API_KEY | deepseek-v4-flash | | Kimi/Moonshot| kimi / moonshot | MOONSHOT_API_KEY | provider default | | OpenRouter | openrouter | OPENROUTER_API_KEY| provider default | | Anthropic | anthropic | ANTHROPIC_API_KEY | Claude models |

DeepSeek is reached by setting DEEPSEEK_API_KEY in the environment and selecting it explicitly:

export DEEPSEEK_API_KEY=sk-...
j-rig eval ./my-skill --spec ./eval-spec.yaml --provider deepseek

The DeepSeek adapter is the shared OpenAI-Chat-Completions adapter pointed at the DeepSeek endpoint and the deepseek-v4-flash model — no DeepSeek-specific SDK is required.

A built-in stub provider exists for pipeline plumbing only. It is gated behind J_RIG_ALLOW_STUB=1 and its results are not ground truth.

License

Apache-2.0 © Jeremy Longshore / Intent Solutions