Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/j-rig/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill j-rig. Copyright stays with the author (Apache-2.0).

j-rig — Skill Refiner

Overview

The Skill Refiner is the eval-guided improvement loop for SKILL.md files. It analyzes an existing skill against measured behavior, proposes safe, minimal, bounded edits, and accepts an edit only when it strictly improves. It is the second product in the Intent Solutions agent-rig stack:

J-Rig Skill Binary Eval   ->   Skill Refiner   ->   Rollout Gate
      (test)                     (improve)             (ship)

The refiner proposes bounded edits (add, delete, or replace operations on the SKILL.md text) and accepts an edit only if a held-out eval score strictly improves with no regression on any other case. It never rewrites a skill wholesale, and it never lets a skill judge itself; scoring is delegated to the separate j-rig eval harness.

This plugin is a thin wrapper. The refiner logic lives in the published @intentsolutions/refiner package and is exposed through the j-rig refine command group in the @intentsolutions/jrig-cli binary. This skill invokes that CLI; it does not reimplement any refiner logic.

Use this skill when:

• Improving an existing skill whose behavior you can measure.
• Bootstrapping a held-out eval set for a skill so you have something to score against.
• Proposing a bounded SKILL.md edit and checking whether it strictly improves.
• Gating skill edits before they ship (the 3-layer hooks do this automatically).

Do NOT use it to hand-author a brand-new skill from scratch; use /skill-creator for that. The refiner improves skills that already exist and already have measurable behavior.

Prerequisites

The subcommands wrap the published j-rig binary. Install it once:

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

# Or per-repo (recommended for CI version-pinning)
pnpm add -D @intentsolutions/jrig-cli   # then: pnpm exec j-rig --help

The refine command group is contributed by @intentsolutions/refiner, which ships as a dependency of the CLI. Model-backed steps (score, propose) require the ANTHROPIC_API_KEY environment variable; the deterministic steps (bootstrap, apply, status) run fully offline.

Instructions

Each /j-rig subcommand maps one-to-one onto a j-rig refine verb. Run them against a skill directory (the folder containing the SKILL.md).

Naming note: this skill's user-facing verbs follow the ratified plan (bootstrap, propose, shadow, promote, status). On the published CLI, shadow-validation is the acceptance gate inside propose, and promote is j-rig refine apply plus the human-gated best-pointer move. The wrappers call the real CLI verbs (bootstrap, score, propose, apply, status).

The 3-layer hook architecture (sinker, line, hook)

The plugin ships three hooks that automatically gate skill quality at three points in the Claude Code lifecycle. They are cost-tiered: the cheapest layer fires most often, the most expensive layer fires least and is rate-limited. This mirrors Anthropic's security-guidance hook pattern.

Why L3 is PreToolUse:Bash, not PostToolUse:Bash (the plan's v4.1 mechanism fix): PreToolUse is in the Anthropic hooks "Can block" allowlist, so exiting with code 2 (or emitting permissionDecision: deny) blocks the bash call before it runs. PostToolUse fires after the bash has already executed, so it cannot prevent the commit or push that triggered it. Only PreToolUse can actually gate the commit.

L3 rate limit: the Opus agentic gate is the most expensive layer, so it is rate-limited to at most once per JRIG_HOOK_RATELIMIT_SECONDS (default 300s / 5 min) via a stamp file at .j-rig/refiner/.hook-last-run. Inside the window the gate is skipped with an advisory note; it only ever blocks on a real regression finding, never on a cost-control skip.

Design principles inherited from j-rig

• Criteria are binary: an edit strictly improves the held-out score or it is rejected. No fuzzy gradients.
• The evaluator is always separate: the skill under test never scores itself.
• Observed behavior outranks claimed behavior: grade what the skill does, not what its description says.
• One change at a time: each proposal is exactly one atomic edit.
• Promotion is human-gated: the refiner proposes and validates; a human moves the best pointer.

Output

Every refiner pass writes to two places under the working directory:

• The append-only event log at .j-rig/refiner/log.jsonl (one value-record per line: rollout captures, stored versions, best-pointer moves).
• The content-addressed store under .j-rig/refiner/store/ (immutable eval sets, score records, edit proposals, and skill versions keyed by SHA-256).

Read the trajectory any time with j-rig refine status <skill-id>. The refiner also renders a signed Evidence Report (markdown plus self-contained HTML) via j-rig refine render-report <report-md-path>.

Error Handling

• propose / score fail without a key: both require ANTHROPIC_API_KEY. They fail loudly with guidance rather than fabricating a result. Set the key, or run the deterministic verbs (bootstrap, apply, status) offline.
• apply rejects a proposal: apply throws on a parent-hash mismatch or a bad anchor. This is the immutability guard, not a bug. Re-run propose against the current version.
• Non-strict edit rejected: if a proposed edit does not strictly improve the held-out score, the acceptance gate rejects it and logs it to the rejected buffer. That is the intended behavior; refine again with a different strategy.
• L3 hook blocks a commit: the commit-time agentic gate exits 2 when a staged SKILL.md regresses the held-out set. Fix the regression, or run /j-rig refine status <skill-id> to inspect what tripped it.
• CLI not installed: the hooks degrade to advisory notes when the j-rig binary is absent. Install @intentsolutions/jrig-cli to enable the model-backed layers.

Examples

A full refine loop against a skill directory:

# 1. Create a held-out eval set for the skill (deterministic, offline).
j-rig refine bootstrap skills/my-skill

# 2. Establish the baseline score (Sonnet tier).
j-rig refine score skills/my-skill --model sonnet

# 3. Propose one bounded edit; the acceptance gate shadow-validates it.
ANTHROPIC_API_KEY=... j-rig refine propose skills/my-skill --strategy skill-opt-style

# 4. If accepted, apply it, producing a new candidate version (human-gated promote).
j-rig refine apply skills/my-skill --proposal "<hash>"

# 5. Inspect the trajectory, best pointer, and event log any time.
j-rig refine status my-skill

Render the Evidence Report for a completed pass:

j-rig refine render-report .j-rig/refiner/report.md --output report.html

Resources

• @intentsolutions/refiner (refiner orchestrator + the j-rig refine command group): https://www.npmjs.com/package/@intentsolutions/refiner
• @intentsolutions/jrig-cli (the j-rig binary): https://www.npmjs.com/package/@intentsolutions/jrig-cli
• /skill-creator — author a new skill from scratch (the refiner improves existing skills; skill-creator makes new ones).
• /validate-skillmd — the deterministic Tier-2 frontmatter check the L1 Sinker hook runs on every SKILL.md edit.