{"slug":"agentscope-ai-rl-reward","name":"rl-reward","description":"Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.","long_description":"---\nname: rl-reward\ndescription: >\n  Build RL reward signals using the OpenJudge framework.\n  Covers choosing between pointwise and pairwise reward strategies based on\n  RL algorithm, task type, and cost; aggregating multi-dimensional pointwise\n  scores into a scalar reward; pairwise tournament reward for GRPO on subjective\n  tasks (net win rate across group rollouts); generating preference pairs for\n  DPO/RLAIF; and normalizing scores for training stability.\n  Use when building reward models, scoring rollouts for GRPO/REINFORCE,\n  generating preference data for DPO, or doing Best-of-N selection.\n---\n\n# RL Reward Construction with OpenJudge\n\nBuild reward signals for reinforcement learning from human feedback (RLHF) and\nreinforcement learning from AI feedback (RLAIF) using the `openjudge` library.\n\n## When to Use This Skill\n\n- Building scalar rewards for GRPO / REINFORCE rollout scoring\n- Generating (chosen, rejected) preference pairs for DPO / IPO\n- Best-of-N candidate selection\n- Multi-dimensional reward shaping (correctness + safety + format)\n- Replacing or bootstrapping a reward model with LLM-as-judge\n\n## Step 1 — Choose Your Reward Strategy\n\nUse this decision tree **before** writing any code:\n\n```\nRL Algorithm + Task type?\n│\n├── GRPO / REINFORCE — Verifiable task (math, code, structured output)\n│   └── → POINTWISE  ✅  (FunctionGrader, exact score, zero LLM cost)\n│\n├── GRPO / REINFORCE — Subjective task (instruction following, dialogue, summarization)\n│   └── → PAIRWISE TOURNAMENT  ✅  (compare each rollout vs all others in group,\n│                                    reward = net win rate within group)\n│\n├── DPO / IPO / SLiC — need (chosen, rejected) pairs\n│   └── → PAIRWISE  ✅  (two-way comparison, return winner/loser)\n│\n└── Best-of-N / reranking — rank N candidates\n    └── → LISTWISE  ✅  (single call ranks all N at once)\n```\n\n```\nCost constraint?\n├── Low budget\n│   └── FunctionGrader (free) → pointwise; or pairwise with small judge model\n│\n├── Medium budget\n│   └── Pointwise: 2–3 LLM graders + WeightedSumAggregator\n│   └── Pairwise tournament: 1 LLM judge, N*(N-1)/2 comparisons per group\n│\n└── High quality / no cost limit\n    └── Pointwise voting (3–5 calls) or pairwise with strong judge + debiasing\n```\n\n## Sub-documents — Read When Relevant\n\n| Topic | File | Read when… |\n|-------|------|------------|\n| Pointwise multi-dim reward | `pointwise.md` | GRPO on verifiable tasks; multi-dimension scoring |\n| Pairwise reward | `pairwise.md` | GRPO on subjective tasks (tournament); DPO/RLAIF preference pairs |\n\nRead the relevant sub-document **before** writing any code.\n\n## Install\n\n```bash\npip install py-openjudge\n```\n\n## Strategy Comparison\n\n| Strategy | Output | Reward signal | Typical use | Cost |\n|----------|--------|---------------|-------------|------|\n| **Pointwise** | scalar per response | direct reward `r(x, y)` | GRPO on verifiable tasks, filtering | Low–Medium |\n| **Pairwise Tournament** | net win rate per response | relative reward within group | GRPO on subjective tasks | Medium (N²/2 calls) |\n| **Pairwise** | winner/loser pair | implicit preference `y+ > y-` | DPO, IPO, RLAIF preference data | Medium |\n| **Listwise** | rank over N responses | ordinal reward / reranking | Best-of-N, reranking | Medium–High |\n\n## Score Normalization\n\nAll graders return scores on different scales. **Always normalize** before feeding into RL:\n\n```python\ndef normalize(score: float, min_score: float, max_score: float) -> float:\n    \"\"\"Map [min_score, max_score] → [0.0, 1.0].\"\"\"\n    if max_score == min_score:\n        return 0.0\n    return (score - min_score) / (max_score - min_score)\n\n# LLM graders (common/*) return 1–5 → normalize to 0–1\nreward = normalize(result.score, min_score=1, max_score=5)\n\n# FunctionGrader / text graders already return 0–1 → no normalization needed\n```\n\n## Evaluation Strategies\n\nEvaluation strategies control **how many times** a grader is called and **how\nresults are aggregated**. They are independent of the grader itself.\n\n### Choose Your Strategy\n\n```\nGrader type?\n│\n├── Deterministic (FunctionGrader, StringMatch, CodeExecution, etc.)\n│   └── → Direct  (zero variance, no need for aggregation)\n│\n├── LLM grader — Pointwise scoring\n│   │\n│   ├── Budget limited / speed critical\n│   │   └── → Direct  (accept variance, 1× cost)\n│   │\n│   ├── Discrete scores (1–5 integer, pass/fail, binary)\n│   │   └── → Voting  (majority vote, robust to outliers, N× cost)\n│   │\n│   └── Continuous / fine-grained scores (need precise ranking)\n│       └── → Average  (mean, preserves signal, N× cost)\n│\n└── LLM grader — Pairwise GRPO tournament\n    └── → GRPOTournament  (all-pairs comparison, net win rate)\n```\n\n| Strategy | Aggregation | Best for | Cost |\n|----------|-------------|----------|------|\n| `DirectEvaluationStrategy` | None | Deterministic graders; low budget | 1× |\n| `VotingEvaluationStrategy` | Majority vote | Discrete / integer LLM scores | N× |\n| `AverageEvaluationStrategy` | Mean | Continuous LLM scores | N× |\n| `GRPOTournamentEvaluationStrategy` | Net win rate | Pairwise GRPO on subjective tasks | N²/2× |\n\nAll strategies are imported from `openjudge.evaluation_strategy`.\n\n### Pointwise — Noise Reduction with Voting / Average\n\nFor high-variance LLM judges, wrap any grader with `VotingEvaluationStrategy`\nto run N calls and take the majority vote:\n\n```python\nfrom openjudge.evaluation_strategy import VotingEvaluationStrategy\n\ngrader = CorrectnessGrader(\n    model=model,\n    strategy=VotingEvaluationStrategy(num_votes=3, tie_breaker=\"closest_to_mean\"),\n)\n# Now each call internally runs 3 LLM evaluations and returns the most common score\n```\n\nUse odd `num_votes` (3, 5) to avoid ties.\n\n### Pairwise — GRPO Tournament\n\nFor GRPO on subjective tasks, use `GRPOTournamentEvaluationStrategy` to run\nall-pairs comparison and compute net win rate per rollout:\n\n```python\nfrom openjudge.evaluation_strategy import GRPOTournamentEvaluationStrategy\n\nstrategy = GRPOTournamentEvaluationStrategy(debiased=False)\nresults = await strategy.execute(\n    pairwise_grader.aevaluate,\n    query=\"Write a haiku about the ocean.\",\n    responses=[\"rollout_1\", \"rollout_2\", \"rollout_3\", \"rollout_4\"],\n)\nrewards = [r.score for r in results]  # net win rates in [-1.0, 1.0]\n```\n\nSet `debiased=True` to run each pair in both orders and only count consistent\nresults (doubles LLM calls but mitigates position bias).\n","tagline":"Build RL reward signals using the OpenJudge framework. 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Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"outcome_stats":null,"safety":{"score":47,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","summary":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","auto_install_policy":"review","reasons":["High-risk permission hints: Shell or command execution","47/100 agent safety score"]},"auto_install_allowed":false,"human_review_required":true,"blocked":false,"audit_risk":"needs_review","permission_hints":[{"id":"shell","label":"Shell or command execution","reason":"Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.","severity":"high"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"database","label":"Database access","reason":"Skill may inspect schemas, query databases, or work with persistent stores.","severity":"medium"}],"policy_warnings":["High-risk permission hints: Shell or command execution","Quality score needs review"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","reasons":["High-risk permission hints: Shell or command execution","47/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"review","score":70,"risk_level":"medium","decision":{"recommendation":"manual_review","reason":"Test manually in an isolated workspace and compare against safer alternatives.","auto_install_allowed":false,"policy":"review","human_review_required":true},"blockers":[],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit score: Needs review","Agent safety gate: Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","Permission surface: shell or command execution, filesystem or document access","High-risk permission hints: Shell or command execution","Quality score needs review"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":84,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate rl-reward before installing it in an agent workflow","design-creative","RAG and knowledge workflows; Claude Code teams; teams that value GitHub adoption signals"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add agentscope-ai/OpenJudge --skill rl-reward"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add agentscope-ai/OpenJudge --skill rl-reward"]},{"id":"trust_score","label":"Trust score","status":"warn","score":78,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","809 GitHub stars","Apache-2.0"]},{"id":"audit_score","label":"Audit score","status":"warn","score":79,"required_for_auto_install":true,"detail":"Needs review","evidence":["Quality score needs review"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":47,"required_for_auto_install":true,"detail":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","evidence":["Test manually in an isolated workspace and compare against safer alternatives.","High-risk permission hints: Shell or command execution"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"Apache-2.0","evidence":["Apache-2.0"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":88,"required_for_auto_install":false,"detail":"1mo since push","evidence":["1mo since push"]},{"id":"permission_surface","label":"Permission surface","status":"warn","score":62,"required_for_auto_install":true,"detail":"shell or command execution, filesystem or document access","evidence":["Shell or command execution: high","Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward/evals","api":"/api/agent/evals?slug=agentscope-ai-rl-reward","text":"/api/agent/evals?slug=agentscope-ai-rl-reward&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"agentscope-ai-rl-reward","name":"rl-reward","description":"Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.","category":"design-creative","url":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","github_repo":"agentscope-ai/OpenJudge"},"suited_tasks":["RAG and knowledge workflows","Claude Code teams","teams that value GitHub adoption signals","Chunk documents","Create embeddings","Retrieve and cite relevant passages","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add agentscope-ai-rl-reward"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"rl-reward\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"rl-reward\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"rl-reward\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/agentscope-ai-rl-reward/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-rl-reward"},"trust":{"score":78,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"809 GitHub stars","repoActivity":"809 stars, 65 forks","lastPushed":"1mo since push","license":"Apache-2.0","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","install":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["design-creative","agent-skill"],"known_risks":["Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":79,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Quality score needs review"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":70,"label":"Strong"},"supply":{"track":"Design and creative production","scenario":"Design and creative","maintenance":"1mo since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"agent_contract":{"task_input":"Use rl-reward in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 78/100 Strong shortlist","Audit: 79/100 Needs review","Safety: 47/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"agentscope-ai-rl-reward (rl-reward)","install_command":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"agentscope-ai-rl-reward","task":"Use rl-reward in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward","api":"https://www.openagentskill.com/api/agent/skills/agentscope-ai-rl-reward","audit":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=agentscope-ai-rl-reward&task=Use%20rl-reward%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20rl-reward%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20rl-reward%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/agentscope-ai-rl-reward/install","manifest":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-rl-reward"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"agentscope-ai-rl-reward","name":"rl-reward","description":"Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.","category":"design-creative","url":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","github_repo":"agentscope-ai/OpenJudge"},"suited_tasks":["RAG and knowledge workflows","Claude Code teams","teams that value GitHub adoption signals","Chunk documents","Create embeddings","Retrieve and cite relevant passages","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add agentscope-ai-rl-reward"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"rl-reward\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"rl-reward\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"rl-reward\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/agentscope-ai-rl-reward/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-rl-reward"},"trust":{"score":78,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"809 GitHub stars","repoActivity":"809 stars, 65 forks","lastPushed":"1mo since push","license":"Apache-2.0","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","install":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["design-creative","agent-skill"],"known_risks":["Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":79,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Quality score needs review"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":70,"label":"Strong"},"supply":{"track":"Design and creative production","scenario":"Design and creative","maintenance":"1mo since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"agent_contract":{"task_input":"Use rl-reward in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 78/100 Strong shortlist","Audit: 79/100 Needs review","Safety: 47/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"agentscope-ai-rl-reward (rl-reward)","install_command":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"agentscope-ai-rl-reward","task":"Use rl-reward in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward","api":"https://www.openagentskill.com/api/agent/skills/agentscope-ai-rl-reward","audit":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=agentscope-ai-rl-reward&task=Use%20rl-reward%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20rl-reward%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20rl-reward%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/agentscope-ai-rl-reward/install","manifest":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-rl-reward"}},"supply_profile":{"track":{"slug":"design","label":"Design and creative production","shortLabel":"Design","description":"Design assets, images, video, audio, multimodal media, presentation, and creative production skills."},"scenario":{"label":"Design and creative","description":"I need my agent to produce design assets, UI directions, presentations, or creative media workflows.","useCases":[{"slug":"rag-knowledge","title":"RAG and knowledge"},{"slug":"browser-automation","title":"Browser automation"},{"slug":"workflow-automation","title":"Workflow automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":809,"starsLabel":"809","forks":65,"license":"Apache-2.0","qualityScore":70,"trustScore":78,"auditScore":79},"maintenance":{"status":"active","label":"1mo since push","daysSincePush":32,"lastPushedAt":"2026-08-03T13:42:53+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Quality score needs review","Needs review"]},"coverageTags":["Design","Design and creative","design-creative","agent-skill"]},"audit":{"audit_score":79,"risk_level":"needs_review","risk_label":"Needs review","quality_score":70,"trust_score":78,"maintenance_score":88,"security_score":82,"install_score":92,"warnings":["Quality score needs review"]},"quality_signals":{"model":"v2","star_score":20.36,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":12},"platforms":["Claude Code"],"use_cases":[{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"},{"slug":"document-processing","title":"Document processing","url":"https://www.openagentskill.com/use-cases/document-processing"}],"stacks":[{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"frontend-product-ui","title":"Frontend and UI","url":"https://www.openagentskill.com/collections/frontend-product-ui"}],"install":"npx skills add agentscope-ai/OpenJudge --skill rl-reward","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add agentscope-ai-rl-reward","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"rl-reward\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"rl-reward\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"rl-reward\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"agentscope-ai-rl-reward\",\"task\":\"Install rl-reward\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","github_repo":"agentscope-ai/OpenJudge","version":"1.0.0","license":"Apache-2.0","urls":{"web":"https://www.openagentskill.com/skills/agentscope-ai-rl-reward","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/rl-reward","api":"/api/agent/skills/agentscope-ai-rl-reward","install_api":"/api/skills/agentscope-ai-rl-reward/install"},"meta":{"created_at":"2026-09-02T17:11:23.305115+00:00","updated_at":"2026-09-02T17:11:23.384348+00:00","agent_friendly":true}}