{"slug":"agentscope-ai-ref-hallucination-arena","name":"ref-hallucination-arena","description":"Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.","long_description":"---\nname: ref-hallucination-arena\ndescription: >\n  Benchmark LLM reference recommendation capabilities by verifying every cited\n  paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate,\n  per-field accuracy (title/author/year/DOI), discipline breakdown, and year\n  constraint compliance. Supports tool-augmented (ReAct + web search) mode.\n  Use when the user asks to evaluate, benchmark, or compare models on academic\n  reference hallucination, literature recommendation quality, or citation accuracy.\n---\n\n# Reference Hallucination Arena Skill\n\nEvaluate how accurately LLMs recommend real academic references using the\nOpenJudge `RefArenaPipeline`:\n\n1. **Load queries** — from JSON/JSONL dataset\n2. **Collect responses** — BibTeX-formatted references from target models\n3. **Extract references** — parse BibTeX entries from model output\n4. **Verify references** — cross-check against Crossref / PubMed / arXiv / DBLP\n5. **Score & rank** — compute verification rate, per-field accuracy, discipline breakdown\n6. **Generate report** — Markdown report + visualization charts\n\n## Prerequisites\n\n```bash\n# Install OpenJudge\npip install py-openjudge\n\n# Extra dependency for ref_hallucination_arena (chart generation)\npip install matplotlib\n```\n\n## Gather from user before running\n\n| Info | Required? | Notes |\n|------|-----------|-------|\n| Config YAML path | Yes | Defines endpoints, dataset, verification settings |\n| Dataset path | Yes | JSON/JSONL file with queries (can be set in config) |\n| API keys | Yes | Env vars: `OPENAI_API_KEY`, `DASHSCOPE_API_KEY`, etc. |\n| CrossRef email | No | Improves API rate limits for verification |\n| PubMed API key | No | Improves PubMed rate limits |\n| Output directory | No | Default: `./evaluation_results/ref_hallucination_arena` |\n| Report language | No | `\"en\"` (default) or `\"zh\"` |\n| Tavily API key | No | Required only if using tool-augmented mode |\n\n## Quick start\n\n### CLI\n\n```bash\n# Run evaluation with config file\npython -m cookbooks.ref_hallucination_arena --config config.yaml --save\n\n# Resume from checkpoint (default behavior)\npython -m cookbooks.ref_hallucination_arena --config config.yaml --save\n\n# Start fresh, ignore checkpoint\npython -m cookbooks.ref_hallucination_arena --config config.yaml --fresh --save\n\n# Override output directory\npython -m cookbooks.ref_hallucination_arena --config config.yaml \\\n  --output_dir ./my_results --save\n```\n\n### Python API\n\n```python\nimport asyncio\nfrom cookbooks.ref_hallucination_arena.pipeline import RefArenaPipeline\n\nasync def main():\n    pipeline = RefArenaPipeline.from_config(\"config.yaml\")\n    result = await pipeline.evaluate()\n\n    for rank, (model, score) in enumerate(result.rankings, 1):\n        print(f\"{rank}. {model}: {score:.1%}\")\n\nasyncio.run(main())\n```\n\n## CLI options\n\n| Flag | Default | Description |\n|------|---------|-------------|\n| `--config` | — | Path to YAML configuration file (required) |\n| `--output_dir` | config value | Override output directory |\n| `--save` | `False` | Save results to file |\n| `--fresh` | `False` | Start fresh, ignore checkpoint |\n\n## Minimal config file\n\n```yaml\ntask:\n  description: \"Evaluate LLM reference recommendation capabilities\"\n\ndataset:\n  path: \"./data/queries.json\"\n\ntarget_endpoints:\n  model_a:\n    base_url: \"https://api.openai.com/v1\"\n    api_key: \"${OPENAI_API_KEY}\"\n    model: \"gpt-4\"\n    system_prompt: \"You are an academic literature recommendation expert. Recommend {num_refs} real papers in BibTeX format. Only recommend papers you are confident actually exist.\"\n\n  model_b:\n    base_url: \"https://dashscope.aliyuncs.com/compatible-mode/v1\"\n    api_key: \"${DASHSCOPE_API_KEY}\"\n    model: \"qwen3-max\"\n    system_prompt: \"You are an academic literature recommendation expert. Recommend {num_refs} real papers in BibTeX format. Only recommend papers you are confident actually exist.\"\n```\n\n## Full config reference\n\n### task\n\n| Field | Required | Description |\n|-------|----------|-------------|\n| `description` | Yes | Evaluation task description |\n| `scenario` | No | Usage scenario |\n\n### dataset\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `path` | — | Path to JSON/JSONL dataset file (required) |\n| `shuffle` | `false` | Shuffle queries before evaluation |\n| `max_queries` | `null` | Max queries to use (`null` = all) |\n\n### target_endpoints.\\<name\\>\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `base_url` | — | API base URL (required) |\n| `api_key` | — | API key, supports `${ENV_VAR}` (required) |\n| `model` | — | Model name (required) |\n| `system_prompt` | built-in | System prompt; use `{num_refs}` placeholder |\n| `max_concurrency` | `5` | Max concurrent requests for this endpoint |\n| `extra_params` | — | Extra API request params (e.g. `temperature`) |\n| `tool_config.enabled` | `false` | Enable ReAct agent with Tavily web search |\n| `tool_config.tavily_api_key` | env var | Tavily API key |\n| `tool_config.max_iterations` | `10` | Max ReAct iterations (1–30) |\n| `tool_config.search_depth` | `\"advanced\"` | `\"basic\"` or `\"advanced\"` |\n\n### verification\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `crossref_mailto` | — | Email for Crossref polite pool |\n| `pubmed_api_key` | — | PubMed API key |\n| `max_workers` | `10` | Concurrent verification threads (1–50) |\n| `timeout` | `30` | Per-request timeout in seconds |\n| `verified_threshold` | `0.7` | Min composite score to count as VERIFIED |\n\n### evaluation\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `timeout` | `120` | Model API request timeout in seconds |\n| `retry_times` | `3` | Number of retry attempts |\n\n### output\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `output_dir` | `./evaluation_results/ref_hallucination_arena` | Output directory |\n| `save_queries` | `true` | Save loaded queries |\n| `save_responses` | `true` | Save model responses |\n| `save_details` | `true` | Save verification details |\n\n### report\n\n| Field | Default | Description |\n|-------|---------|-------------|\n| `enabled` | `true` | Enable report generation |\n| `language` | `\"zh\"` | Report language: `\"zh\"` or `\"en\"` |\n| `include_examples` | `3` | Examples per section (1–10) |\n| `chart.enabled` | `true` | Generate charts |\n| `chart.orientation` | `\"vertical\"` | `\"horizontal\"` or `\"vertical\"` |\n| `chart.show_values` | `true` | Show values on bars |\n| `chart.highlight_best` | `true` | Highlight best model |\n\n## Dataset format\n\nEach query in the JSON/JSONL dataset:\n\n```json\n{\n  \"query\": \"Please recommend papers on Transformer architectures for NLP.\",\n  \"discipline\": \"computer_science\",\n  \"num_refs\": 5,\n  \"language\": \"en\",\n  \"year_constraint\": {\"min_year\": 2020}\n}\n```\n\n| Field | Required | Description |\n|-------|----------|-------------|\n| `query` | Yes | Prompt for reference recommendation |\n| `discipline` | No | `computer_science`, `biomedical`, `physics`, `chemistry`, `social_science`, `interdisciplinary`, `other` |\n| `num_refs` | No | Expected number of references (default: 5) |\n| `language` | No | `\"zh\"` or `\"en\"` (default: `\"zh\"`) |\n| `year_constraint` | No | `{\"exact\": 2023}`, `{\"min_year\": 2020}`, `{\"max_year\": 2015}`, or `{\"min_year\": 2020, \"max_year\": 2024}` |\n\nOfficial dataset: [OpenJudge/ref-hallucination-arena](https://huggingface.co/datasets/OpenJudge/ref-hallucination-arena)\n\n## Interpreting results\n\n**Overall accuracy (verification rate):**\n- **> 75%** — Excellent: model rarely hallucinates references\n- **60–75%** — Good: most references are real, some fabrication\n- **40–60%** — Fair: significant hallucination, use with caution\n- **< 40%** — Poor: model frequently fabricates references\n\n**Per-field accuracy:**\n- `title_accuracy` — % of titles matching real papers\n- `author_accuracy` — % of correct author lists\n- `year_accuracy` — % of correct publication years\n- `doi_accuracy` — % of valid DOIs\n\n**Verification status:**\n- `VERIFIED` — title + author + year all exactly match a real paper\n- `SUSPECT` — partial match (e.g. title matches but authors differ)\n- `NOT_FOUND` — no match in any database\n- `ERROR` — API timeout or network failure\n\n**Ranking order:** overall accuracy → year compliance rate → avg confidence → completeness\n\n## Output files\n\n```\nevaluation_results/ref_hallucination_arena/\n├── evaluation_report.md          # Detailed Markdown report\n├── evaluation_results.json       # Rankings, per-field accuracy, scores\n├── verification_chart.png        # Per-field accuracy bar chart\n├── discipline_chart.png          # Per-discipline accuracy chart\n├── queries.json                  # Loaded evaluation queries\n├── responses.json                # Raw model responses\n├── extracted_refs.json           # Extracted BibTeX references\n├── verification_results.json     # Per-reference verification details\n└── checkpoint.json               # Pipeline checkpoint for resume\n```\n\n## API key by model\n\n| Model prefix | Environment variable |\n|-------------|---------------------|\n| `gpt-*`, `o1-*`, `o3-*` | `OPENAI_API_KEY` |\n| `claude-*` | `ANTHROPIC_API_KEY` |\n| `qwen-*`, `dashscope/*` | `DASHSCOPE_API_KEY` |\n| `deepseek-*` | `DEEPSEEK_API_KEY` |\n| Custom endpoint | set `api_key` + `base_url` in config |\n\n## Additional resources\n\n- Full config examples: [cookbooks/ref_hallucination_arena/examples/](../../cookbooks/ref_hallucination_arena/examples/)\n- Documentation: [docs/validating_graders/ref_hallucination_arena.md](../../docs/validating_graders/ref_hallucination_arena.md)\n- Official dataset: [HuggingFace](https://huggingface.co/datasets/OpenJudge/ref-hallucination-arena)\n- Leaderboard: [openjudge.me/leaderboard](https://openjudge.me/leaderboard)\n","tagline":"Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. 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issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"1mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"Apache-2.0"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":28,"weight":0.12,"status":"fail","detail":"command execution surface, credential or environment access"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":18,"weight":0.07,"status":"fail","detail":"secrets or environment access, shell or command execution"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"info","label":"GitHub adoption","detail":"809 GitHub stars"},{"status":"info","label":"Stars/forks activity","detail":"809 stars, 65 forks; 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require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"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"]},"outcome_stats":null,"safety":{"score":32,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","summary":"This skill should not be selected by an agent without explicit human security review.","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","auto_install_policy":"block","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"auto_install_allowed":false,"human_review_required":true,"blocked":true,"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":"secrets","label":"Secrets or environment access","reason":"Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.","severity":"high"},{"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, Secrets or environment access","Dependency or permission surface needs review"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"blocked":true,"human_review_required":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":66,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Agent safety gate: This skill should not be selected by an agent without explicit human security review.","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Agent safety gate: This skill should not be selected by an agent without explicit human security review.","Permission surface: secrets or environment access, shell or command execution"],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit score: Needs review","High-risk permission hints: Shell or command execution, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"],"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":94,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate ref-hallucination-arena before installing it in an agent workflow","security","Research agents 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 ref-hallucination-arena"]},{"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 ref-hallucination-arena"]},{"id":"trust_score","label":"Trust score","status":"warn","score":72,"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":76,"required_for_auto_install":true,"detail":"Needs review","evidence":["Dependency or permission surface needs review"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":32,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. 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Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.","category":"security","url":"https://www.openagentskill.com/skills/agentscope-ai-ref-hallucination-arena","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena","github_repo":"agentscope-ai/OpenJudge"},"suited_tasks":["Research agents workflows","Claude Code teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Read uploaded files","Extract structured fields"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","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-ref-hallucination-arena"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"ref-hallucination-arena\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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-ref-hallucination-arena/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-ref-hallucination-arena"},"trust":{"score":72,"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/ref-hallucination-arena","install":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","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":["security","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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Inspect the source, dependencies, and permission surface first."},"quality":{"score":70,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","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, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use ref-hallucination-arena in an agent workflow","recommended_action":"Do not auto-install. 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Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.","category":"security","url":"https://www.openagentskill.com/skills/agentscope-ai-ref-hallucination-arena","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena","github_repo":"agentscope-ai/OpenJudge"},"suited_tasks":["Research agents workflows","Claude Code teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Read uploaded files","Extract structured fields"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","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-ref-hallucination-arena"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"ref-hallucination-arena\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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-ref-hallucination-arena/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-ref-hallucination-arena"},"trust":{"score":72,"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/ref-hallucination-arena","install":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","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":["security","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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Inspect the source, dependencies, and permission surface first."},"quality":{"score":70,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","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, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use ref-hallucination-arena in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 72/100 Strong shortlist","Audit: 76/100 Needs review","Safety: 32/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"agentscope-ai-ref-hallucination-arena (ref-hallucination-arena)","install_command":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","risk_summary":"Needs review; Blocked for auto-install; 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-ref-hallucination-arena","task":"Use ref-hallucination-arena 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-ref-hallucination-arena","api":"https://www.openagentskill.com/api/agent/skills/agentscope-ai-ref-hallucination-arena","audit":"https://www.openagentskill.com/skills/agentscope-ai-ref-hallucination-arena/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=agentscope-ai-ref-hallucination-arena&task=Use%20ref-hallucination-arena%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20ref-hallucination-arena%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20ref-hallucination-arena%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/agentscope-ai-ref-hallucination-arena/install","manifest":"https://www.openagentskill.com/api/registry/manifest/agentscope-ai-ref-hallucination-arena"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"Research agents","description":"I need my agent to research a topic, compare sources, and produce a concise report.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"document-processing","title":"Document processing"},{"slug":"workflow-automation","title":"Workflow automation"}]},"applicableAgents":["Claude Code","OpenAI Agents","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":809,"starsLabel":"809","forks":65,"license":"Apache-2.0","qualityScore":70,"trustScore":72,"auditScore":76},"maintenance":{"status":"active","label":"1mo since push","daysSincePush":34,"lastPushedAt":"2026-08-03T13:42:53+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"coverageTags":["Research","Research agents","security","agent-skill"]},"audit":{"audit_score":76,"risk_level":"needs_review","risk_label":"Needs review","quality_score":70,"trust_score":72,"maintenance_score":88,"security_score":74,"install_score":92,"warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"quality_signals":{"model":"v2","star_score":20.36,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":12},"platforms":["Claude Code","OpenAI Agents"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"document-processing","title":"Document processing","url":"https://www.openagentskill.com/use-cases/document-processing"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"},{"slug":"security-compliance","title":"Security and compliance","url":"https://www.openagentskill.com/use-cases/security-compliance"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"web-data-pipeline","title":"Web data pipeline","url":"https://www.openagentskill.com/collections/web-data-pipeline"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add agentscope-ai/OpenJudge --skill ref-hallucination-arena","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-ref-hallucination-arena","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 \"ref-hallucination-arena\" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" as a Claude Code skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena. 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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 \"ref-hallucination-arena\" from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena 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: Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy. 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-ref-hallucination-arena\",\"task\":\"Install ref-hallucination-arena\",\"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/ref-hallucination-arena","github_repo":"agentscope-ai/OpenJudge","version":"1.0.0","license":"Apache-2.0","urls":{"web":"https://www.openagentskill.com/skills/agentscope-ai-ref-hallucination-arena","repository":"https://github.com/agentscope-ai/OpenJudge/tree/main/skills/ref-hallucination-arena","api":"/api/agent/skills/agentscope-ai-ref-hallucination-arena","install_api":"/api/skills/agentscope-ai-ref-hallucination-arena/install"},"meta":{"created_at":"2026-09-02T18:11:25.096625+00:00","updated_at":"2026-09-02T18:11:25.162844+00:00","agent_friendly":true}}