inno-idea-eval

Inno Idea Eval

Directory structure

skills/inno-idea-eval/
├── SKILL.md                                    ← this file
├── prompts/
│   ├── build_eval_query.md                     ← Per-persona evaluation query (all 5 dims)
│   ├── build_evidence_assembly.md              ← How to compose evidence from pipeline artifacts
│   ├── build_meta_review_query.md              ← Area-chair aggregation of 3 persona reviews
│   ├── build_novelty_queries.md                ← Query extraction for novelty verification (Step 0.5a)
│   ├── build_novelty_analysis.md               ← Similarity analysis for novelty verification (Step 0.5c)
│   └── build_refinement_feedback_query.md      ← Structured feedback for refinement loop
└── references/
    ├── eval_agent_instructions.md              ← Full eval agent system prompt + scoring rubrics
    ├── novelty_verification_config.md          ← Novelty search config, threat levels, fast-fail protocol
    └── reviewer_personas.md                    ← 3 persona definitions + evidence filter logic

How to use the resource files: Each prompt template in prompts/ documents the exact parameters, the full text template, and usage notes (when it is a new conversation vs. appended message, how to format evidence blocks, etc.). The references/ directory contains the Eval Agent's complete system instructions including its scoring rubrics, persona definitions, and evidence filter logic. Consult these files for the authoritative details; the steps below provide a summary.

Inputs

Paths for Ideation/ideas and Ideation/references come from instance.json (instance.Ideation.ideas, instance.Ideation.references). They are absolute in Dr. Claw-created projects; use as-is. If relative, resolve with path.join(project_path, value).

*Standalone mode: only selected_idea required; evaluation proceeds ungrounded with a noted limitation.

Outputs

Cache file outputs

Each step produces two kinds of files:

1. .txt files (primary) -- the full markdown content of each review, written directly to Ideation/ideas/
2. .json files (derived) -- structured metadata under Ideation/ideas/logs/, whose text fields must be copied verbatim from the corresponding .txt files (never summarized)

Full directory layout

Ideation/ideas/
├── novelty_grounding_report.txt                ← Step 0.5: Active Novelty Verification report
├── eval_report.txt                             ← Step 4: full meta-review report (markdown)
├── eval_persona_1_review.txt                   ← Step 1: Senior ML Researcher review
├── eval_persona_2_review.txt                   ← Step 2: Domain Expert review
├── eval_persona_3_review.txt                   ← Step 3: Methods Specialist review
└── logs/
    ├── idea_eval_agent_novelty.json            ← Step 0.5: Novelty search + analysis structured data
    ├── idea_eval_agent_persona_1.json          ← Step 1: Persona 1 structured scores
    ├── idea_eval_agent_persona_2.json          ← Step 2: Persona 2 structured scores
    ├── idea_eval_agent_persona_3.json          ← Step 3: Persona 3 structured scores
    └── idea_eval_agent_meta_review.json        ← Step 4: Aggregated decision + full report

Write order (critical)

For every step, always write the .txt file first, then build the .json file by copying the .txt content into the appropriate field:

For the novelty verification step:

1. Write novelty_grounding_report.txt with the full novelty analysis report
2. Copy that full text into report_text
3. Write logs/idea_eval_agent_novelty.json

For each persona review:

1. Write eval_persona_{N}_review.txt with the agent's full review
2. Read it back (or keep in memory) and embed the full text into review_text
3. Write the corresponding logs/idea_eval_agent_persona_{N}.json

For the meta-review step:

1. Write eval_report.txt with the agent's full meta-review report
2. Copy that full text into report_text
3. Write logs/idea_eval_agent_meta_review.json

.txt file naming

.json file naming

.json file format (each persona)

Each file contains context_variables only (no messages). The review_text field holds the full text copied from the corresponding .txt file:

{
  "context_variables": {
    "ideas_path": "<instance.Ideation.ideas>",
    "references_path": "<instance.Ideation.references>",
    "persona": "senior_ml_researcher | domain_expert | methods_specialist",
    "scores": {
      "clarity": { "score": 0, "reason": "...", "references": [] },
      "novelty": { "score": 0, "reason": "...", "references": [] },
      "validity": { "score": 0, "reason": "...", "references": [] },
      "feasibility": { "score": 0, "reason": "...", "references": [] },
      "significance": { "score": 0, "reason": "...", "references": [] }
    },
    "strengths": [],
    "weaknesses": [],
    "suggestions": [],
    "recommendation": "Accept|Reject|...",
    "review_text": "<FULL text from eval_persona_{N}_review.txt>"
  }
}

.json file format (meta-review)

{
  "context_variables": {
    "ideas_path": "<instance.Ideation.ideas>",
    "references_path": "<instance.Ideation.references>",
    "aggregated_scores": {
      "clarity": { "avg": 0, "scores": [0, 0, 0] },
      "novelty": { "avg": 0, "scores": [0, 0, 0] },
      "validity": { "avg": 0, "scores": [0, 0, 0] },
      "feasibility": { "avg": 0, "scores": [0, 0, 0] },
      "significance": { "avg": 0, "scores": [0, 0, 0] }
    },
    "overall_avg": 0,
    "decision": "strong_accept|accept|borderline_accept|borderline_reject|reject",
    "report_text": "<FULL text from eval_report.txt>",
    "strengths": [],
    "weaknesses": [],
    "suggestions": [],
    "idea_evaluation_result": { "...complete structured result..." }
  }
}

.json file format (novelty verification)

{
  "context_variables": {
    "step": "novelty_verification",
    "search_config": {
      "num_queries": 4,
      "sources": ["arxiv", "semantic_scholar", "openalex"],
      "max_results_per_query": 10,
      "year_from": "<current_year - 3>"
    },
    "queries": [
      { "type": "core_method", "query": "...", "rationale": "..." },
      { "type": "problem_domain", "query": "...", "rationale": "..." },
      { "type": "key_component", "query": "...", "rationale": "..." },
      { "type": "broad_approach", "query": "...", "rationale": "..." }
    ],
    "idea_summary": "...",
    "search_results": { "total_raw": 0, "total_unique": 0 },
    "triage": [
      { "title": "...", "year": 0, "relevance": "high|medium|low|irrelevant", "is_inspiration_source": false, "assessment": "..." }
    ],
    "detailed_analysis": [
      { "title": "...", "year": 0, "overlap": "...", "differences": "...", "threat_level": "..." }
    ],
    "novelty_threat_level": "critical_overlap|high_overlap|moderate_overlap|low_overlap|novel",
    "genuine_novel_contributions": ["..."],
    "report_text": "<FULL text from novelty_grounding_report.txt>",
    "fast_fail_triggered": false,
    "user_decision": null
  }
}

• review_text and report_text must contain the complete markdown from the .txt file -- never a summary or abbreviation.
• IMPORTANT: Each persona .json grows independently; the meta-review .json aggregates all three.

Step-by-step Instructions

Step 0 -- Assemble Evidence

Full template: prompts/build_evidence_assembly.md

Read existing pipeline artifacts and compose 3 evidence blocks (one per persona knowledge level):

Sources: Ideation/references/papers/, Experiment/code_references/, references string, prepare_res, data_module.TASK. No new search needed.

If running in standalone mode (no pipeline artifacts), note this limitation in each review and proceed with ungrounded evaluation.

Step 0.5 -- Active Novelty Verification

Query template: prompts/build_novelty_queries.md Analysis template: prompts/build_novelty_analysis.md Configuration: references/novelty_verification_config.md

Proactively search the literature to verify whether the idea (or key components) already exists. This step runs before persona reviews so all 3 reviewers have the prior art report as evidence.

Sub-steps:

0.5a — Extract search queries (LLM call using build_novelty_queries.md):

• Input: selected_idea + known source_papers (inspiration)
• Output: 4 search queries (core_method, problem_domain, key_component, broad_approach) + idea_summary + key_terms
• If query extraction fails, fall back to extracting queries from the idea title and key sentences

0.5b — Execute searches (4 invocations of search_ai_papers.py):

python3 <searching-ai-papers-skill-directory>/scripts/search_ai_papers.py \
  --query "<query>" --sources arxiv,semantic_scholar,openalex \
  --max-results 10 --year-from <current_year-3> --format json

• To resolve <searching-ai-papers-skill-directory>, search for the script at runtime:
  1. Look for a sibling skill directory: find a directory named searching-ai-papers alongside the other installed skills (e.g., next to this skill's own directory).
  2. Fallback: use find or glob to locate searching-ai-papers/scripts/search_ai_papers.py under common skill installation roots (~/.claude/skills/, ~/.codex/skills/, or the parent of this skill's directory).
  3. If the script cannot be found, report the missing dependency to the user and skip the search step.
• Do not hardcode ~/.claude/... or any other user-specific home path.
• Run once per query (4 total)
• Collect all results and cross-deduplicate by title similarity
• If a search fails, log the error and proceed with available results
• If ALL searches fail, proceed with unverified novelty (set threat level to unverified)

0.5c — Analyze similarity (LLM call using build_novelty_analysis.md):

• Input: selected_idea + deduplicated search results + source_papers + idea_summary + key_terms
• Three-phase analysis: Triage → Deep Analysis → Synthesis
• Papers matching known inspiration sources are tagged [INSPIRATION_SOURCE]
• Output: Novelty Grounding Report with threat level assessment

0.5d — Fast-fail check:

• If threat level is critical_overlap on a non-inspiration paper AND CRITICAL_OVERLAP_FAST_FAIL is true:
  • Present the overlapping paper to the user
  • Offer choices: Proceed / Refine / Abandon
  • Record the user's decision in the JSON log
• If user chooses "Refine": return to idea generation with the overlapping paper as context
• If user chooses "Abandon": stop evaluation

0.5e — Inject report into evidence:

• The Novelty Grounding Report is included in evidence blocks for ALL 3 personas (regardless of evidence level)
• In standalone mode, this step still runs (search does not depend on pipeline artifacts)

Save (txt first, then json):

1. Write the full report -> Ideation/ideas/novelty_grounding_report.txt
2. Build structured data with report_text copied verbatim from the .txt file
3. Write -> Ideation/ideas/logs/idea_eval_agent_novelty.json

For refinement re-runs, save as novelty_grounding_report_v{N}.txt and idea_eval_agent_novelty_v{N}.json.

Steps 1-3 -- Three Persona Reviews (each in a NEW conversation)

Full template: prompts/build_eval_query.md Agent system prompt: references/eval_agent_instructions.md Persona definitions: references/reviewer_personas.md

For each persona (1=Senior ML Researcher, 2=Domain Expert, 3=Methods Specialist):

1. Build eval query using prompts/build_eval_query.md template with persona-specific evidence block from Step 0
2. Start a NEW conversation with the Eval Agent
3. The agent evaluates all 5 dimensions and produces structured scores

Scoring Calibration (from InnoEval):

• 9-10 (10%): Groundbreaking / paradigm-shifting
• 7-8 (25%): Strong contribution with clear novelty
• 5-6 (45%): Solid but incremental
• 3-4 (15%): Notable weaknesses
• 0-2 (5%): Fundamentally flawed

Self-Discovery Check (Novelty only): If a found paper appears identical to the idea, assume it IS the idea's inspiration source -- don't penalize.

Save (txt first, then json) after each persona:

1. Write the agent's full review -> Ideation/ideas/eval_persona_{N}_review.txt
2. Build structured scores JSON
3. Write -> Ideation/ideas/logs/idea_eval_agent_persona_{N}.json

Step 4 -- Meta-Review

Full template: prompts/build_meta_review_query.md

Aggregate all 3 reviews. The agent acts as Area Chair:

• Computes average score per dimension across all personas
• Resolves reviewer disagreements (where scores differ by >3 points)
• Produces final recommendation

Decision Thresholds:

Save (txt first, then json):

1. Write the meta-review report -> Ideation/ideas/eval_report.txt
2. Build aggregated scores and decision
3. Write -> Ideation/ideas/logs/idea_eval_agent_meta_review.json

Step 5 -- Quality Gate

• Accept path (strong_accept or accept): Pipeline continues to inno-code-survey. selected_idea passes through unchanged.
• Borderline path (borderline_accept or borderline_reject): Present evaluation report to user. Ask whether to proceed, refine, or abandon.
• Reject path (reject): Build structured feedback via prompts/build_refinement_feedback_query.md. Trigger refinement loop.

Step 6 -- Refinement Loop (if triggered)

Full template: prompts/build_refinement_feedback_query.md

1. Build structured feedback from all persona reviews (weaknesses + suggestions)
2. Append refinement prompt to the original idea generation conversation (from inno-idea-generation)
3. Idea Agent revises the idea (not generates new)
4. Save revised idea as Ideation/ideas/refined_idea_v{N}.txt
5. Re-run evaluation (Steps 1-4) on the refined idea
6. Maximum 2 refinement iterations before requiring user decision
7. If accepted after refinement, update selected_idea.txt and final_selected_idea_data

Step 7 -- Output

Set context_variables["idea_evaluation_result"] with complete structured data:

{
  "decision": "strong_accept|accept|...",
  "overall_avg": 0.0,
  "aggregated_scores": { "..." },
  "persona_reviews": [ "..." ],
  "report": "<full report text>",
  "novelty_verification": {
    "threat_level": "critical_overlap|high_overlap|moderate_overlap|low_overlap|novel",
    "genuine_novel_contributions": ["..."],
    "search_coverage": { "total_raw": 0, "total_unique": 0, "sources": ["..."] },
    "fast_fail_triggered": false,
    "user_decision": null
  },
  "refinement_iterations": 0,
  "grounded": true
}

If refinement occurred, also update:

• Ideation/ideas/selected_idea.txt with the refined idea
• context_variables["final_selected_idea_data"] with updated text

Configuration

Checklist

• Evidence assembled from pipeline artifacts (or standalone mode noted)
• Novelty queries extracted (4 queries: core_method, problem_domain, key_component, broad_approach)
• Literature search executed (4 queries x 3 sources) and results deduplicated
• Novelty Grounding Report generated with threat level assessment
• Fast-fail check applied (if critical_overlap detected on non-inspiration paper)
• Report saved -> novelty_grounding_report.txt, then full text copied into logs/idea_eval_agent_novelty.json
• Novelty report injected into evidence blocks for all 3 personas
• Persona 1 review saved -> eval_persona_1_review.txt, then full text copied into logs/idea_eval_agent_persona_1.json
• Persona 2 review saved -> eval_persona_2_review.txt, then full text copied into logs/idea_eval_agent_persona_2.json
• Persona 3 review saved -> eval_persona_3_review.txt, then full text copied into logs/idea_eval_agent_persona_3.json
• Meta-review saved -> eval_report.txt, then full text copied into logs/idea_eval_agent_meta_review.json
• Decision computed from aggregated scores
• Quality gate applied: accept -> proceed; borderline -> ask user; reject -> refine
• If refinement: feedback built, idea revised, re-evaluated (max 2 iterations)
• context_variables["idea_evaluation_result"] set with complete structured data (including novelty_verification)
• If refinement occurred: selected_idea.txt updated, final_selected_idea_data updated
• All .txt files written to Ideation/ideas/, all .json files written to Ideation/ideas/logs/