PaperSpine Research

Use this skill before motivation confirmation and before any scene-specific writing. No target-scene research means no venue-specific writing advice.

Research runs in three stages: index locally, launch three parallel specialist sub-agents, then merge findings into motivation options.

Inputs

Read paper_rewriting_output/paper_spine_config.json when available. The important fields are scene, tier, target_name, official_urls, materials_dir, draft_path, reference_mode, reference_paths, and output_language.

Tier Rules

• flash: collect 3 target-scene examples and 3 recent high-quality field/SOTA examples.
• pro: collect 6 target-scene examples and 6 recent high-quality field/SOTA examples.

Users may override counts explicitly, but do not invent that override.

These learning examples are separate from citation_support_bank.md. Learning examples teach structure and writing strategy. Citation-support papers support individual literature statements later.

Stage 1 — Index Local References (main thread)

Create the reference materials workspace:

paper_rewriting_output/reference_materials/
  source_index.md
  official_requirements/
  target_examples/
  field_sota/
  templates/
  figures_images/
  extracted_notes/

Index all locally available references. Use scripts/reference_inventory.py or produce the same source_index.md format:

Do NOT stop after indexing. Proceed immediately to Stage 2.

Stage 2 — Parallel Specialist Agents

Launch the following three sub-agents simultaneously (single message, three Agent tool calls). Each agent works independently and does not see the others' outputs. Each agent is given only the context it needs — do not pass the full conversation history.

Agent A: Scene Analyst

Goal: Produce paper_rewriting_output/research_dossier.md

Context to pass:

• scene, target_name, official_urls, output_language from config
• reference_materials/source_index.md from Stage 1
• The scene-specific reference: references/scenario-{journal|conference|report_review|competition}.md

Instructions:

You are a Scene Analyst. Write research_dossier.md and stop.

LIMITS (must obey):
- Read the scene reference file, search official URLs at most TWICE
- Target output: 300-500 words total across 4 sections
- Do NOT enumerate every possible requirement — list only the top constraints
- Write the file immediately after gathering key facts, do NOT keep searching

Sections:
1. ## Venue Requirements — format rules (page limit, structure, anonymization)
2. ## Review Criteria — what reviewers evaluate
3. ## Accepted Paper Patterns — 1-2 structural patterns from scene reference
4. ## Constraints for This Paper

Output ONLY research_dossier.md. Do NOT produce other files.

Agent B: Exemplar Learner

Goal: Produce paper_rewriting_output/exemplar_learning_dossier.md

Context to pass:

• tier from config (to know how many examples to analyze)
• reference_materials/source_index.md from Stage 1
• The scene scenario reference file path

Instructions:

You are an Exemplar Learner. Write exemplar_learning_dossier.md and stop.

LIMITS:
- Analyze at most 3 papers (flash) or 6 papers (pro) — do NOT exceed tier count
- For each paper: ONE paragraph summarizing structural patterns, NOT a full review
- Target output: 400-600 words total
- Write the file immediately after the last paper, do NOT keep adding

Sections:
1. ## Exemplar Inventory — table: title, venue, year, why selected
2. ## Structural Patterns — 2-3 reusable moves observed across exemplars
3. ## Rhetorical Patterns — 1-2 opening/closing techniques
4. ## Language Patterns — brief note on register and conventions

Output ONLY exemplar_learning_dossier.md. Do NOT copy claims/results.

Agent C: SOTA Mapper

Goal: Produce paper_rewriting_output/sota_gap_map.md

Context to pass:

• tier from config
• reference_materials/source_index.md from Stage 1
• The user's user_motivation if set (treat as hypothesis, not confirmed)

Instructions:

You are a SOTA Mapper. Write sota_gap_map.md and stop.

LIMITS:
- Map at most 6 relevant SOTA papers — pick the most representative ones
- ONE line per paper in the table, do NOT write paragraphs per entry
- Target output: table with 4-6 rows + 2-3 gap summary lines
- If the user provided a motivation hypothesis, add it as ONE additional row

Table format:
| Candidate Contribution | What SOTA Already Does | User Evidence | Real Gap | Claim Strength | Risk |

Add a ## Gap Summary with the 2 most promising gaps. Output ONLY sota_gap_map.md.

Agent launch checklist

• Launch all three in ONE message with three Agent tool calls.
• Each agent gets ONLY the context listed above — stripped-down, task-specific.
• Do NOT let agents see each other's instructions or outputs.
• All three write to paper_rewriting_output/.

Stage 3 — Merge and Synthesize (main thread)

After all three agents complete, read their outputs and produce:

style_profile.md

Merge exemplar language patterns with scene norms:

motivation_options_after_research.md

Merge the dossier, exemplar analysis, and SOTA gap map into candidate motivations:

Rules:

• Each option must be concise. Prefer one controlling contribution.
• If the real novelty is narrow, say so honestly.
• Cross-reference all three agents: a good motivation is one that fits the venue (Scene Analyst), follows exemplar structural patterns (Exemplar Learner), and occupies a real gap (SOTA Mapper).

User Confirmation

Stop and present the motivation options to the user. Ask them to choose, revise, or write their own. Only after confirmation, write confirmed_motivation.md:

• exact confirmed motivation,
• user confirmation status,
• rejected options and why,
• scope limits and forbidden overclaims.

Required Outputs

• paper_rewriting_output/reference_materials/source_index.md
• paper_rewriting_output/research_dossier.md
• paper_rewriting_output/exemplar_learning_dossier.md
• paper_rewriting_output/style_profile.md
• paper_rewriting_output/sota_gap_map.md
• paper_rewriting_output/motivation_options_after_research.md
• paper_rewriting_output/confirmed_motivation.md only after user confirmation