Research Report Skill

Research Report Skill

Description

Generates a structured markdown research report from all previous phase outputs. Actively integrates existing plots, generates missing visualizations, and cross-verifies claim-evidence integrity. Requires at least some prior phase results to exist.

Usage

/research-report [path/to/output/dir]

Arguments

• $ARGUMENTS — Optional path to the research output directory. If not provided, uses the most recent outputs/*/ directory.

Instructions

Shared rules: Read ${CLAUDE_PLUGIN_ROOT}/shared/rules.md before starting. §MCP, §Claude-Only, §Visualization, §LaTeX apply to this skill. Inline fallback (if shared rules unavailable): Gemini models: gemini-3.1-pro-preview → gemini-2.5-pro → Claude. Codex: gpt-5.4. All math in LaTeX only (no Unicode). scienceplots ['science','nature'], 300dpi PNG+PDF, Nature widths (3.5/7.2in). Subagents use Read tool.

Claude-Only Mode

See §Claude-Only in shared rules.

MCP Tool Rules

See §MCP, §Visualization in shared rules. Additionally:

• When to search: citation verification, related work context, factual accuracy checks

Step 0: Gather Materials & Health Check

1. Find the active research output directory.
2. Inventory available materials by checking for:
   • brainstorm/synthesis.md (and other brainstorm files)
   • brainstorm/weights.json (scoring weights)
   • brainstorm/personas.md (assigned expert personas)
   • brainstorm/debate_round2_gemini.md (adversarial debate, if --depth high was used)
   • brainstorm/debate_round2_codex.md (adversarial debate, if --depth high was used)
   • plan/research_plan.md
   • plan/murder_board.md (plan stress-testing results)
   • plan/mitigations.md (murder board mitigations)
   • plan/phase_gate.md (plan phase gate report)
   • src/ contents
   • src/phase_gate.md (implementation phase gate report)
   • tests/ and test results
   • tests/phase_gate.md (test phase gate report)
   • plots/ visualizations
3. Read the plot manifest (plots/plot_manifest.json):
   • If the manifest exists, parse it as the primary source of plot information.
   • If the manifest does NOT exist but plots/ contains files, create the manifest by inventorying all .png/.pdf files in plots/ and generating metadata for each:
     • description, section_hint, caption, markdown_snippet (existing fields)
     • style: array of style sheets used (e.g., ["science", "nature"])
     • dpi: output resolution (e.g., 300)
     • source_script: path to the Python script that generated this plot
     • source_function: function name within the script (if applicable)
     • generation_date: ISO-8601 timestamp of plot generation
   • If neither exists, note that no visualizations are available yet (they may be generated in the mini-loop).
4. Plot health check: For each plot listed in the manifest, verify:
   • The PNG file exists and is non-empty (file size > 0)
   • If any plot file is missing or corrupt, note it for re-generation in Step 2.
5. Read the report template from ${CLAUDE_PLUGIN_ROOT}/templates/report_template.md.
6. Determine the domain and load the relevant domain template from ${CLAUDE_PLUGIN_ROOT}/templates/domains/ for tone/style guidance.

Step 0.5: Plot Style Validation & Regeneration

Before assembling content, validate that all existing plots comply with the required style:

1. Scan existing plots: For each plot in plots/ (or referenced in plot_manifest.json):

   • Locate the generating script (check source_script in manifest, or search src/ and plots/ for Python files that produce each plot filename)
   • Verify the script imports scienceplots and calls plt.style.use(['science', 'nature'])
   • Verify no manual plt.rcParams overrides that conflict with scienceplots (e.g., font family, linewidth, figure.facecolor)
   • Verify figsize uses Nature column widths (single: 3.5 in, double: 7.2 in)
   • Verify output is saved at 300 dpi with both PNG and PDF formats

2. Flag non-compliant plots: If any plot fails validation: a. Write a regeneration script using the required style:

   import matplotlib.pyplot as plt
   import scienceplots
   plt.style.use(['science', 'nature'])
   # ... (reuse data loading from original script)
   

   b. Ensure all text in the script is ASCII or LaTeX-escaped (no Unicode π, ², etc.) c. Execute with uv run python {script_path} d. Verify the regenerated plots exist and are non-empty e. Update plots/plot_manifest.json with style metadata

3. If no plots exist yet: Skip to Step 1 (plots will be generated in Step 3 if needed).

Step 1: Content Assembly & Plot Mapping

Read all available materials:

• brainstorm/synthesis.md — for Research Background and Brainstorming Summary sections
• brainstorm/weights.json — for Brainstorming Summary (scoring weights used)
• brainstorm/personas.md — for Brainstorming Summary (expert personas assigned)
• brainstorm/debate_round2_*.md — for Brainstorming Summary (debate resolution, if available)
• plan/research_plan.md — for Methodology section
• plan/murder_board.md — for Methodology section (Plan Stress Testing subsection)
• plan/mitigations.md — for Methodology section (mitigation strategies)
• plan/phase_gate.md — for Appendix F (Quality Assurance)
• All files in src/ — for Implementation section
• src/phase_gate.md — for Appendix F
• Test results and tests/ — for Testing section
• tests/phase_gate.md — for Appendix F
• plots/plot_manifest.json — for Results & Visualization section

Plot-to-Section Mapping: Using the section_hint field from the manifest, assign each plot to a report section:

• results → Section 5 (Results & Visualization)
• methodology → Section 3 (Methodology)
• validation → Section 5 or Section 6 (Testing)
• comparison → Section 5 (Results & Visualization)
• testing → Section 6 (Testing)

Step 2: Report Draft with Integrated Plots (Iteration 1)

Using the template structure from ${CLAUDE_PLUGIN_ROOT}/templates/report_template.md, generate the report:

Section 1 — Research Background:

• Problem statement and motivation
• Relevant prior work and context
• Research questions or hypotheses

Section 2 — Brainstorming Summary:

• Key ideas generated during brainstorming
• Expert Personas: Summarize the Gemini and Codex personas assigned and their influence on ideation (from personas.md)
• Scoring Weights: Show the weights used for direction ranking (from weights.json)
• Notable points from cross-check reviews
• Debate Resolution (if debate_round2_*.md exists): Summarize key disagreements and their resolutions
• Final chosen direction and rationale (with weighted score breakdown)

Section 3 — Methodology:

• Approach description from the research plan
• Key algorithms or techniques used
• Assumptions and their justifications
• Plan Stress Testing (if murder_board.md exists): Summarize the murder board's critical findings and the mitigations applied (from mitigations.md). This demonstrates that the methodology was adversarially tested before implementation.
• Embed any methodology-tagged plots here with their captions

Section 4 — Implementation:

• Architecture overview
• Key components and their roles
• Notable implementation decisions

Section 5 — Results & Visualization:

• Key findings and observations
• For each results/comparison-tagged plot from the manifest:
  • Write a contextualizing paragraph with concrete quantitative observations (read from captions and source data if available)
  • Embed the plot using the manifest's markdown_snippet
  • Follow with interpretation of what the plot shows
• Do NOT use passive references like "see figure below" — actively discuss what the data shows with specific numbers

Anti-pattern: Do NOT list figures in a table at the end of the report. Every figure must be embedded inline with ![caption](path) immediately before or after the paragraph that discusses it. Orphaned figure tables at the end of the report are a report quality failure.

Section 6 — Testing:

• Test strategy overview
• Test results summary
• Validation against expected outcomes
• Embed any validation/testing-tagged plots with their captions

Section 7 — Conclusion:

• Summary of contributions
• Limitations and caveats
• Future work suggestions

Appendix:

• Links to all brainstorm documents
• Link to research plan
• Links to source and test directories
• Plot manifest summary table (plot_id, description, section)

Step 3: Gap Detection & Plot Generation Loop

After completing the initial draft, perform a gap analysis:

1. Identify missing visualizations: Review the draft and ask:

   • Are there quantitative claims without supporting figures?
   • Are there comparisons described in text that would benefit from a chart?
   • Are there test results that lack visual representation?
   • Would a summary/overview figure help tie the narrative together?

2. If gaps are found (and iteration count < 2): a. For each needed plot, write a self-contained Python script using:

   import matplotlib.pyplot as plt
   import scienceplots
   plt.style.use(['science', 'nature'])
   

   b. Execute the script with uv run python {script_path} c. Save plots to plots/ as both PNG (300 dpi) and PDF d. Update plots/plot_manifest.json with the new plot entries e. Re-draft the affected report sections to integrate the new plots f. Increment the iteration counter

3. If no gaps are found (or iteration limit reached): Proceed to Step 4.

Loop constraints (scaled by depth):

Depth	Max iterations	Max plots per iteration	Total plot budget
min	1	2	2
default	2	3	6
high	3	4	12
max	3	5	15

• If depth is not set, use default.
• New plots must use existing data from src/ or test outputs — do NOT fabricate data

Step 3.5: Draft Validation Gate

Before the MAGI traceability review, run the automated validator to catch structural issues early:

1. Execute: uv run python ${CLAUDE_PLUGIN_ROOT}/utils/validate_draft.py {output_dir}/report.md --json
2. Parse the JSON output and check status:
   • "pass" → Proceed to Step 4.
   • "fail" → Fix all errors before proceeding:
     • Missing evidence blocks: Add <!-- EVIDENCE BLOCK: ev-X --> annotations for unsupported claims
     • LaTeX violations: Convert single-line display math to proper $$...$$ on separate lines
     • Missing sections: Fill in required sections (see standalone experiment guidance in Notes)
     • Word budget overruns: Trim sections exceeding their budget by >10%
3. Re-run the validator after fixes. Do NOT proceed to Step 4 with a "fail" status.

Step 4: MAGI Traceability Review

Execute these two review calls simultaneously (in the same message):

Gemini (BALTHASAR) — Scientific Rigor Review:

mcp__gemini-cli__ask-gemini(
  prompt: "You are a scientific reviewer. Analyze this research report for claim-evidence integrity. Identify:\n\n1. **Orphaned claims**: Text assertions that lack a supporting figure, table, or data reference\n2. **Orphaned plots**: Figures that are embedded but never discussed or interpreted in the text\n3. **Weak links**: Claims that reference a figure but the figure doesn't clearly support the claim\n4. **Caption quality**: Are figure captions precise, quantitative, and publication-ready?\n\nFor each issue found, specify the section, the problematic text or figure, and a concrete fix.\n\nReport draft:\n@{output_dir}/report.md\n\nPlot manifest:\n@{output_dir}/plots/plot_manifest.json",
  model: "gemini-3.1-pro-preview"  // fallback: "gemini-2.5-pro" → Claude
)

Codex (CASPER) — Visualization Quality Review:

mcp__codex-cli__ask-codex(
  prompt: "You are a data visualization reviewer. Analyze this research report for visualization quality and completeness. Identify:\n\n1. **Missing visualizations**: Quantitative results or comparisons described in text that would benefit from a chart/plot but have none\n2. **Plot-narrative mismatch**: Figures whose captions or surrounding text don't accurately describe what the plot shows\n3. **Visualization improvements**: Existing plots that could use better chart types, scales, or encodings for clarity\n4. **Reproducibility gaps**: Plots that lack source context or data references needed to regenerate them\n5. **Style compliance**: Are all figures generated with the required scienceplots style? Check for: serif fonts, thin lines, Nature-compatible sizing (single: 3.5in, double: 7.2in), 300 dpi, PDF availability. Flag any plot that appears to use matplotlib defaults or custom rcParams overrides.\n\nFor each issue found, specify the section, the problematic text or figure, and a concrete fix.\n\nReport draft:\n@{output_dir}/report.md\n\nPlot manifest:\n@{output_dir}/plots/plot_manifest.json",
  model: "gpt-5.4"
)

Note: If Codex MCP is unavailable, fall back to mcp__gemini-cli__ask-gemini with the Gemini fallback chain and visualization-focused framing.

If --claude-only: Per §SubagentExec, spawn simultaneously:

• A (CD, BALTHASAR — Scientific Rigor): Read report.md + plots/plot_manifest.json. Review: 1.Orphaned claims (text without supporting figure/table/data), 2.Orphaned plots (embedded but never discussed), 3.Weak links (claim→figure mismatch), 4.Caption quality (precise, quantitative, publication-ready?). Per issue: section, problematic text/figure, concrete fix. Return structured text.
• B (AC, CASPER — Visualization Quality): Read report.md + plots/plot_manifest.json. Review: 1.Missing visualizations, 2.Plot-narrative mismatch, 3.Visualization improvements (chart type, scales, encodings), 4.Reproducibility gaps, 5.Style compliance (serif fonts, Nature sizing 3.5in/7.2in, 300dpi, PDF available, no matplotlib defaults/custom rcParams). Per issue: section, text/figure, concrete fix. Return structured text.

Claude (MELCHIOR) — Synthesis & Revision:

After both reviews are received, synthesize the feedback:

1. Identify consensus issues (flagged by both models) — these are high-priority fixes
2. Identify divergent suggestions — evaluate each on merit and apply where appropriate
3. Apply revisions:
   • For each orphaned claim: Add a supporting visualization (if within loop budget) or add an explicit caveat ("This observation requires further visual analysis.")
   • For each orphaned plot: Add discussion text around the embedded figure
   • For each weak link: Strengthen the connecting narrative or replace with a more appropriate figure reference
   • For each visualization improvement: Generate updated plot code if the fix is straightforward (e.g., scale change, label fix)
4. Update the report draft with all revisions

Step 5: Write Final Report

1. Determine the version number:
   • If report_versions.json exists, read current_version and increment.
   • Otherwise, this is version 1.
2. If version > 1: archive current report.md → report_v{N-1}.md
3. Save the completed report to report.md.
4. Write/update report_versions.json:

   {
     "schema_version": "1.1.0",
     "current_version": 1,
     "versions": [{
       "version": 1,
       "file": "report.md",
       "created_at": "ISO-8601",
       "feedback_tier": null,
       "feedback_summary": "Initial report",
       "changes": []
     }]
   }
   

   The changes array tracks structured diffs for each version. Each entry has:
   • type: one of "plot_restyle", "plot_new", "text_fix", "section_rewrite", "style_fix", "gap_fill"
   • files (for plot changes): array of affected plot filenames
   • section (for text changes): section number or name
   • reason: brief explanation of why the change was made
5. Present summary: location, word count, plot count, thin sections, traceability review findings.

Step 6: User Feedback Loop

Maximum 3 feedback iterations per entry into this step.

Step 6a: Solicit Feedback

Present the report and ask: "Report v{N} is ready at report.md. Please review and provide feedback, or say approve to finalize."

Step 6b: Classify Feedback

When user provides feedback (not "approve"):

1. Classify into one of three tiers using these keyword signals:

   Tier 1 (Cosmetic) — wording, tone, structure, formatting, caption rewording

   • Signals: "reword", "rephrase", "move section", "fix typo", "shorten", "expand on", "rename", "reformat", "caption"

   Tier 2 (Visualization) — new/modified plots, chart type changes, scale changes, plot-narrative linkage

   • Signals: "add plot", "change chart", "log scale", "bar chart instead", "overlay", "heatmap", "color", "axis", "resize figure", "add error bars"

   Tier 3 (Substantive) — code changes, re-execution, different methodology, new experiments

   • Signals: "rerun", "different method", "add experiment", "change algorithm", "new baseline", "fix the code", "wrong results"

   If the feedback does not clearly match any tier's signals, ask the user to confirm before proceeding.

2. Present classification: "I classify this as Tier {N} ({name}). Planned action: {description}. Proceed?"

3. If user disagrees, re-classify.

4. Mixed-tier feedback: decompose and apply Tier 1/2 first, then escalate Tier 3.

Step 6c: Apply — Tier 1 (Cosmetic)

1. Archive: report.md → report_v{N}.md
2. Apply text changes directly to report
3. Write updated report.md
4. Update report_versions.json (increment version, tier=1, summary)
5. Present diff summary → return to Step 6a

Step 6d: Apply — Tier 2 (Visualization)

1. Archive: report.md → report_v{N}.md
2. For each visualization change: a. Write matplotlib+scienceplots script b. Execute with uv run python {script_path} c. Save to plots/ (PNG 300dpi + PDF) d. Update plots/plot_manifest.json
3. Re-draft affected report sections with updated plots
4. Scoped MAGI traceability review (Step 4 procedure, but only modified sections + new plots):
   • Gemini: scientific rigor on changed sections only
   • Codex: visualization quality on new/modified plots only
   • Claude: synthesize and apply
5. Write updated report.md
6. Update report_versions.json (increment version, tier=2, summary)
7. Present changes summary → return to Step 6a

Step 6e: Apply — Tier 3 (Substantive / Escalation)

This skill cannot handle substantive changes alone.

• Standalone (/research-report): Inform user which phase needs re-running. Suggest /research --resume {output_dir}. Exit loop.
• Pipeline (/research): Return control to orchestrator for outer-loop handling (see orchestrator Step R2).

Step 6f: Finalize

When user approves or iteration limit reached:

• Announce final version number and version history summary.

LaTeX Formatting Rules

See §LaTeX in shared rules.

Output Files

report.md                          # Final report (always latest version)
report_v{N}.md                     # Archived report versions (created on feedback)
report_versions.json               # Version manifest with feedback history
plots/
├── *.png                          # Plot images (300 dpi)
├── *.pdf                          # Plot vector versions
└── plot_manifest.json             # Plot registry

Notes

• Write in clear, academic-but-accessible style
• Use markdown formatting effectively (headers, lists, code blocks, tables)
• Include all plot references as relative paths
• Standalone experiments (executed directly, not through the brainstorm → plan pipeline): Do NOT leave sections as "Not Available." Instead, adapt them to the content that exists:
  • Section 2 (Brainstorming Summary) → Repurpose as "Analysis Framework" or "MAGI Panel Analysis" with the three-model deep analysis
  • Section 3 (Methodology) → Keep as-is (always applicable)
  • Section 6 (Testing) → Repurpose as "Validation" (e.g., RMT checks, Parseval identity, conservation laws)
  • Adapt section titles and content to match what was actually done
• The report should stand alone as a complete document
• When writing about plots, always include concrete quantitative observations — never just "As shown in Figure X"
• The gap detection loop is optional: if all claims are well-supported and all plots are referenced, skip directly to traceability review