research-report

name

research-report

description

Create or revise a markdown research or experiment report that makes the work UNDERSTANDABLE (why it matters, what the core idea is, what it achieved), backed by traceable evidence, integrated plots, optional literature support, plot manifest tracking, and version history, inside a single Harness without external Codex or Gemini calls. Use when you need to generate `report.md`, explain a result or method so a colleague can understand it, inventory or validate plots, create `plots/plot_manifest.json`, manage `report_versions.json`, connect report sections to supporting references, or turn an experiment or output directory into a reusable report workflow.

Research Report

Use this skill to produce a self-contained research or experiment report from an output directory that may contain notes, source code, tests, metrics, tables, and plots. The skill is harness-only: it never calls external Gemini or Codex MCP tools. Where the magi-researchers pipeline uses BALTHASAR (Gemini) and CASPER (Codex) reviewers, this skill spawns Claude subagents with cognitive-style framing instead.

What this report is for

A research report here is a document that makes the work understandable, not a lab audit log. When it is done, the target reader (a sharp colleague new to this specific problem, and future-you months from now) should be able to answer three questions without asking you:

1. Why does this matter? What was blocked, wrong, or impossible before, and what opens up now.
2. What is it? The core idea, explained intuition-first, before any formalism.
3. What did it achieve? The findings, stated in plain words and then earned with evidence.

Everything else (plots, manifests, validators, versioning) is supporting infrastructure that keeps those three answers honest and reproducible. It must never become the point of the report. A report that faithfully logs every artifact but leaves the reader unable to explain the idea back to you has failed, even if every validator passes.

Two habits carry the whole skill:

• Intuition before formalism. Give the one-sentence version and the mental model first; the equation, the config, and the proof come after the reader already sees the shape of the thing. (Borrow the concept-explainer skill's discipline here.)
• Meaning before evidence. State what a finding means in plain words, then show the number or figure that earns it. Evidence supports a claim the reader already understands; it does not replace the claim.

Inputs to confirm

Ask for only what is missing:

• target output directory
• report title
• the reader this is written for (default: a sharp colleague new to this problem, and future-you) and how much they already know
• the core idea in one sentence, if the user can state it (if not, deriving it from the materials is part of the job)
• why this work matters (the gap it closes or what it unblocks)
• domain (used to load references/domains/<domain>.md; default: general)
• whether existing plots already exist in plots/
• whether a lightweight plot metadata file should be supplied for better captions or section mapping
• whether section-level reference support is needed for motivation, the core idea, methodology, baseline comparisons, benchmark context, or claim-heavy passages

Expected directory shape

The skill works best when the target directory looks roughly like this:

{output_dir}/
  report.md
  report.pdf
  report_versions.json
  plots/
    *.png
    *.pdf | *.svg
    plot_manifest.json
    _plot_style.py            # copy of the helper used by every plot script
    *.py                      # plot generation scripts
    data/<stem>.csv           # raw data consumed by each script
  src/
  tests/
  notes/ | brainstorm/ | plan/ | results/ | tables/
  references/ | bib/ | related_work/

Missing folders are acceptable. Adapt the report to whatever evidence actually exists.

Report workflow conventions

When tailoring this skill to a project that already uses outputs/ directories:

• Prefer a self-contained target like outputs/{report_slug}/.
• Keep report.md, report_versions.json, and plots/plot_manifest.json in the same report root.
• Copy or regenerate only artifacts that belong to the current report narrative. Do not mix unrelated experiment outputs.
• Treat report_v{N}.md files as immutable archives once versioned.
• Keep plot paths relative to the report root so the directory can move without edits.
• Reuse existing project metrics, CSV or JSON summaries, tables, and previous report drafts before creating new artifacts.

Single-Harness workflow

The workflow has nine ordered steps. Steps marked (gate) must pass before continuing.

Step 1 — Gather materials

• Inventory src/, tests/, notes, metrics, tables, any prior report.md or report_v*.md, and any existing references/, bib/, or related-work notes.
• Read the report template in references/report_template.md.
• Read the domain template at references/domains/<domain>.md for tone and visualization conventions. Fall back to references/domains/general.md when the domain is unknown.
• If plots/ exists, build or refresh plots/plot_manifest.json:

  python skills/research-report/scripts/build_plot_manifest.py \
    "{output_dir}/plots" \
    --report-root "{output_dir}"
  

  Pass --metadata path/to/plot_metadata.json for richer captions or section hints.

Step 2 — Plot-style pre-flight (gate)

Before drafting prose, audit every plot generation script for compliance with the shared style helper. This is the local equivalent of magi's BALTHASAR/CASPER style-compliance check, but enforced statically over scripts instead of begging an LLM to spot regressions.

1. Ensure plots/_plot_style.py exists. If the project does not have it yet, copy it from assets/plot_templates/_plot_style.py so from _plot_style import ... resolves.
2. Run the plot-script auditor:

   python skills/research-report/scripts/validate_plot_scripts.py "{output_dir}" --json
   

   The auditor flags:
   • scripts that import matplotlib but do not import _plot_style or scienceplots,
   • scripts that call plt.style.use(['science', ...]) without 'no-latex' while also setting text.usetex=False (silent-fallback bug, pitfall #21),
   • scripts that override font.family or font.size after apply_style(),
   • scripts that call plt.savefig(...) only as PNG (PDF/SVG missing),
   • scripts that hardcode dpi below 300,
   • scripts that fail to call assert_english(...) on label/title strings.
3. Resolve every error before continuing. Warnings should be fixed or explicitly justified in the report.
4. If a plot is non-compliant, regenerate it via the assets/plot_templates/*.py family and re-run build_plot_manifest.py.
5. Run the JSON-artifact validator afterwards:

   python skills/research-report/scripts/validate_artifacts.py "{output_dir}" --json
   

   Treat errors as blockers. Treat warnings as items to fix or explicitly mention in the report.

Step 3 — Map evidence to the narrative

The report follows a understanding-first arc (see references/report_template.md). Sort every piece of raw material into the section whose question it answers, not into a generic bucket:

• TL;DR: the 30-second version: problem, what you did, headline result, what it means. Write it last, place it first.
• §1 Why this matters: the gap, the stakes, who feels it, why it is hard. Prior notes and literature that establish tension belong here, not a background data dump.
• §2 The core idea: the central concept explained intuition-first: the one-sentence version, the mental model or analogy, then the formal statement. Method citations that establish lineage ("this extends X") go here.
• §3 How it works: the mechanism, subordinate to §2. Data/materials, procedure, and the setup a reader needs to trust the result. Exhaustive configs go to the appendix.
• §4 What we found: quantitative outcomes, comparisons, plots, and tables, each stated as a finding-in-plain-words before its evidence. Baseline/benchmark context when needed.
• §5 So what: significance, implications, scope of the claim; pays off the stakes from §1.
• §6 Limits & open questions: failure modes actually observed, edge cases, and concretely-answerable open questions.
• Appendix: reproduction notes, source/test/artifact inventories, plot manifest summary. Optional; never the body.

Plot → section mapping uses the manifest's section_hint:

section_hint	Report section
methodology	§3 How it works
results	§4.1 Headline finding
comparison	§4.2 Supporting and comparative findings
validation	§4 What we found or §6 Limits (whichever the figure argues for)
testing	§6 Limits & open questions

Step 4 — Attach literature support where needed

• Use the companion reference-search skill whenever a section depends on prior work, external benchmark framing, method lineage, or an externally grounded claim that cannot be supported by local artifacts alone.
• Typical mappings:
  • §1 stakes / why the gap is real → background or survey
  • §2 core-idea lineage ("this extends X") and method rationale → method
  • §4 comparison targets → baseline
  • dataset, metric, or benchmark protocol context → evaluation
  • standalone factual claims → claim-support
• Prefer weaving only the strongest 2–5 references into the report prose instead of dumping long bibliographies.
• Only save section-level reference notes when the user asks for them or the project already maintains a references/ or similar folder.

Step 5 — Handle plots

• Reuse existing plots when they already support the narrative.
• Generate missing plots only from real data already present in the workspace.
• Preferred stack: matplotlib + scienceplots + the shared helper at assets/plot_templates/_plot_style.py. Copy the helper into the project's plots/ directory next to any template script you adapt so from _plot_style import ... resolves.
• Plot text must be English. Korean or other non-ASCII characters in axis labels, titles, legends, or tick labels typically render as missing-glyph boxes (□) and the failure is silent. Move localized commentary to the report caption. The shared assert_english(...) helper enforces this at runtime; call it on every user-facing string before plotting.
• LaTeX % rule. When text.usetex=True, % starts a comment and silently truncates the rest of the string ("95%" becomes "95"). The shared apply_style() keeps text.usetex=True whenever LaTeX is usable (so science/nature render correctly) and falls back to scienceplots' no-latex style only when LaTeX is unavailable. Always route user-controlled strings through latex_escape(...) (covers % & # _ $ { }) before passing them to axis labels, legends, or titles.
• Output formats: PNG at dpi=300 plus PDF (vector). SVG is an acceptable PDF substitute when PDF is impractical. The shared save_figure() enforces this and always closes the figure.
• LaTeX is the default rendering path for science and nature styles. apply_style() defaults to use_latex=True and probes for latex + dvipng at runtime. If LaTeX is unavailable, the helper automatically appends scienceplots' no-latex style modifier and emits a RuntimeWarning so you know the rendering downgraded. Never call plt.style.use(['science']) and then set text.usetex=False by hand — that combination silently substitutes DejaVu Sans for Times and the resulting figures are indistinguishable from default matplotlib (scienceplots is loaded but invisible). Always go through apply_style() so the LaTeX/no-LaTeX decision is made coherently.
• Recording the decision: apply_style() returns a dict containing latex_active, latex_probe_error, and scienceplots_available. Persist these into plot_manifest.json (or per-plot metadata) so downstream readers can tell which rendering mode produced the figure.
• PDF font embedding: the shared helper sets pdf.fonttype=42 and ps.fonttype=42 so PDFs embed TrueType fonts. Type-3 fonts are rejected by many journals.
• Color palette: Okabe-Ito (colorblind-safe) by default; TAB10 is available as a fallback constant. The cycle is unified across all templates so the same series gets the same color across plots in a report. When series ordering varies between plots, pin colors with an explicit series -> color dict.
• Figure size standards (constants in _plot_style.py):
  • FIGSIZE_SINGLE = (3.5, 2.6) — single-column journal figure (Nature single-column = 3.5 in)
  • FIGSIZE_ONE_HALF = (5.0, 3.2) — 1.5-column
  • FIGSIZE_DOUBLE = (7.0, 3.6) — full-width / double-column (Nature double-column = 7.2 in)
  • FIGSIZE_PANEL_WIDE = (7.2, 2.8) — 2-up panel base height
• In-figure title vs. caption: journal-style figures usually keep the title in the caption only. Templates include ax.set_title(...) for convenience; remove it (or leave empty) when the report caption already states the same thing.
• Raw-data contract: save the CSV consumed by each plot script to plots/data/<stem>.csv and record the path in plot_metadata.source_context. Scripts should be reproducible from a single CSV input.
• Keep filenames stable and descriptive; rebuild plot_manifest.json after any plot change.
• Use the templates in assets/plot_templates/ as starting points for learning curves, grouped comparisons, or multi-panel ablations.

Common plot pitfalls to prevent

The most frequent silent failures when generating research plots. The shared _plot_style.py helper guards against most of these; the rest belong to drafting discipline.

#	Pitfall	Why it bites	Prevention
1	Unescaped % under text.usetex=True	% is a LaTeX comment; "95%" silently becomes "95"	Default use_latex=False; otherwise wrap text with latex_escape()
2	Other unescaped LaTeX specials (& # _ $ { })	Same class as #1 — silent or noisy errors	latex_escape() covers all of them
3	Korean / CJK in axis labels, legends, titles	Glyph fallback to □, no warning	English-only rule + assert_english() runtime check
4	Type-3 fonts in saved PDFs	Many journals reject; reviewers cannot select text	pdf.fonttype=42, ps.fonttype=42 set in apply_style()
5	Non-colorblind-safe palette (red+green)	~8% of male readers cannot distinguish	Okabe-Ito default in apply_style()
6	Inconsistent series-to-color mapping across plots	"Series A" is blue in fig 1 but red in fig 2	Single shared cycle; pin colors via dict when ordering varies
7	legend(loc="best") on dense plots	Non-deterministic placement, layout shifts run-to-run	Pick an explicit loc=... for production figures
8	tight_layout() clipping suptitle	suptitle gets cut off	constrained_layout=True or subplots_adjust(top=0.85)
9	Forgetting plt.close(fig) in batch generation	Memory leak; "RuntimeWarning: figures retained"	save_figure() always closes
10	np.log of zero/negative on log axis	Silent NaN, missing data	Validate inputs; use symlog if zero is meaningful
11	Categorical x-axis without set_xticklabels	Numeric tick labels appear instead of names	Always call set_xticks and set_xticklabels together
12	Grid drawn over data	Distracting; data legibility hurt	axes.axisbelow=True (set in shared helper)
13	plt.show() in scripts run by automation	Blocks pipelines; CI hangs	Templates never call plt.show()
14	.savefig() after plt.close() (or vice versa)	Empty/black PDFs	save_figure() enforces correct order
15	96 dpi PNG used for print	Pixelation in printed reports	savefig.dpi=300 enforced
16	Hardcoded font (Times New Roman, etc.) not installed	Silent fallback to DejaVu — different look from preview	Do not override font.family per-script; use shared helper
17	transparent=True on a white-background figure	Background drops, looks wrong on colored pages	Leave default white background
18	Mismatched DPI between PNG and PDF	Tick label spacing differs across formats	Set savefig.dpi once globally
19	Empty alt text in 	Validator warns; accessibility regression	Always pass meaningful alt; copy from manifest caption
20	Plot manifest path drift after directory rename	Captions reference stale paths; build fails	Keep paths relative to report root; rebuild manifest after moves
21	science/nature styles WITHOUT LaTeX rendering	scienceplots' science.mplstyle sets text.usetex: True AND font.family: serif. Override text.usetex=False and Times silently substitutes to DejaVu Sans on machines without Times — figures look like default matplotlib, scienceplots is invisible.	apply_style() defaults to use_latex=True; auto-probes for latex + dvipng and appends scienceplots' no-latex style to the chain when LaTeX is missing. Never set text.usetex=False after plt.style.use(['science']) without also adding 'no-latex' to the chain.
22	Silent fallback when scienceplots is missing	Final figures look unlike previews	Record scienceplots_available from apply_style() in plot metadata
23	Hardcoded font.family override after apply_style()	Re-introduces pitfall #21	Treat the helper output as authoritative; do not touch font.* rcParams in scripts

Step 6 — Draft the report

Draft for understanding first. The structure exists to force three payoffs: the reader should finish §1 wanting the answer, finish §2 able to explain the idea back to you, and finish §5 seeing why §1's stakes are now settled. Before writing a section, ask what question it answers for the target reader; if it answers none, cut it.

Understanding-first drafting principles:

• Lead §1 with tension, not background. Open on what was blocked, wrong, or impossible, not on a literature tour. A reader should feel the gap before you name the method.
• §2 is the section a reader remembers. Give the one-sentence idea, then the mental model or analogy, then the formalism, in that order. If the equation appears before the intuition, reorder. Say why this idea beats the obvious alternative.
• State the meaning before the number. Every finding in §4 opens with what it means in plain words; the figure or metric follows as the thing that earns it. A paragraph that starts with a number and never says why it matters is a metrics dump.
• §5 must pay off §1. Return to the exact stakes you opened with and say what changed. If §5 introduces a brand-new concern instead of settling §1's, the arc is broken.
• Prose carries the argument; bullets only itemize. Each section needs at least one paragraph a reader can follow linearly. A section that is only bullets forces the reader to reverse-engineer the argument.
• Analogy is allowed; hand-waving is not. An analogy that builds intuition is good. A vague claim with no number, citation, or mechanism behind it is not, no matter how confident it sounds.

Evidence discipline (unchanged from the old skill, still mandatory):

• Use references/report_template.md as the starting structure.
• Embed each plot inline near the paragraph that interprets it. Never write a "list of figures" appendix table — that is an anti-pattern.
• Never use passive references such as "as shown in the figure below" or "see Figure X" without an accompanying concrete quantitative observation in the same paragraph (e.g., specific deltas, percentages, R², slope, p-values, runtime). The figure earns its space by being interpreted.
• When a section depends on prior work or benchmark framing, use curated results from reference-search and mention only references you actually reviewed.
• Avoid fake citation placeholders or generic "prior work shows" wording without concrete support.
• Avoid orphaned figures and unsupported quantitative claims.
• If a canonical section has no source material, rename or repurpose it instead of leaving a hollow placeholder.
• For dense reports, add <!-- EVIDENCE BLOCK: ev-N --> markers near the paragraph that consumes evidence id ev-N, and list the evidence inventory at the top of the report or in evidence/inventory.json so the validator can cross-check.

Report-body math conventions (LaTeX-only)

Every mathematical expression in report.md MUST use LaTeX. Unicode math symbols are not acceptable in the report body — they cause inconsistent rendering across PDF exporters (Typora, Pandoc, Marp, GitHub) and many journal stylesheets refuse to typeset them.

Inline math ($...$) — use for variable names, parameter values, complexity notation, short expressions. Examples:

Concept	Wrong (Unicode)	Right (LaTeX)
Greek	α = 0.05, σ₁, λ, θ̂	$\alpha = 0.05$, $\sigma_1$, $\lambda$, $\hat{\theta}$
Number sets	ℝⁿ, ℕ, ℤ, ℂ	$\mathbb{R}^n$, $\mathbb{N}$, $\mathbb{Z}$, $\mathbb{C}$
Operators	≈, ≤, ≥, ≠, ≪, ≫, ±, ×, ·, ÷	$\approx$, $\leq$, $\geq$, $\neq$, $\ll$, $\gg$, $\pm$, $\times$, $\cdot$, $\div$
Logic / sets	∈, ∉, ⊂, ⊆, ∪, ∩, ∀, ∃	$\in$, $\notin$, $\subset$, $\subseteq$, $\cup$, $\cap$, $\forall$, $\exists$
Sub/super	², ³, ⁴, ⁿ, xᵢ, H₂O	$^2$, $^3$, $^4$, $^n$, $x_i$, $\mathrm{H}_2\mathrm{O}$
Arrows	→, ←, ↔, ⇒, ⇔	$\to$, $\leftarrow$, $\leftrightarrow$, $\Rightarrow$, $\Leftrightarrow$
Calculus	∂, ∇, ∫, ∑, ∏, √	$\partial$, $\nabla$, $\int$, $\sum$, $\prod$, $\sqrt{}$
Infinity / Const.	∞, π, ℏ, °	$\infty$, $\pi$, $\hbar$, $^{\circ}$

Display math ($$...$$) — use for key formulas, derivations, loss functions, and main results.

✘  Inline-style display equation (silently breaks Pandoc/Typora):
$$L = \frac{1}{N}\sum_i (y_i - \hat{y}_i)^2$$

✓  Display equation on its own lines (correct):

$$
L = \frac{1}{N}\sum_i (y_i - \hat{y}_i)^2
$$

Always:

• Put $$ on a line by itself, with one blank line above and below the block.
• Use \text{...} for textual labels inside math ($L_{\text{train}}$, not $L_{train}$, which renders as $L \cdot t \cdot r \cdot a \cdot i \cdot n$).
• Use \,, \;, \quad to control spacing inside math; never insert literal spaces or unicode whitespace.
• Use \mathrm{} for upright multi-letter operators (e.g., $\mathrm{erf}(x)$, $\mathrm{KL}(p \,\|\, q)$).

The validator (validate_artifacts.py) flags single-line $$..$$ and unicode math characters in the report body as errors.

Step 7 — Coverage & gap-detection loop

After the first complete draft, walk every section and answer this checklist before presenting the draft. The first block guards understanding; the second guards evidence. A draft that passes the evidence block but fails the understanding block is not done.

Understanding checks (does the reader actually get it?):

Question	If the answer is bad
Could the target reader explain the core idea back in one sentence after reading §2?	Rewrite §2 intuition-first: one-liner, then mental model, then formalism
Does §1 make the stakes felt before any method appears (tension, not a background tour)?	Reopen §1 on what was blocked/wrong/impossible; move background trivia out
Does every finding in §4 state its meaning in plain words before its number/figure?	Add the plain-words claim; demote the metric to evidence for it
Does §5 return to and settle the exact stakes raised in §1?	Rewrite §5 to pay off §1; delete off-topic new concerns
Does the formalism in §2/§3 arrive only after the intuition it formalizes?	Reorder so the equation follows the picture, never precedes it

Evidence checks (is every claim earned?):

Question	If the answer is bad
Does every quantitative claim point to a number, a table cell, a CSV column, or a figure?	Add the missing artifact, weaken the claim, or remove the claim
Does every figure get at least one paragraph that quotes specific numbers from it?	Either add the interpretation, or remove the figure
Does §2/§3 cite the canonical reference for any non-trivial method or prior idea it builds on?	Use reference-search and weave the citation into the prose
Do comparisons report uncertainty (CI, std, error bars) or at least say why they don't?	Add error bars or an explicit caveat
Could a reader regenerate every figure from plots/data/*.csv + the script in plots/?	Restore the data file, point source_script at the right file, or remove the figure
Are limitations honest and specific (not "may not generalize")?	Rewrite with the specific failure modes you actually observed

Gap-fill plot budget (per draft pass):

Draft depth	Max new plots	Max iterations
min	2	1
default	4	2
high	8	3

If a gap requires data the workspace does not have, do not fabricate. Add an explicit caveat instead.

Step 8 — Dual-subagent traceability review

Spawn two Claude subagents simultaneously (single message, two Agent tool uses, subagent_type: general-purpose). They must run independently — neither sees the other's output. This is the harness-only equivalent of magi's BALTHASAR + CASPER pair. One reviewer guards understanding; the other guards evidence and visualization. Both matter: a report can be crystal-clear and unsupported, or airtight and incomprehensible.

Subagent A, Understanding & Narrative (does a new colleague get it?):

Prompt template: "Use the Read tool to read {output_dir}/report.md. You are a sharp colleague seeing this problem for the first time. Review whether the report makes the work understandable, and identify, per issue, the section, the problematic text, and a concrete fix.

1. Core-idea test: after reading §2, can you state the central idea in one sentence? If not, say exactly where the explanation loses you and whether intuition was skipped or buried under formalism.
2. Stakes test: does §1 make you want the answer (real tension: something blocked, wrong, or impossible), or is it a background tour? Flag background trivia that should be cut.
3. Intuition-before-formalism: flag any place an equation, config, or proof appears before the intuition it formalizes.
4. Meaning-before-evidence: flag findings in §4 that open with a number and never say what it means in plain words (metrics dumps).
5. Payoff test: does §5 return to and settle the exact stakes raised in §1? Flag a §5 that drifts to unrelated concerns.
6. Bullet-only sections: flag any section that is only bullets with no connecting prose. Return structured text. Do not save to a file."

Subagent B, Evidence Integrity & Visualization (is every claim earned?):

Prompt template: "Use the Read tool to read {output_dir}/report.md, {output_dir}/plots/plot_manifest.json, and any {output_dir}/plots/*.py scripts. Review for claim–evidence integrity and visualization correctness, and identify, per issue, the section, the figure or text, and a concrete fix.

1. Orphaned claims: text assertions without a supporting figure, table, metric, or citation.
2. Orphaned plots: figures embedded but never discussed.
3. Weak links: a claim references a figure that does not actually support it.
4. Caption quality: captions must state what the figure shows in numbers (AdamW lowers val NLL 0.94→0.71), not just Comparison of methods.
5. Missing / mis-encoded visualizations: quantitative claims that need a chart but have none; better encodings (bar→box, linear→log, grouped bars→error-bar dot plot).
6. Reproducibility gaps: plots without source_script or source_context in the manifest.
7. Style compliance: every script imports _plot_style (or scienceplots) and calls apply_style(); no per-script font.* overrides; PNG@300dpi + PDF; Nature widths; Okabe-Ito palette.
8. Math rendering: flag any unicode math symbol or single-line $$..$$. Return structured text. Do not save to a file."

After both reviews return, synthesize:

1. Consensus issues (flagged by both subagents) → fix first.
2. Divergent suggestions → evaluate on merit; apply when defensible.
3. Apply revisions:
   • buried core idea → rewrite §2 intuition-first (one-liner, mental model, then formalism),
   • weak stakes → reopen §1 on the real tension; move background out,
   • metrics dump → add the plain-words meaning, demote the number to evidence,
   • broken payoff → rewrite §5 to settle §1's stakes,
   • orphaned claim → add a supporting plot/table/citation OR weaken the claim,
   • orphaned plot → add an interpretation paragraph OR remove the figure,
   • weak link → strengthen the connecting prose or replace with a more apt figure,
   • chart-type fix → regenerate via assets/plot_templates/*.py,
   • reproducibility gap → restore the missing CSV / source script.
4. Re-run validate_artifacts.py and validate_plot_scripts.py after all edits.

Anti-consensus discipline. When both subagents agree on an issue, the fix MUST cite at least one independent piece of evidence — a concrete number, a specific section line, a named pitfall — not just "both reviewers agreed".

Step 9 — Versioning & finalization

• If report_versions.json already exists, archive the current report before overwriting:
  • read current_version
  • copy report.md to report_v{current_version}.md
• After writing the new report.md, record the new version:

  python skills/research-report/scripts/record_report_version.py \
    "{output_dir}" \
    --summary "Summarize the update" \
    --tier 1
  

• --tier corresponds to the feedback loop tiers below:
  • 1 — wording, structure, captions, formatting
  • 2 — plot/figure changes, scale/encoding swaps, manifest updates
  • 3 — substantive methodology or result changes (re-run experiments, change baselines)
• Add structured change records with repeated --change '{...json...}' arguments when useful.
• Re-run the JSON-artifact validator and the plot-script auditor.
• Report file locations, plot count, validation findings, thin sections, missing figure references, sections that still need stronger literature support, and any known caveats.

Step 10 — Export PDF and archive to Dropbox (automatic)

Every finished report is exported to PDF and mirrored into the user's ~/Dropbox/ResearchReport archive, organised by category. Do this automatically, do not wait to be asked. The category archive is a reading collection, separate from the research-backup skill which mirrors the full project tree (including src/ code) — only the report-facing artifacts travel here.

1. Export the PDF with the md2pdf-typora skill on {output_dir}/report.md, producing {output_dir}/report.pdf. This runs regardless of report language; the Whitey theme handles Korean and English both.
2. Pick the category folder. The archive is organised as ~/Dropbox/ResearchReport/<Category>/<report_slug>/ (the report_slug is the basename of {output_dir}). List the existing categories (ls ~/Dropbox/ResearchReport) and choose the one that fits the report. If none fits, ask the user which category to use or whether to create a new one (reuse the JournalClub topic vocabulary — InverseProblem, NeuralOperators, PBH, SMEFT, Unfolding, ... — when sensible); create it only after they confirm the name. Do not silently invent a category.
3. Copy the artifacts into ~/Dropbox/ResearchReport/<Category>/<report_slug>/: report.md, report.pdf, report_versions.json, and the plots/ directory (PNG + PDF
   • scripts + data, including plot_manifest.json and _plot_style.py). Do not copy src/, tests/, or other code — research-backup already mirrors those from the project tree. Match the existing layout in sibling report folders.
4. Confirm the destination path and file sizes to the user.

User-feedback loop (Tier 1 / 2 / 3)

When the user reviews report.md and asks for changes, classify the request before applying it. The classification keywords mirror magi's tiered feedback loop.

Tier	Signals	Action
1 — Cosmetic	"reword", "rephrase", "move section", "fix typo", "shorten", "expand on", "rename", "reformat", "caption"	Edit report.md directly. Archive previous version, bump tier=1.
2 — Visualization	"add plot", "change chart", "log scale", "bar chart instead", "overlay", "heatmap", "color", "axis", "resize figure", "add error bars"	Generate or modify plot via templates, rebuild manifest, re-run dual-subagent review on the affected sections only, archive previous version, bump tier=2.
3 — Substantive	"rerun", "different method", "add experiment", "change algorithm", "new baseline", "fix the code", "wrong results"	This skill cannot resolve substantive changes alone. Tell the user which experiment / source / test must be re-executed and pause. Do not bump the version.

If the request is mixed-tier, decompose: apply Tier 1 / Tier 2 first, then escalate Tier 3.

If a request does not clearly match any tier, ask the user to confirm the classification before acting.

Maximum 3 feedback iterations per entry into the loop without explicit user re-approval.

Anti-patterns (flag and fix on sight)

Understanding anti-patterns (the ones this redesign targets):

• Opening §1 with a literature tour or definitions instead of the stakes. The reader should feel what is at risk before meeting the method.
• Burying the core idea under formalism: an equation, loss function, or config block in §2 before any one-sentence version or intuition. Intuition first, always.
• A §4 finding written as a bare number ("val NLL was 0.71") with no plain-words statement of what it means or why it matters. State the meaning, then show the number.
• A §5 that restates the results instead of settling the stakes from §1, or that raises a brand-new concern the report never set up.
• Sections that are only bullet lists with no connecting prose, forcing the reader to reverse-engineer the argument.
• Confident hand-waving: a sweeping claim with no number, citation, or mechanism behind it. Analogy that builds intuition is fine; a vague assertion is not.
• Treating the report as an artifact log: cataloguing what was produced (files, runs, tests) without ever explaining why it matters, what the idea is, or what it achieved.

Evidence and formatting anti-patterns (still enforced):

• Ending the report with a "List of Figures" or "Figure Inventory" table that re-lists already-embedded plots. Every figure should be embedded inline beside its interpretation.
• "As shown in the figure below" / "see Figure X" / "the plot illustrates" without a quantitative observation in the same paragraph. Replace with concrete numbers.
• Captions that name only the chart type (Bar chart of metrics). Captions must state the takeaway in numbers (AdamW lowers val NLL from 0.94 to 0.71 at 200 steps; SGD plateaus at 1.02).
• Display equations on a single line ($$x = y$$ inline). Always put $$ on its own line with the equation between.
• Unicode math characters in the report body (σ, ², ≈, →, ℝ, ∈, ±, °, ...). Use the LaTeX equivalents from the table above.
• Hand-rolled plt.style.use(['science', 'nature']) in plot scripts that bypass apply_style(). The hand-rolled call leaves Times-vs-DejaVu fallback unhandled (pitfall #21).
• "Prior work shows that X" without a citation. Either cite or remove the claim.
• Section bodies that are only bullet points with no narrative. Each section needs at least one paragraph of prose so a reader can follow the argument without reverse-engineering bullets.

Plot metadata file contract

Use a JSON object keyed by plot stem or plot id when default inference is not enough.

{
  "training_curves": {
    "description": "Training and validation metrics across optimization steps",
    "section_hint": "results",
    "caption": "Validation loss stabilizes after the early rapid descent phase while the baseline remains consistently higher.",
    "source_context": "metrics/train_history.csv",
    "source_script": "plots/training_curves.py",
    "source_function": "main",
    "style": ["science", "nature"],
    "palette": "okabe_ito",
    "language": "en",
    "dpi": 300
  }
}

Template resources

• references/report_template.md: baseline report structure aligned with the report workflow, with embedded <!-- EVIDENCE BLOCK --> and <!-- WORD_BUDGET --> hints.
• references/domains/<domain>.md: tone, methodology, and visualization conventions per domain (ai_ml, physics, statistics, mathematics, general).
• reference-search: companion skill for background references, method citations, baseline references, benchmark context, and claim-support searches while drafting the report.
• assets/plot_templates/_plot_style.py: shared styling helper (color palette, figure sizes, rcParams, apply_style, save_figure, assert_english, latex_escape). Copy into the project's plots/ directory next to any template you adapt.
• assets/plot_templates/training_curves_template.py: line-plot template for learning curves or time-series diagnostics.
• assets/plot_templates/comparison_bars_template.py: grouped comparison template for model, dataset, or ablation summaries.
• assets/plot_templates/multi_panel_ablation_template.py: faceted multi-panel template for ablation or per-regime comparisons.
• assets/plot_templates/plot_metadata_template.json: starter metadata payload for build_plot_manifest.py --metadata.

Quality bar

• The report succeeds only if the target reader can answer, unprompted: why it matters, what the core idea is, and what it achieved. Everything below serves that.
• The core idea is explained intuition-first: one-sentence version, then mental model, then formalism.
• Every finding states its meaning in plain words before the number or figure that earns it.
• §5 pays off the exact stakes raised in §1.
• Prefer concrete quantitative statements over vague summaries.
• Keep every file path relative to the report root.
• Never fabricate data for missing plots.
• Preserve reproducibility hints: source script, source context, and generation timestamp.
• Keep plot_id stable once published.
• Plot text is English-only; localized commentary belongs in the report caption, not the figure.
• Every plot script goes through apply_style() and save_figure() from _plot_style.py — do not hand-roll style/save logic in new scripts.
• Every math expression in report.md is LaTeX. Unicode math is a validator error.
• Every figure is embedded inline next to its interpretation and accompanied by at least one quantitative observation.

When sections are missing

Adapt rather than apologizing. The narrative arc still holds even when material is thin:

• can't state a crisp core idea yet → §2 becomes the honest working hypothesis and why you believe it, not a hollow placeholder
• no formal derivation → keep §2.1/§2.2 (one-liner + intuition), mark §2.3 as "empirical / no closed form"
• no tests → fold verification evidence into §4 and honest boundaries into §6
• no implementation code → §3.3 becomes the experimental setup or materials that make the result trustworthy
• nothing surprised you → delete §4.3; never fabricate surprise

Outputs

• report.md
• report.pdf
• report_versions.json
• plots/plot_manifest.json
• optional archived reports: report_v{N}.md
• optional section-level reference support notes only when the user requests saved citation outputs or the project already uses a references directory
• category archive at ~/Dropbox/ResearchReport/<Category>/<report_slug>/ (report.md + report.pdf + report_versions.json + plots/)

After creating or updating this skill, suggest starting a new session so the new skill is discoverable from session start.