deep-research

name	deep-research
description	Deep scientific investigation with papercli. Iterative search, broad PDF corpus download and reading, equation-level analysis, and exhaustive referenced markdown findings.
homepage	https://github.com/jimezsa/papercli
metadata	{"opencolab":{"emoji":"📄","os":["linux","darwin"],"requires":{"bins":["papercli"]},"install":[{"id":"homebrew","kind":"shell","script":"brew install jimezsa/tap/papercli","bins":["papercli"],"label":"Install PaperCLI with Homebrew"},{"id":"source","kind":"shell","script":"git clone https://github.com/jimezsa/papercli.git && cd papercli && make build && sudo install -m 0755 ./bin/papercli /usr/local/bin/papercli","bins":["papercli"],"label":"Build PaperCLI from source"}]}}

Deep Research Skill

Use this skill for comprehensive scientific research tasks such as state-of-the-art reviews, deep comparisons, research strategy, and evidence-heavy decision support.

If the user later asks an exact follow-up question about a downloaded paper or wants a bounded local verification pass, switch to pageindex-grounded for grounded retrieval over the existing PDF corpus.

Update This Skill

Only do this if the user explicitly asks to update this skill from the GitHub repo.

To refresh this skill directly from the GitHub repo:

curl -fsSL https://raw.githubusercontent.com/jimezsa/papercli/main/SKILLS/deep-research/SKILL.md \
  -o SKILLS/deep-research/SKILL.md

Mission

Deliver an institutional-grade findings.md by:

1. Running iterative papercli retrieval across multiple query waves.
2. Downloading and reading a broad, diverse paper corpus.
3. Extracting core ideas, concepts, results, assumptions, and key mathematics.
4. Producing a detailed markdown report inside a topic-scoped research run folder, where all claims are grounded by references.
5. Producing a companion literature-map block diagram that shows how the main papers or paper families connect.

Prerequisites

• papercli is installed and available in PATH.

Non-Negotiable Rules

• Use papercli as the retrieval backbone.
• Read paper content from downloaded PDFs whenever possible.
• Never present uncited factual claims.
• Surface conflicts and uncertainty explicitly.
• Final output must be a detailed markdown file named findings.md inside the active research run folder.
• Each distinct topic must live in its own dated, topic-slugged folder under research/.
• Maintain research/INDEX.md and the run-local RUN.md metadata file so later agents can recognize what each research folder contains.
• After synthesis, produce a companion literature-map diagram through the shared block-diagram skill.
• The literature map must only show evidence-backed relations such as method lineage, direct comparison, shared benchmark or dataset, critique, or common problem framing.
• Do not invent paper-to-paper influence or citation edges that are not supported by the corpus.
• OpenColab normally provides OPENCOLAB_PROGRESS_FILE during provider runs. When it is set, emit bounded JSON progress updates for long-running stages instead of remaining silent until the end.

OpenColab Progress Helper

OpenColab exposes this progress channel by default during provider runs. When OPENCOLAB_PROGRESS_FILE is available, use this helper:

emit_progress() {
  if [ -z "${OPENCOLAB_PROGRESS_FILE:-}" ]; then
    return 0
  fi
  printf '%s\n' "$1" >> "$OPENCOLAB_PROGRESS_FILE"
}

Write one-line JSON events. Allowed kind values are started, progress, milestone, warning, needs_input, and completed.

Example:

emit_progress '{"kind":"progress","stage":"download","slot":"search","current":8,"total":12,"message":"Downloaded 8 of 12 PDFs."}'

Let the agent decide what is worth sending. Use progress for countable ongoing work, milestone for stage changes, warning for degraded runs, needs_input for blockers, and completed when an explicit completion event helps. Do not narrate every minor command.

Topic-Scoped Research Workspace

Every run must use an active run root:

• New topic: research/<YYYY-MM-DD>-<topic-slug>/.
• Topic slug: lowercase ASCII, hyphenated, 3-8 meaningful words, and specific enough to distinguish the topic from nearby research.
• Collision rule: if the folder exists for different work, append -2, -3, or another short disambiguator.
• Continuation rule: reuse an existing folder only when the user asks to continue the same topic or the folder clearly matches the current request.
• Root catalog: update research/INDEX.md when the run starts and again when it finishes, is blocked, or is left partial.
• Run metadata: create and update <RUN_ROOT>/RUN.md with topic, question, skill, status, created/updated timestamps, corpus counts, generated artifact paths, and follow-up notes.

Recommended research/INDEX.md columns:

| Folder | Skill | Topic | Status | Created | Updated | Corpus | Deliverables | Notes |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |

Recommended <RUN_ROOT>/RUN.md headings:

# Research Run: <topic>

## Metadata

- Skill: deep-research
- Status: in-progress
- Created:
- Updated:
- Topic slug:
- Question:

## Corpus

- Candidate:
- Deep-read:
- Downloaded:
- Summarized:
- Failure events:

## Artifacts

- Findings:
- Literature map:
- Search files:
- Metadata:
- PDFs and summaries:
- Tables:

## Notes

Recommended Corpus Size

• Candidate set: 50-100 papers.
• Deep-read set: 40-60 papers.
• If access constraints reduce coverage, document the shortfall in the report.

End-to-End Workflow

1. Scope and evaluation design

Define:

• Research question(s).
• Inclusion/exclusion criteria.
• Comparison axes (data, methods, metrics, assumptions, compute, robustness).
• Time split (foundational vs. recent papers).

2. Multi-wave retrieval with papercli

Create workspace:

TOPIC_SLUG="<topic-slug>"
RUN_ROOT="research/$(date +%F)-${TOPIC_SLUG}"
mkdir -p "$RUN_ROOT"/{search,meta,pdf,tables,diagrams}
printf "stage\tid\treason\n" > "$RUN_ROOT/meta/failures.tsv"
: > "$RUN_ROOT/meta/downloaded_ids.txt"
: > "$RUN_ROOT/meta/summarized_ids.txt"

Before retrieval, add or update the row for $RUN_ROOT in research/INDEX.md with status in-progress, and create or update $RUN_ROOT/RUN.md.

Run at least 4 waves:

1. Core terminology.
2. Synonyms and adjacent terminology.
3. Method families.
4. Recent trend and benchmark-focused search.

papercli search "<core query>" --provider all --sort relevance --limit 30 --format json --out "$RUN_ROOT/search/w1_core.json"
papercli search "<adjacent query>" --provider all --sort relevance --limit 30 --format json --out "$RUN_ROOT/search/w2_adjacent.json"
papercli search "<method family query>" --provider all --sort relevance --limit 30 --format json --out "$RUN_ROOT/search/w3_methods.json"
papercli search "<benchmark/trend query>" --provider all --sort date --year-from <recent_year> --limit 30 --format json --out "$RUN_ROOT/search/w4_recent.json"

Optional citation-hub expansion through author trails:

papercli author "<influential author>" --provider all --sort relevance --limit 20 --format json --out "$RUN_ROOT/search/author_1.json"
papercli author "<contrasting author>" --provider all --sort relevance --limit 20 --format json --out "$RUN_ROOT/search/author_2.json"

3. Candidate consolidation and screening

jq -r '.[].id' "$RUN_ROOT"/search/*.json | awk 'NF && !seen[$0]++' > "$RUN_ROOT/meta/candidate_ids.txt"

Screen candidates for:

• Relevance to user question.
• Methodological diversity.
• Dataset/benchmark coverage.
• Publication-year balance.

Write selected IDs to $RUN_ROOT/meta/deep_read_ids.txt.

4. Metadata enrichment and bulk download

while read -r id; do
  safe_id="$(echo "$id" | tr '/:' '__')"

  if ! papercli info "$id" --provider all --format json --out "$RUN_ROOT/meta/${safe_id}.json"; then
    printf "info\t%s\tmetadata lookup failed\n" "$id" >> "$RUN_ROOT/meta/failures.tsv"
  fi

  if papercli download "$id" --provider all --out "$RUN_ROOT/pdf/${safe_id}.pdf"; then
    printf "%s\n" "$id" >> "$RUN_ROOT/meta/downloaded_ids.txt"
  else
    printf "download\t%s\tpdf download failed\n" "$id" >> "$RUN_ROOT/meta/failures.tsv"
  fi
done < "$RUN_ROOT/meta/deep_read_ids.txt"

5. Create agent-ready paper summaries

Delegate the summary phase to the paper-summary skill so the deep workflow uses the same canonical schema and batch summarizer as the other research skills.

Run it after the deep-read PDFs and metadata are ready:

python3 SKILLS/paper-summary/scripts/gemini_parallel_summary.py \
  --pdf-dir "$RUN_ROOT/pdf" \
  --metadata-dir "$RUN_ROOT/meta" \
  --summarized-ids "$RUN_ROOT/meta/summarized_ids.txt" \
  --failures-tsv "$RUN_ROOT/meta/failures.tsv" \
  --concurrency 20

Retry one paper with:

python3 SKILLS/paper-summary/scripts/gemini_parallel_summary.py \
  --pdf "$RUN_ROOT/pdf/<safe_id>.pdf" \
  --metadata-dir "$RUN_ROOT/meta" \
  --summarized-ids "$RUN_ROOT/meta/summarized_ids.txt" \
  --failures-tsv "$RUN_ROOT/meta/failures.tsv"

Summary requirements:

• Use the canonical schema in SKILLS/paper-summary/references/summary_schema.md.
• Write each summary to $RUN_ROOT/pdf/<safe_id>.md.
• Treat figures, captions, tables, appendix visuals, equations, and page anchors as first-class evidence.
• Mark metadata-only evidence explicitly when the PDF cannot be analyzed directly.
• Record summary failures in $RUN_ROOT/meta/failures.tsv and keep the corpus moving.

6. Cross-paper synthesis

Build at least these comparative artifacts inside findings.md:

• Taxonomy table (approach families).
• Results table (metrics and conditions).
• Assumption table (where methods break).
• Equation registry (important formulas and interpretation).

Then analyze:

• Consensus patterns.
• Contradictions and likely causes.
• Gaps and open problems.
• Most defensible practical recommendations.
• Use the structured paper summaries in $RUN_ROOT/pdf/ as the canonical source for cross-paper comparison.

7. Produce literature-map block diagram

Delegate this step to the shared block-diagram skill. It owns the canonical D2 source, render, validation, and diagram-file delivery flow.

Diagram requirements:

• Base the diagram on the same corpus and [R#] references used in findings.md.
• Show how the main papers, method families, benchmark clusters, or critique branches connect through evidence-backed relations only.
• Prefer compact family clusters when a flat per-paper graph would be noisy.
• Use a topic-derived slug such as <topic-slug>-literature-map under $RUN_ROOT/diagrams/.
• Prefer png as the primary delivered literature-map artifact.
• Keep svg as the editable or fallback artifact when PNG rendering is unavailable.

Key Math Protocol

• Extract 5+ important equations across the corpus when available.
• Write equations in plain-text markdown, not LaTeX blocks.
• Prefer ASCII-friendly math so the output stays readable in raw markdown and easy to parse by tools.
• Use a consistent three-line pattern:
  • Equation: <name> = <plain-text formula> [R#]
  • Where: <symbol> = <meaning>; ...
  • Interpretation: <what the equation does, why it matters, and any assumptions> [R#]
• Explain each equation in domain terms, not only symbol definitions.
• Attach at least one citation per equation explanation.

Example:

Equation: ELBO = E_q_phi(z | x)[log p_theta(x | z)] - KL(q_phi(z | x) || p(z)) [R5]
Where: x = observed input; z = latent variable; q_phi = approximate posterior; p_theta = decoder; KL = Kullback-Leibler divergence.
Interpretation: This objective trades reconstruction fidelity against posterior regularization, which shapes representation quality and generative calibration [R5].

Output Contract (findings.md)

Use this exact top-level structure:

# Findings: <topic>

## Executive Answer

Direct answer to the user question with confidence-qualified claims [R#].

## Scope and Method

- Question framing
- Inclusion/exclusion criteria
- Corpus stats (candidate count, deep-read count, downloaded count, summarized count, failure-event count)

## Literature Map

| Ref | Paper | Year | Method family | Evidence depth |
| --- | ----- | ---- | ------------- | -------------- |
| R1  | ...   | ...  | ...           | pdf-read       |

## Core Ideas and Concepts

Deep synthesis paragraphs with inline refs [R#].

## Quantitative Evidence

| Ref | Dataset/Setting | Metric | Reported result | Notes |
| --- | --------------- | ------ | --------------- | ----- |
| R3  | ...             | ...    | ...             | ...   |

## Key Math and Mechanisms

Equation: <name> = <plain-text formula> [R#]
Where: <symbol> = <meaning>; ...
Interpretation and implications [R#].

## Agreements, Conflicts, and Uncertainty

- Agreement:
- Conflict:
- Sources of uncertainty:

## Recommendations and Research Gaps

- What is ready to use now.
- What needs further validation.
- High-value open research directions.

## References

| Ref | Title | Authors | Year | Provider ID | Local evidence                            |
| --- | ----- | ------- | ---- | ----------- | ----------------------------------------- |
| R1  | ...   | ...     | ...  | ...         | `meta/...json`, `pdf/...md`, `pdf/...pdf` |

Companion literature-map artifacts:

• $RUN_ROOT/diagrams/<topic-slug>-literature-map.d2
• $RUN_ROOT/diagrams/<topic-slug>-literature-map.png
• optional $RUN_ROOT/diagrams/<topic-slug>-literature-map.svg

Final Chat Reply

After writing $RUN_ROOT/findings.md, return a short, friendly summary for the user-facing chat reply. Keep findings.md as the full canonical report and do not change its structure.

• Use an executive-summary tone that still reads well in chat.
• Light emoji use is allowed when it makes the message easier to scan.
• Include:
  • one direct-answer line
  • one coverage line with candidate, deep-read, downloaded, summarized, and failure counts
  • one short literature-map line explaining how the main papers or paper families connect
  • 3-5 cited takeaways covering the strongest findings and the main disagreements
  • one short uncertainty or risk line when it materially affects the recommendation
  • one closing line that points to $RUN_ROOT/findings.md for the full evidence base
• Do not paste the full literature map, quantitative tables, or long report sections into chat.
• If the active channel supports returning files, return findings.md plus the PNG literature-map diagram after the summary. If PNG rendering is unavailable, return the SVG artifact instead.

Referencing Standard

• Use [R1], [R2], ... inline everywhere factual.
• Tables must include citations in relevant cells.
• For numerical claims, cite source paper(s) in the same sentence or cell.
• Do not add a claim if evidence is not present in metadata, the PDF, or the structured summary.

Quality Gate Before Finish

Before finalizing findings.md, verify:

1. All major sections are present.
2. Every analytical claim has citations.
3. Math section uses plain-text equations plus interpretation.
4. Conflicting evidence is surfaced, not hidden.
5. References map to real downloaded/local files.
6. Each deep-read paper has an agent-ready summary in $RUN_ROOT/pdf/ unless extraction failed.
7. Downloaded and summarized counts reconcile with $RUN_ROOT/meta/downloaded_ids.txt and $RUN_ROOT/meta/summarized_ids.txt, and failure events reconcile with $RUN_ROOT/meta/failures.tsv.
8. A PNG literature-map artifact exists, or an SVG fallback is returned when PNG rendering is unavailable, and the diagram only shows evidence-backed cross-paper connections.
9. research/INDEX.md and $RUN_ROOT/RUN.md are updated with final status, corpus counts, artifact paths, and any limitations or next-step notes.