Lit Review Orchestrator

Lit-Review Orchestrator

Run the literature-review pipeline from a single command, starting from a document that describes your article.

Input: a .tex or .docx document — a full manuscript, an abstract, or any text describing the article's content. Output: a deduplicated, relevance-screened master list (JSON + RIS), plus the extracted search plan and all intermediate stage files.

Quick Start

The orchestrator.py commands below are the autonomous fallback (reasoning on the Sonnet/DeepSeek API). When an agent runs this skill interactively, use the agent-driven flow instead; see How it runs below. That flow performs non-browser reasoning at the agent layer with no Anthropic API key, using the platform routing in docs/claude-code.md or docs/codex.md.

pip install -r requirements.txt                                  # one-time
cp lit-review-pipeline.env.example ~/.lit-review-pipeline.env    # then fill in keys

# From a full manuscript
python scripts/orchestrator.py paper.docx --output-dir ~/lit-reviews/mypaper

# From just an abstract (any .tex/.docx describing the article works)
python scripts/orchestrator.py abstract.tex --output-dir ~/lit-reviews/mypaper

# Add opt-in sources; DOI-only dedup
python scripts/orchestrator.py paper.tex --ssrn --nber --no-llm --output-dir out

# Escape hatch: run from a raw query string (skips Stage 0 extraction)
python scripts/orchestrator.py --query "dual-class shares cost of equity" --output-dir out

Pipeline

| Stage | Name | Default | What it does | |-------|------|---------|--------------| | 0 | Extract | on | Parse the document; the agent derives the research question, an Undermind brief, a Scholar Labs question, and Google Scholar queries | | 1 | Undermind | on | Automated Undermind.ai Classic deep search from the brief (Playwright; signs in with stored credentials) | | 2 | Scholar Labs | opt-in | Google Scholar Labs deep search via --scholarlabs (Playwright; stored Google login). Off by default — Google rate-limits its Cite/BibTeX export under automation, so it often defers | | 2b | Deep Research | on | Gemini Deep Research Agent (Interactions API; GEMINI_API_KEY) — alternative API-driven deep search | | 4a | Google Scholar | on | SearchAPI.io Google Scholar, driven by the extracted queries | | 4b | Supplementary | off | SSRN / NBER / HeinOnline / forthcoming (--ssrn --nber --heinonline --forthcoming) | | 4c | Citation chain | off | Semantic Scholar (--citation-chain; needs DOI-bearing seeds) | | 5 | Dedup | on | Merge all outputs; metadata enrichment + DOI and LLM fuzzy dedup | | 5b | Verify | on | Cross-check every paper against OpenAlex / Crossref / Semantic Scholar and drop any none can confirm (anti-hallucination); if an index outage leaves >30% of papers uncheckable, it keeps everything and warns instead of dropping. --no-verify keeps all; dropped papers saved to stage5_merged_unverified.json | | 6 | Screen | on | Abstract relevance screening against the research question |

Stage 0 runs first; Stages 1, 2b, and 4a run concurrently (Stage 2 Scholar Labs joins them only with --scholarlabs, and 4b when opted in); 4c follows them; then 5, then 6.

How it runs: agent-driven (default) vs autonomous fallback

LLM work follows one routing rule: the agent-driven flow uses no Anthropic API for the pipeline's reasoning steps. Stage 0 extraction, Stage 4 query condensation, Stage 5 dedup judgments, Stage 6 screening, keyless web search, and Undermind clarifying answers run at the agent layer. The exact model and delegation policy depends on the host agent:

• Claude Code: read docs/claude-code.md.
• Codex: read docs/codex.md.

Undermind is a subprocess that owns the live browser, so its clarifying questions come back to the agent through a small file handshake (--answers-dir): the driver writes each question to a file and types whatever answer you drop back. The Sonnet/DeepSeek API path remains the autonomous fallback (orchestrator.py) for unattended runs with no agent present.

Because a Python subprocess cannot spawn subagents, each reasoning script exposes an emit/ingest seam: the script does the deterministic work (parsing, candidate-pair generation, validation, enrichment, merge, all file output) and hands only the LLM step out to you in the middle. Each script also keeps its in-script Sonnet/DeepSeek API path as an autonomous fallback for unattended runs, so the same files support both interactive and unattended use.

Platform routing (agent-driven flow)

The shared pipeline uses named reasoning roles. Map those roles to the host platform before dispatching subagents or doing web-search work.

| Role / stage | Strong-reasoning route | Cost-conscious route | |--------------|------------------------|----------------------| | Orchestrator: coordinate the run, parse the GUI config, fan out, merge | Host platform's strongest interactive model | Parent session default | | Stage 0: extract the search plan | Strong-reasoning route | Avoid downgrading unless the user requests a fast pass | | Stage 4d: keyless web-search fan-out | Strong-reasoning route | Avoid downgrading; recall and precision matter | | Stage 6: relevance re-ranking | Strong-reasoning route | Avoid downgrading; this determines final ranking | | Stage 4a query writing, Stage 5 dedup judgments, Undermind clarifying answers | Strong-reasoning route when accuracy is prioritized | Cheaper platform worker model; escalate uncertain cases |

For Claude Code, the strong route is Opus 4.8 and the cheaper worker route is Sonnet 4.6. For Codex, the strong keyless route is gpt-5.5 with xhigh reasoning, and lower-stakes batch judgments use cheaper Codex workers such as gpt-5.4-mini at medium or high reasoning. See docs/claude-code.md and docs/codex.md for the complete mapping.

The GUI still emits the legacy field "all_opus". Interpret "all_opus": true as the high-accuracy platform profile: Claude Code uses Opus for all delegated reasoning, while Codex uses gpt-5.5 with xhigh reasoning for all delegated reasoning. When it is false or absent, follow the platform's default split.

Interactive entry (GUI)

When the skill is triggered interactively, open the settings dialog first, let the user choose the input and options, then run the agent-driven stages below honouring what it returns:

python scripts/lit_review_gui.py --config-out OUT/gui_config.json   # blocks until Run/Cancel

The window has a Browse field for the document (or a raw-query box), an output folder, the search channels as checkboxes — keyed (Undermind / Deep Research / Google Scholar checked; Scholar Labs unchecked — opt-in) and keyless (Free index search / Web search, both on) — supplementary sources (SSRN checked by default, NBER, HeinOnline) plus citation chaining, Processing (Deduplicate / Verify sources / Screen / DOI-only), and an Advanced group (Quick mode, Max chars, and a legacy Use Opus for all tasks toggle that is off by default. Leave it off to run the platform's default routing split; check it to request the high-accuracy route for every delegated reasoning stage. On Run it writes the settings to --config-out and echoes them to stdout between ===LITREVIEW_CONFIG_BEGIN=== and ===LITREVIEW_CONFIG_END=== (exit 0); Cancel or closing the window exits 2 — abort the run. Parse that JSON and map it onto the stages: skip a channel set false, set output_dir, run Scholar Labs / supplementary / citation and the keyless freesearch (Stage 4e) / websearch (Stage 4d) channels when true, pass --no-verify to dedup when verify is false and --no-llm when no_llm is true, skip dedup/screen when false, pass max_chars to extraction, and, when all_opus is true, use the high-accuracy platform profile instead of the default split (see Platform routing). The GUI runs nothing itself and calls no API. Shape:

{"document":"…","query":"","output_dir":"…",
 "channels":{"undermind":true,"deepresearch":true,"scholar":true,"scholarlabs":false,"freesearch":true,"websearch":true},
 "supplementary":{"ssrn":true,"nber":false,"heinonline":false},
 "citation_chain":false,"top_seeds":20,
 "dedup":true,"verify":true,"screen":true,"no_llm":false,"quick":false,"max_chars":30000,"all_opus":false}

Agent-driven run (the default; you orchestrate)

Pick an output dir OUT. Run the deterministic stages as subprocesses and do the reasoning stages with the host platform routing described above. In Codex, spawn subagents only after explicit user authorization for parallel agent work. Substitute ` and the extracted `.

Stage 0: extract (strong-reasoning route):

python scripts/extract_search_plan.py  --emit-prompt OUT/extract_prompt.txt -o OUT/search_plan.json
# Read OUT/extract_prompt.txt, produce the plan JSON with the platform's strong-reasoning route, write OUT/plan.json.
python scripts/extract_search_plan.py  --plan-file OUT/plan.json -o OUT/search_plan.json

The plan JSON must carry extract_search_plan.py's REQUIRED_KEYS; --plan-file validates them (exit 1 on a bad plan) and writes searchplan.json/.md, scholarqueries.json, undermindbrief.txt, scholarlabsquery.txt.

Stages 1 / 2b / 4a — search (subprocesses; run concurrently, background + Monitor). Stage 2 Scholar Labs is opt-in — run it only on request (see below):

python undermind-search/scripts/undermind_search.py --brief-file OUT/undermind_brief.txt \
    -o OUT/stage1_undermind.json --debug-dir OUT/debug_undermind --answers-dir OUT/undermind_clarify
# Agent-in-the-loop, no API: while it runs, watch OUT/undermind_clarify for clarify_request_.json.
# When one appears, answer the question with the platform worker route (strong route when all_opus is true), grounded in the
# brief and the Stage-0 undermind_clarifications, and write OUT/undermind_clarify/clarify_answer_.json = {"answer": "..."}.
# Practical pattern: launch a background job that blocks until the request file exists (so you are
# notified), answer it, then re-arm for the next turn. Undermind is interactive in this mode.
# Opt-in only (Scholar Labs): Google rate-limits its Cite export under automation, so skip it by
# default and run this line only when asked / retrying from a fresh session:
python scholarlabs-search/scripts/scholarlabs_search.py --query-file OUT/scholarlabs_query.txt \
    --research-question "" -o OUT/stage2_scholarlabs.json --hidden --debug-dir OUT/debug_scholarlabs
python deepresearch-search/scripts/deepresearch_search.py --query-file OUT/undermind_brief.txt \
    --research-question "" -o OUT/stage2b_deepresearch.json --debug-dir OUT/debug_deepresearch  # Gemini Deep Research (GEMINI_API_KEY), pure subprocess
python supplementary-search/scripts/supplementary_search.py --scholar \
    --queries-file OUT/scholar_queries.json -o OUT/stage4a_scholar.json --debug-dir OUT/debug_scholar

Passing --queries-file (the agent-written queries, using the platform worker route by default and the strong route when all_opus is true) bypasses the in-script condense_query fallback in supplementary-search. For a raw-query agent run (no document, hence no Stage 0 to produce scholar_queries.json), first have the agent write that file (a short JSON array of query strings) and pass it the same way, or add --no-condense. Either route keeps the agent path free of the in-script API call.

Web search (keyless agent-driven channel, and a useful add-on alongside the keyed channels). Subagent fan-out, so the raw web text stays out of your context. This is the keyless search route in Platform routing. Emit a batched task plan, dispatch one strong-reasoning subagent per batch when parallel agents are authorized, then merge:

python websearch-search/scripts/websearch_ingest.py --emit-tasks \
    --queries-file OUT/scholar_queries.json --research-question "" \
    --batch-size 3 -o OUT/websearch_tasks.json
# Dispatch one strong-reasoning worker per tasks[k]: hand it the system_prompt + its queries;
# each uses the platform's web-search/fetch tools over its queries and writes
# OUT/websearch_results_batch_.json (only title required; never invent fields; do
# not fetch scholar.google.com). Then merge the partials:
python websearch-search/scripts/websearch_ingest.py \
    --results OUT/websearch_results_batch_*.json -o OUT/stage4d_websearch.json

This writes stage4d_websearch.json (source="websearch"), deduped by title with best-effort keyless Crossref DOI fill, which the dedup --inputs glob below picks up. The hits are real web results, so keep Stage 5b verification ON. For a few queries you can skip the fan-out and ingest a single websearch_results.json. Empty input defers (WEBSEARCH_DEFERRED). Full recipe: websearch-search/SKILL.md.

Free index search (keyless; pairs with web search for the no-key fallback). A plain keyless subprocess that searches OpenAlex / Crossref / Semantic Scholar with the Stage-0 queries:

python freesearch-search/scripts/freesearch_search.py \
    --queries-file OUT/scholar_queries.json -o OUT/stage4e_freesearch.json

This writes stage4e_freesearch.json (real index records, source set per index), which the dedup --inputs glob below also picks up. No key needed; see freesearch-search/SKILL.md.

Stage 5: dedup (platform worker route by default; strong route when all_opus):

python lit-dedup/scripts/lit_dedup.py --inputs OUT/stage[0-9]*.json --emit-pairs OUT/dedup_pairs.json -o OUT/stage5_merged.json
# Read OUT/dedup_pairs.json; for each pairs[k] = {i, j, a, b} decide if a and b are the
# same paper. Fan out across parallel platform workers for large pair sets when authorized. Write
# OUT/dedup_verdicts.json = [{"i":N,"j":N,"decision":"yes|no","confidence":"high|medium|low","rationale":"..."}].
python lit-dedup/scripts/lit_dedup.py --ingest-verdicts OUT/dedup_pairs.json OUT/dedup_verdicts.json -o OUT/stage5_merged.json

Exclude stage5_* / stage6_* from the --inputs glob. If dedup_pairs.json has no pairs, write [] to the verdicts file and still run --ingest-verdicts.

Stage 6: screen (the re-ranker; strong-reasoning route):

python lit-screen/scripts/lit_screen.py --input OUT/stage5_merged.json --query "" \
    --emit-tasks OUT/screen_tasks.json -o OUT/stage6_screened.json
# Read OUT/screen_tasks.json = {system_prompt, query, tasks:[{index, user_message}]}.
# Score each task following system_prompt. Fan out across parallel strong-reasoning workers in batches when authorized. Write
# OUT/screen_results.json = [{"index":N,"relevance_score":1-10,"rationale":"..","paper_type":"..","identification_strategy":"..","relationship":".."}].
python lit-screen/scripts/lit_screen.py --input OUT/stage5_merged.json --ingest-results OUT/screen_results.json -o OUT/stage6_screened.json

Autonomous fallback (no agent)

python scripts/orchestrator.py  --output-dir OUT

Runs every stage end-to-end as subprocesses; the reasoning stages call the Anthropic API on Sonnet (DeepSeek for dedup). The keyless free index search (Stage 4e) runs here by default (--no-freesearch to skip); web search (Stage 4d) is agent-only and not available in this runner. Use it for unattended runs or when no agent is driving. It is the fallback, not the default.

Undermind (Stage 1)

Undermind runs automatically from the extracted brief. The driver (undermind-search/scripts/undermind_search.py) launches Playwright, signs in with the credentials in ~/.lit-review-pipeline.env, drives the Classic search (sidebar Classic → Search → brief → the agent answers the clarifying questions → Generate Research Report), waits for the report, and exports the references (BibTeX by default). undermind_ingest.py then parses and enriches them into stage1_undermind.json (+ .bib). First-time setup stores the credentials with `python undermin

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kennethkhoocy
• Source: kennethkhoocy/legal-scholarship-skills
• License: MIT

Install and usage instructions live in the source repository linked above.