skills/60-regisely-superpapers/skills/execute-plan/SKILL.md
Use when a research implementation plan exists in docs/superpapers/plans/ and the user is ready to execute it — collecting data, running analysis, producing outputs, writing the paper. Orchestrates task execution with replication-driven verification and two-stage review at phase boundaries.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research execute-planInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill executes a research plan phase by phase. It starts by invoking academic-baseline as the standing policy layer, dispatches subagents for independent tasks, invokes replication-driven-research as a guardrail, and runs two-stage review at phase boundaries: correctness first, reproducibility second. It must honor each task's declared Skills involved field during execution rather than improvising generic behavior. Stops and asks the user at any scaffolding or destructive action. The skill never proceeds past a phase until both reviews pass.
docs/superpapers/plans/write-plan to implementationdocs/superpapers/plans/YYYY-MM-DD-<topic>-plan.mdreplication-driven-research has been invoked at least once in this project (to scaffold directories) or will be invoked as the first stepInvoke academic-baseline first. This resolves CLAUDE.superpapers.md via the walk-up Read (current working directory, then parent directories) and carries its settings into the session. Keep academic-baseline active through every phase.
Load and parse the plan. Read the plan file in full. Extract tasks, phases, dependencies, verification criteria, and Skills involved.
Invoke replication-driven-research to ensure project structure. If directories are missing, propose scaffolding. Wait for user confirmation before creating directories or files at the project root.
Pre-flight declaration before every task. Before executing any task, output a structured block:
## Task: <task title>
Phase: <phase name>
Skills (from routing table): <list from CLAUDE.superpapers.md table>
Skills (from plan): <list from plan's Skills involved field>
Will invoke: <union of both lists, no duplicates>
Invoke every skill in the union list before doing any task work. If paper-writing appears, confirm you are in the main session — never dispatch it to a subagent. If journal-guidelines appears, confirm the target journal is resolved — if not, invoke journal-selection first. If a task is missing a clearly necessary skill, stop and repair the plan before proceeding.
Enforce journal routing. Any task or review involving a target journal, author instructions, formatting, templates, blinding, cover letters, checklists, or submission portals must invoke journal-guidelines before work begins. If the outlet is not fixed yet, invoke journal-selection first, then journal-guidelines. Never declare journal compliance or submission readiness without this step.
Execute tasks phase by phase. Within a phase, independent tasks can be dispatched to subagents in parallel. Sequential tasks run in order.
Verify after every task. Run the task's verification command. If it fails, stop and diagnose — do not proceed silently.
Run end-to-end integration at each phase boundary. Execute the full pipeline from data/raw/ to the farthest artifact produced so far. Confirm exit code 0 and that all expected outputs exist.
Two-stage phase review:
replication-driven-research verification checklist.Only proceed to the next phase after both stages pass.
On failure: stop, diagnose root cause, fix, re-run from the failing task. Never skip failed tasks. Never re-run from scratch without understanding what broke.
Final full-pipeline run. Before declaring the plan complete, run everything from raw data to final PDF. All verifications must pass.
Report status to the user at phase boundaries. At the end, summarize what was built, what passed review, and where the artifacts live.
Suggest the pre-submission audit. After the final summary, recommend that the user run /superpapers:paper-review before submission. The paper-review skill performs a cross-cutting audit of prose, code, tables, figures, citations, and reproducibility and writes a consolidated report. This suggestion is non-blocking — do not auto-invoke and do not gate the plan's completion on it.
Use subagents for:
Do NOT use subagents for:
paper-writing skill is invoked in the main session for these tasks)academic-baseline is never optional during execution. If the work is a research task, keep it active.Skills involved field to decide which superpapers skills are active for a task. Do not freehand domain work with generic reasoning when the plan called for a specific skill.journal-guidelines. Formatting a manuscript for a named journal, checking a submission checklist, applying blinding rules, or adapting a template without journal-guidelines is invalid.academic-baselineSkills involved field and improvising the work genericallyjournal-guidelinesacademic-baseline invoked first and kept active during executionSkills involvedjournal-guidelines in the current sessiondata/manifest.md up to date with every dataset usedoutput/logs/ contains the latest execution logoutput/tables/, output/figures/, and paper/tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.