skills/42-wanshuiyin-ARIS/skills/result-to-claim/SKILL.md
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research result-to-claimInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Experiments produce numbers; this gate decides what those numbers mean. Collect results from available sources, get a Codex judgment, then auto-route based on the verdict.
Gather experiment data from whatever sources are available in the project:
wandb.Api().run("<entity>/<project>/<run_id>").history() — metrics, training curves, comparisonsssh server "tail -100 /path/to/training.log" if no other sourceAssemble the key information:
Send the collected results to Codex for objective evaluation:
mcp__codex__codex:
config: {"model_reasoning_effort": "xhigh"}
prompt: |
RESULT-TO-CLAIM EVALUATION
I need you to judge whether experimental results support the intended claim.
Intended claim: [the claim these experiments test]
Experiments run:
[list experiments with method, dataset, metrics]
Results:
[paste key numbers, comparison deltas, significance]
Baselines:
[baseline numbers and sources — reproduced or from paper]
Known caveats:
[any confounding factors, limited datasets, missing comparisons]
Please evaluate:
1. claim_supported: yes | partial | no
2. what_results_support: what the data actually shows
3. what_results_dont_support: where the data falls short of the claim
4. missing_evidence: specific evidence gaps
5. suggested_claim_revision: if the claim should be strengthened, weakened, or reframed
6. next_experiments_needed: specific experiments to fill gaps (if any)
7. confidence: high | medium | low
Be honest. Do not inflate claims beyond what the data supports.
A single positive result on one dataset does not support a general claim.
Extract structured fields from Codex response:
- claim_supported: yes | partial | no
- what_results_support: "..."
- what_results_dont_support: "..."
- missing_evidence: "..."
- suggested_claim_revision: "..."
- next_experiments_needed: "..."
- confidence: high | medium | low
no — Claim not supportedpartial — Claim partially supportedpartial on the same claim → record analysis in findings.md, consider whether to narrow the claim scope or switch ideasyes — Claim supported/ablation-plannerconfidence is low, treat the judgment as inconclusive and add experiments rather than committing to a claim.[pending Codex review] — do not block the pipeline.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.