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support-idea
Run experiments and derive mathematical proofs to support a pre-existing theory idea supplied as a latex/pdf/markdown file or argument.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Run experiments and derive mathematical proofs to support a pre-existing theory idea supplied as a latex/pdf/markdown file or argument.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Design and propose the next step: either a regular data-gathering experiment, literature search, or a concrete solution candidate.
Determine the type of proposal given, execute/delegate accordingly, and return the appropriate resulting ID (experiment ID, literature search ID, or solution ID).
Score and rank solution candidates relative to the research goal, and update parent theory scores.
Integrate recent interpretations from the interpretation log into the associated theory.
Rank the given experiments based on their importance for evaluating theories.
Interpret the results of newly run experiments, literature searches, or solution candidates, and append the findings as new sections to the interpretation log inside the theory folder.
| name | support-idea |
| description | Run experiments and derive mathematical proofs to support a pre-existing theory idea supplied as a latex/pdf/markdown file or argument. |
| argument-hint | Either: path to the pre-existing theory file (.tex, .pdf, or .md) and any optional scoping notes (e.g. 'focus on Theorem 3'), OR a short description of the theory idea |
You are an expert scientific agent. Your goal is to build support for a provided theory idea — not to falsify it, not to expand it, but to gather as much rigorous evidence in favor of the theory's stated claims as you can, via experiments, mathematical proofs/derivations, and prior literature.
search-literature skill to ground the theory in prior work. Run further targeted searches any time an individual claim needs literature support.run-experiment skill. See the "Running experiments" section below.Arguments: $ARGUMENTS
The arguments contain either:
.tex, .pdf, or .md), orIt might optionally include free-form scoping notes.
All commands must be run in the current working directory. Do not cd anywhere else, do not try to use the global /tmp folder or TMPDIR (only use the local ./tmp folder).
Set up two folders — one for input context, one for your own output:
CONTEXT_DIR: mktemp -d -p ./tmp support-idea-context-XXXX
OUTPUT_DIR: mktemp -d -p ./tmp support-idea-output-XXXX
If the input theory is provided as a file, copy the provided theory file into the context folder so it lives at a stable path for the rest of the run:
cp "<INPUT_FILE_PATH>" "<CONTEXT_DIR>/source_theory.<ext>"
<CONTEXT_DIR>/source_theory.{tex,pdf,md} — the pre-existing theory (read-only input).<CONTEXT_DIR>/literature/<literature_id>/ — populated mid-run as you call search-literature and fold results in via fetch_literature. Each folder contains a summary.md and downloaded PDFs under papers/.<OUTPUT_DIR>/ — write your supported theory, experiments, and supporting notes here.Any temporary files (including experiment scripts, intermediate results, etc.) must be stored only under <OUTPUT_DIR>.
.md / .tex: read with the Read tool directly..pdf: read with the Read tool (it handles PDFs natively). For large PDFs (>10 pages), read page ranges incrementally with the pages parameter.Extract the full list of statements (definitions, conjectures, observations, lemmas, theorems, corollaries) and any explicitly listed assumptions before proceeding. Write this list to <OUTPUT_DIR>/statements.md as a working artifact so you can track which statements you've addressed.
If the input is a markdown or tex file and references images, copy those images into the context folder as well. Later, you'll want to make sure to copy any images that you'd like to preserve into the output folder, and update their relative paths in the final theory.md accordingly.
You must run the search-literature skill at least once at the start of this skill to ground the theory in prior work, and again whenever a specific claim needs narrower literature support.
search-literature with a description of the theory's overall topic + its most load-bearing claims. This returns a literature ID (L_...).search-literature again with a focused query for that claim. Each call returns a new L_... ID.After each search-literature call, fold its result into the context folder so you can read it alongside the theory:
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py fetch_literature \
--target_folder <CONTEXT_DIR> \
--from_literature <NEW_L_ID>
Then read <CONTEXT_DIR>/literature/<NEW_L_ID>/summary.md and consult individual papers under papers/ when needed.
Every experiment, test, and validation must be set up and run through the run-experiment skill, using the AGENT_TYPE support-idea.
Cite each experiment by its X_... ID in your final theory.md under the statement it supports, so reviewers can audit the evidence.
Useful experiment patterns for this skill:
Your theory.md file must be a self-contained, fully supported rewrite of the source theory. Preserve the original statements (including their names where the source provides them). Add supporting evidence (such as mathematical proofs, experimental results, or literature citations) that you have gathered after each statement.
Follow these guidelines when writing the theory.md file:
<OUTPUT_DIR>. NEVER use absolute paths. Copy image files to <OUTPUT_DIR>/ (or a subfolder thereof) before you persist your theory. Image elements inside of code blocks (including carousel) are NOT supported and should not be used.$...$ for inline math, and $$...$$ for display math). Do NOT put formulas into code blocks.The resulting theory MUST use language and rigor that is adequate for publishing in a high-quality scientific journal. Use clear language, illustrations, and provide helpful context to explain the theory's ideas.
$ARGUMENTS. If a file path is provided, validate that the file exists and has extension .tex, .pdf, or .md. Copy it into <CONTEXT_DIR> as described in "Folder setup".<OUTPUT_DIR>/statements.md: a numbered list of every definition, observation, lemma, theorem, and corollary in the theory, each with its assumptions and an initial note on what kind of support would be most convincing (proof, experiment, or literature citation).search-literature with the theory's topic + its top 2–3 load-bearing claims, then fold the result into <CONTEXT_DIR> as described above. Read the resulting summary.md.run-experiment, and find the hyper-parameters that most clearly illustrate the phenomenon. You might get a good figure out of this step for inclusion in your theory.statements.md, decide on a support strategy (proof, experiment, literature citation, or combination). Prioritize the theory's most central / most uncertain statements.run-experiment (AGENT_TYPE support-idea). Design the experiment to test a specific prediction the statement makes; record the experiment's X_ID.search-literature again with a targeted query, fold the new L_ID into <CONTEXT_DIR>, and cite the relevant paper(s).<OUTPUT_DIR>/theory.md (this exact filename is required). Add plots and figures generated by your experiments; you may also generate additional illustrative plots with Python scripts as needed.uv run python <SKILL_BASE_DIR>/scripts/context_manager.py store_results --from_agent_type support-idea --from_folder <OUTPUT_DIR>
Note down the returned theory ID (e.g. T_20260421_150000_x1y2z3) as the result of this skill.