Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision…
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OpenLAIR/dr-claw ds-rebuttal --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ds-rebuttal .claude/skills/ds-rebuttal && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .claude/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OpenLAIR/dr-claw ds-rebuttal --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ds-rebuttal .agents/skills/ds-rebuttal && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .agents/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OpenLAIR/dr-claw ds-rebuttal --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ds-rebuttal .cursor/skills/ds-rebuttal && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .cursor/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/OpenLAIR/dr-claw.git --path skills/ds-rebuttal--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OpenLAIR/dr-claw ds-rebuttal --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ds-rebuttal .gemini/skills/ds-rebuttal && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .gemini/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install OpenLAIR/dr-claw ds-rebuttalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ds-rebuttal .github/skills/ds-rebuttal && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .github/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OpenLAIR/dr-claw ds-rebuttal --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ds-rebuttal .opencode/skills/ds-rebuttal && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ds-rebuttal" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-rebuttal into .opencode/skills/ds-rebuttal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-rebuttal", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ds-rebuttalA skill your agent uses when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision…
Ds Rebuttal is an agent skill from OpenLAIR/dr-claw. Use when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision response.
Its SKILL.md is about 5.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/action-plan-template.md`, `references/evidence-update-template.md` and `references/response-letter-template.md`).
It sits in Research & Science. The repository describes itself as: A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit d51b64e. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Ds Rebuttal loads about 5.2k tokens when it runs, and up to ~7k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 2,629 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from OpenLAIR/dr-claw at commit d51b64e, republished under its MIT licence (© OpenLAIR). 2,629 words, ~5,214 tokens.
.claude/skills/ds-rebuttal/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Use this skill when the quest is in review, revision, or rebuttal mode.
This is not the same as ordinary write.
The task is no longer “draft the paper from evidence”.
The task is “respond to concrete reviewer pressure with the smallest honest set of experiments, text changes, claim adjustments, and response artifacts”.
artifact.interact(kind='milestone', reply_mode='threaded', ...) update that says what reviewer concerns are now addressed, what still remains open, and what happens next.bash_exec.rebuttal is an auxiliary orchestration skill for review-driven work.
It should convert reviewer material into a durable response workflow:
analysis-campaign only after the analysis step says they are truly neededwriteDefault rebuttal stance: analysis before execution.
Do not jump from “reviewer asked for more evidence” straight to experiments.
Do not invent rebuttal-only special tools or side workflows.
Stay inside the normal DeepScientist surface: memory, artifact, bash_exec, plus ordinary stage/companion skills.
First decide whether the issue is actually:
startup_contract.custom_profile = revision_rebuttalstartup_contract.baseline_execution_policy is present, honor it:must_reproduce_or_verifyreuse_existing_onlyskip_unless_blockingstartup_contract.manuscript_edit_mode = latex_required, treat the provided LaTeX tree or paper/latex/ as the preferred writing surface when manuscript revision is needed.latex_required is requested, do not pretend the manuscript was edited; produce LaTeX-ready replacement text and an explicit blocker note instead.Use, in roughly this order:
evaluation_summary blocks from recent main experiments and analysis slicesIf the current paper/result state is still unclear, open intake-audit first before continuing the rebuttal workflow.
Before launching any new supplementary experiment, read those structured evaluation_summary blocks first so the rebuttal plan starts from the already-recorded evidence state rather than from raw narrative memory.
If the user provided manuscript files or review-packet files directly, first normalize them into durable quest-visible paths under paper/ or paper/rebuttal/input/ before planning reviewer-linked experiments or draft replies.
The rebuttal pass should usually leave behind:
paper/rebuttal/review_matrix.mdpaper/rebuttal/action_plan.mdpaper/rebuttal/response_letter.mdpaper/rebuttal/text_deltas.mdpaper/rebuttal/evidence_update.mdpaper/paper_experiment_matrix.md when reviewer concerns materially change the paper experiment planpaper/paper_experiment_matrix.json when reviewer concerns materially change the paper experiment planUse the templates in references/ when needed:
review-matrix-template.mdaction-plan-template.mdresponse-letter-template.mdevidence-update-template.mdBefore any rebuttal experiment or major rewrite, normalize reviewer pressure into stable atomic items.
For each item:
R1-C1, R1-C2, R2-C1missing_evidence if the gap is still realtext_revisionevidence_repackagingliterature_positioningbaseline_recoverysupplementary_experimentclaim_downgradeexplicit_limitationDo not let one vague reviewer paragraph remain as one vague work item. The point is to make downstream routing auditable.
Every substantive reviewer comment should be classified as one or more of:
editorialtext_onlyevidence_gapexperiment_gapclaim_scopecannot_fully_addressDo not blur these categories. The whole point is to route work correctly.
Useful stance values for draft replies:
agreepartially_agreeclarifyrespectful_disagreeUseful concern-type labels when the simple class list is not enough:
non_experimentalexperimentalwriting_logicscope_noveltyCollect reviewer inputs into a durable matrix using references/review-matrix-template.md.
For each comment, record:
R1-C1If the user gave only rough prose rather than a structured review package, build that matrix yourself before planning experiments or edits.
For each reviewer issue, decide whether the right answer is:
Then write one durable rebuttal plan in paper/rebuttal/action_plan.md.
That plan should explicitly include the analysis-experiment TODO list for reviewer-linked follow-up work.
If reviewer concerns materially change the paper's experiment story, also create or revise paper/paper_experiment_matrix.* so the rebuttal experiment package stays consistent with the paper-facing plan rather than drifting into a reviewer-only side list.
The action plan should be the main thinking draft before execution. For each serious item, record:
For experimental items, do not stop at “run experiment”. Write at least:
For novelty / comparison / positioning complaints, do not default to experiments. First decide whether the issue is better answered by a focused literature audit and clearer paper positioning.
When a reviewer concern really does imply experimental follow-up, map it into the same paper experiment taxonomy used by the writing line:
component_ablationsensitivityrobustnessefficiency_costhighlight_validationfailure_boundarycase_study_optionalCase study remains optional unless the reviewer concern is specifically qualitative and cannot be addressed better with quantitative evidence.
If one or more comments truly require new runs:
scout first instead of treating it as an experiment requestbaseline firstdecision(action='launch_analysis_campaign')analysis-campaignartifact.record_analysis_slice(...)Do not launch a free-floating ablation batch.
Every supplementary run should answer a named reviewer issue.
Every slice should reference one or more stable reviewer item ids.
Every rebuttal-linked slice should also reference the corresponding exp_id from paper/paper_experiment_matrix.* when that matrix exists.
After each completed reviewer-linked slice, record the result, the implication for the manuscript, and the concrete modification advice in paper/rebuttal/evidence_update.md.
Use the same shared supplementary-experiment protocol as ordinary analysis work; do not invent a rebuttal-only experiment system.
If ids or refs are unclear, recover them first with artifact.resolve_runtime_refs(...), artifact.get_analysis_campaign(...), or artifact.list_paper_outlines(...).
After each completed, excluded, or blocked reviewer-linked slice:
paper/paper_experiment_matrix.*exp_idDo not finalize the rebuttal package while reviewer-critical and currently feasible matrix rows remain unresolved without an explicit blocker note.
If the paper text, structure, or claim scope must change:
writetext_deltas.md explicit:If a reviewer request forces a narrower story, revise the outline before polishing prose.
Use references/response-letter-template.md when helpful.
Before treating the response letter as final:
paper/paper_experiment_matrix.* have been refreshed after those runsThe response should be:
Good response structure:
Drafting style rules for the actual author reply body:
response_letter.md as rebuttal-ready author text, not as internal coaching notes.startup_contract.manuscript_edit_mode = latex_required, keep manuscript-facing replacement text LaTeX-ready.If details are still genuinely unknown, use explicit placeholders such as [[AUTHOR TO FILL]] rather than inventing specifics.
Avoid:
When the rebuttal package is durably ready:
artifact.submit_paper_bundle(...)If a combined rebuttal note is useful, make sure the total package still covers:
Open additional skills only when the rebuttal workflow requires them:
intake-auditscoutbaselineanalysis-campaignwritefigure-polishdecisionUse these tools deliberately:
artifact.record(payload={'kind': 'decision', ...})artifact.create_analysis_campaign(...)artifact.record_analysis_slice(...)artifact.submit_paper_outline(mode='revise', ...)artifact.submit_paper_bundle(...)artifact.interact(...)Stage-start requirement:
memory.list_recent(scope='quest', limit=5)memory.search(...) for:Stage-end requirement:
memory.write(...)Useful tags include:
stage:rebuttaltype:review-matrixtype:claim-downgradetype:revision-lessontype:reviewer-requestrebuttal is successful when:
The goal is not just “write a nicer response”. The goal is to convert review pressure into a durable, auditable revision workflow.
© OpenLAIR, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (references) in skills/ds-rebuttal of OpenLAIR/dr-claw.
Open the folder on GitHubat commit d51b64e
Ds Rebuttal next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ds Rebuttal this skillOpenLAIR/dr-claw | 1.2k | — | ~5.2k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
OpenLAIR/dr-claw
Analyzes reviewer comments and drafts venue-specific rebuttals for AI and computer science conferences, with an issue board, task list and paper edit plan.
OpenLAIR/dr-claw
Turns a research paper into a slide deck and, optionally, a narrated demo video, through script, slide generation, text-to-speech and video assembly stages you control.
OpenLAIR/dr-claw
Clusters the latest news-feed results by topic and writes a briefing of research idea seeds with citations, plus a structured seeds file, without crawling new sources.
OpenLAIR/dr-claw
Searches Hugging Face Hub, OpenML, GitHub and paper references for datasets that fit a research task and returns a ranked, de-duplicated table.
OpenLAIR/dr-claw
Runs multi-source web research through Google's Gemini Deep Research Agent with a bundled Python script and saves a structured, cited report as files.
OpenLAIR/dr-claw
Six-phase workflow for writing, revising and adapting grant proposals for NSF, NIH, DOE, DARPA, NASA and China's NSFC, from profiling through simulated peer review.
Categories
A skill your agent uses when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision…. Ds Rebuttal is an agent skill from OpenLAIR/dr-claw. Use when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision response.
Ds Rebuttal fits situations like: A quest already has a paper; review package and the task is to map reviewer feedback into experiments; manuscript deltas; A durable rebuttal / revision response.
Run `npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a claude-code`. Or copy the skill folder (skills/ds-rebuttal in OpenLAIR/dr-claw) into .claude/skills/ds-rebuttal in your project. Claude Code loads it when a task matches its description.
Run `npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a codex`. Or copy the skill folder (skills/ds-rebuttal in OpenLAIR/dr-claw) into .agents/skills/ds-rebuttal in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenLAIR/dr-claw --skill ds-rebuttal -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ds-rebuttal, .gemini/skills/ds-rebuttal, .github/skills/ds-rebuttal and .opencode/skills/ds-rebuttal in your project.
SKILL.md names no scripts, command-line tools or credentials: Ds Rebuttal is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Ds Rebuttal is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.2k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Ds Rebuttal: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
OpenLAIR (a GitHub organization) maintains it in OpenLAIR/dr-claw, which has 1,155 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on September 17, 2026.
Source: OpenLAIR/dr-claw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.