Grants
alirezarezvani/claude-skills
NIH grant research skill for clinical researchers. An agent skill from alirezarezvani/claude-skills.
中文申请书五阶段研究与写作:课题准备、文献调研、方案制定、大纲规划、正文写作. An agent skill from dashbear-ai/grant-master.
$ npx skills add dashbear-ai/grant-master --skill auto -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dashbear-ai/grant-master auto --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/dashbear-ai/grant-master.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto .claude/skills/auto && 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 "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .claude/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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/dashbear-ai/grant-master/tree/main/skills/autoType 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 dashbear-ai/grant-master --skill auto -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dashbear-ai/grant-master auto --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dashbear-ai/grant-master.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/auto .agents/skills/auto && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .agents/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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 dashbear-ai/grant-master --skill auto -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dashbear-ai/grant-master auto --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dashbear-ai/grant-master.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/auto .cursor/skills/auto && 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 "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .cursor/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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/dashbear-ai/grant-master.git --path skills/auto--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 dashbear-ai/grant-master --skill auto -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dashbear-ai/grant-master auto --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dashbear-ai/grant-master.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/auto .gemini/skills/auto && 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 "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .gemini/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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 dashbear-ai/grant-master autoInstalls 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 dashbear-ai/grant-master --skill auto -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dashbear-ai/grant-master.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/auto .github/skills/auto && 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 "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .github/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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 dashbear-ai/grant-master --skill auto -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dashbear-ai/grant-master auto --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dashbear-ai/grant-master.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/auto .opencode/skills/auto && 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 "auto" agent skill from https://github.com/dashbear-ai/grant-master/tree/main/skills/auto into .opencode/skills/auto/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto", 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.
auto中文申请书五阶段研究与写作:课题准备、文献调研、方案制定、大纲规划、正文写作. An agent skill from dashbear-ai/grant-master.
Auto is an agent skill from dashbear-ai/grant-master. 中文申请书五阶段研究与写作:课题准备、文献调研、方案制定、大纲规划、正文写作。 用户要求使用 Grant Master、推进申请书、恢复已有项目,或询问本 skill 如何使用时触发。 默认打开 Codex 内置工作台;研究在当前对话进行,网页展示资料并收集用户决策。
Its SKILL.md is about 710 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/workbench-api.md`).
The repository describes itself as: 中文项目申请书全流程写作工具链 Grant-Master,支持课题理解、文献调研、论文精读、方案收敛、大纲规划、正文写作、审阅与 docx 输出。 The licence is MIT.
Read from SKILL.md and the folder at commit 23c4948. 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.
Shell commands in SKILL.md call:
python3From 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.
Auto loads about 714 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 35 tokens; SKILL.md has 119 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 dashbear-ai/grant-master at commit 23c4948, republished under its MIT licence (© dashbear-ai). 119 words, ~714 tokens.
.claude/skills/auto/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.直接启动工作台 · Linux/WSL:运行 python3 ../../workbench/launch.py。无需先请求 AI。
首次使用或恢复时明确告诉用户:主流程在 Codex 的 AI 对话框中进行,网页只作为报告展示、资料编辑和辅助交互。请配合 Codex 一起使用;AI 停止后,请回到此对话发送“继续”。
即使用户只是询问用法,也先运行插件的 workbench/launch.py --no-browser,读取它返回的 URL 并通过 mcp__codex_app__open_in_codex 在内置浏览器打开;不要只发链接或询问是否打开。用户明确不打开时尊重其选择。只问用法时介绍 Demo,不创建项目或启动研究。
每次对话开始,hook 只检查小型修改索引;发现未读记录后,先运行 gm.py changes --project ID,按 more 逐页读到 false,理解并响应用户修改,再继续研究。没有 hook 提示时,恢复项目仍通过 context 核对 userRevision 与 changesReadRevision。读取回执仅表示已接收,不代表修改要求已经完成;把未完成工作说明留在研究报告或待办中,不通过手写状态绕过后端。
进入阶段时先读取 context 返回的 stageGuidance 中对应文件。这是网页“本阶段研究经验”展示的同一份长期共用指导;文献调研前运行 gm.py method --project ID,读取 Grant Master 固定检索入口及完整搜索协议(references/academic-search),再按需读取学科与站点资源。CLI 回执绑定入口和核心协议内容;修改后需重新读取。研究报告说明实际采用的方法步骤和证据范围,不能只说“已遵循”。方法只提供研究规则,其外部操作仍受用户授权与可用工具约束。
研究文件以工作台返回的项目目录为准,与 Codex 当前工作目录无关。用户新建项目默认位于 ~/.grant-master/<随机项目编号>/;位置可更改。原始资料、下载论文、候选稿和交付物都放入该项目,外部课题资料先导入副本。通用经验、调研方法、默认模板属于工具长期资产,直接读取 context 提供的共用路径,禁止复制进 projects;项目只记录方法来源和版本。只读取当前阶段需要的报告,需要核实具体论据时再追溯原件。
目标字数:N 字,发布时传当前 outlineStatus.revision 为 outline_revision。用户修改后的三个视图可暂时不同,AI 恢复后负责协调;不把网页不一致提示作为下一步入口。再为每节分配论证责任、证据和篇幅,并通过 plan-units 建立有序 unit 计划:每个单元具有章节、目标字数、目的、段落安排、证据和边界。章节引言、标题与叶子章节都要有明确归属,确认大纲前完成单元预算校验。大纲必须能读出完整推理链,避免重复背景、同一创新点多处改写以及空泛标题。使用 workbench/gm.py 的 context 读取资产、校验状态和未处理回答。currentStage 表示最早需要重新验证的位置,不代表用户退回了研究阶段;先响应最新修改,不机械重跑已有研究。继续时先处理已提交回答和拒绝,处理完成后确认;已有问题继续等待原问题,不重复投递。用户只要求当前一步时止于该范围,要求完整流程时持续推进至交付。
内容交付及等待的具体命令见 工作台调用,按需读取。你只提供研究内容、判断和待确认的问题;项目状态、资产索引、版本和事件记录由后端创建和维护,不手写状态文件。
需要用户参与时向统一待办中心投递解释、背景、选项和自由输入,等待真实回答;用户拒绝不视为同意。网页保存动作不启动模型。回答在停止期间也会保留,下次恢复后接收。不能把“请去网页点击下一步”当作本次研究已完成。
© dashbear-ai, 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 1 other file (references) in skills/auto of dashbear-ai/grant-master.
Open the folder on GitHubat commit 23c4948
Auto 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 |
|---|---|---|---|---|---|---|
| Auto this skilldashbear-ai/grant-master | 125 | — | ~714 | Automated safety check: Pass | MIT | |
| Grantsalirezarezvani/claude-skills | 28k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Research GrantsK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.3k | Automated safety check: Notes | MIT | |
| Grantsborghei/Claude-Skills | 886 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Pp Grantsmvanhorn/printing-press-library | 2.1k | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Grant Proposalmohitagw15856/pm-claude-skills | 1.4k | — | ~1.1k | Automated safety check: Pass | MIT |
alirezarezvani/claude-skills
NIH grant research skill for clinical researchers. An agent skill from alirezarezvani/claude-skills.
K-Dense-AI/scientific-agent-skills
Supports research proposal preparation and review for NSF, NIH, DOE, DARPA, and Taiwan NSTC, including opportunity-specific requirements, aims, review criteria, budgets, broader impacts, forms, and…
borghei/Claude-Skills
Grant writing and proposal architecture: funder fit, proposal structure, budget design, and success-factor scoring.
mvanhorn/printing-press-library
Find open US federal research funding and benchmark award sizes — Grants.gov open opportunities plus awarded NIH RePORTER and NSF grants, keyless.
mohitagw15856/pm-claude-skills
Write a structured grant proposal or funding application for any grant type.
marin-community/marin
Review an explicitly identified marin-iac grant PR that edits IAM data or a deploy-target module, confirm its decrypted principals and roles, then apply only the confirmed grant.
dashbear-ai/grant-master
Grant Master 文献调研阶段的多源检索、两遍筛选、开放全文获取和元数据合并方法. An agent skill from dashbear-ai/grant-master.
中文申请书五阶段研究与写作:课题准备、文献调研、方案制定、大纲规划、正文写作. An agent skill from dashbear-ai/grant-master. Auto is an agent skill from dashbear-ai/grant-master.
Run `npx skills add dashbear-ai/grant-master --skill auto -a claude-code`. Or copy the skill folder (skills/auto in dashbear-ai/grant-master) into .claude/skills/auto in your project. Claude Code loads it when a task matches its description.
Run `npx skills add dashbear-ai/grant-master --skill auto -a codex`. Or copy the skill folder (skills/auto in dashbear-ai/grant-master) into .agents/skills/auto 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 dashbear-ai/grant-master --skill auto -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto, .gemini/skills/auto, .github/skills/auto and .opencode/skills/auto in your project.
Going by SKILL.md and its folder, Auto needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
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.
Auto is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 714 tokens (SKILL.md is roughly 2.9k 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 1.7k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Auto: Grants (alirezarezvani/claude-skills, 28k stars), Research Grants (K-Dense-AI/scientific-agent-skills, 48k stars), Grants (borghei/Claude-Skills, 886 stars) and Pp Grants (mvanhorn/printing-press-library, 2.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
dashbear-ai (a GitHub organization) maintains it in dashbear-ai/grant-master, which has 125 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on September 9, 2026.
Source: dashbear-ai/grant-master on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.