This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Grant writing and proposal architecture: funder fit, proposal structure, budget design, and success-factor scoring. Use when writing a grant proposal, evaluating funder fit, auditing a draft for competitiveness, or planning a budget.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A skill for crafting competitive grant proposals across funder types: government (NIH, NSF, DOE, ARPA), foundation, corporate, philanthropic, and SBIR / STTR. Focuses on the architecture of a winning proposal: fit, structure, narrative, budget — not boilerplate templating.
Before generating the proposal, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
funder_fit_scorer.py to grade fit on 7 dimensions.python3 grants/scripts/funder_fit_scorer.py \
--input funder_fit.json --format markdown
proposal_structure_validator.py against funder type expectations.python3 grants/scripts/proposal_structure_validator.py \
--input proposal_structure.json --funder-type nih --format markdown
budget_realism_checker.py against funder norms + project scope.python3 grants/scripts/budget_realism_checker.py \
--input budget.json --format markdown
A score below 65 across these is usually a "skip this funder" signal.
| Funder type | Emphasizes | De-emphasizes |
|---|---|---|
| NIH | Significance + innovation + approach + investigator + environment (5 criteria) | Commercial outcome |
| NSF | Intellectual merit + broader impacts | Direct commercial outcome |
| ARPA / DARPA | Heilmeier catechism (defined moonshot question) | Incremental work |
| SBIR / STTR | Commercial path + technical risk | Pure science |
| Foundation | Mission fit + measurable outcomes | Pure academic novelty |
| Corporate | Commercial relevance to sponsor | Independence from sponsor |
| Crowdfunding | Story + community appeal | Technical rigor |
Write to the funder's mental model, not a generic "good grant."
A proposal that can't answer all seven crisply isn't ready.
references/funder-fit-and-research-strategy.md — fit dimensions, funder types, multi-funder strategyreferences/proposal-structure-and-narrative.md — per-funder structures, narrative disciplinereferences/budget-design-and-justification.md — budget categories, indirect costs, common errorsresearch/litreview — literature review for proposalsc-level-advisor/general-counsel-advisor — legal review of termsc-level-advisor/cfo-advisor — financial review아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.