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.
Structured intelligence dossiers on companies, people, markets, or domains, with source triangulation and fact/inference discipline. Use when preparing a deal-prep dossier, executive briefing, or due-diligence overview.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A research skill for producing structured intelligence dossiers — the kind of document a CEO reads before a meeting, a PM reads before market-entry, or an investor reads before due diligence.
Before building the dossier, 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.
dossier_outline_generator.py to produce a structured outline
tailored to subject + purpose.python3 dossier/scripts/dossier_outline_generator.py \
--subject-type company --purpose deal-prep --format markdown
source_triangulation_validator.py to check each claim has
multiple independent supporting sources + source reliability.python3 dossier/scripts/source_triangulation_validator.py \
--input claims_with_sources.json --format markdown
fact_inference_separator.py to classify each statement and
flag unsupported inferences.python3 dossier/scripts/fact_inference_separator.py \
--input dossier_statements.json --format markdown
A useful structure for any dossier:
Three categories per statement:
| Category | Definition | Example |
|---|---|---|
| Fact | Verifiable, sourced | "Founded 2018; HQ in Chicago" |
| Inference | Reasoned from facts | "Likely targeting enterprise segment based on hiring pattern" |
| Speculation | No supporting evidence | "Might pivot to AI next year" |
A trustworthy dossier separates these clearly. Mixing them = loss of credibility.
| Reliability | Code | Description |
|---|---|---|
| Completely reliable | A | Established, history of completely reliable info |
| Usually reliable | B | History of mostly reliable info |
| Fairly reliable | C | History of reliable info with notable errors |
| Not usually reliable | D | Limited history; mixed accuracy |
| Unreliable | E | Known for inaccurate info |
| Cannot be judged | F | New / unknown source |
| Information credibility | Code | Description |
|---|---|---|
| Confirmed | 1 | Confirmed by other independent sources |
| Probably true | 2 | Not confirmed; consistent with other info |
| Possibly true | 3 | Not confirmed; reasonable but unsupported |
| Doubtful | 4 | Inconsistent with other info |
| Improbable | 5 | Contradicted by other info |
| Cannot be judged | 6 | New info; no validation possible |
A "B-2" rated claim is "usually reliable source, probably true" — workable. An "F-6" claim is "unknown source, unverified" — barely worth including.
For each significant claim:
"Independent" means not derived from the same underlying source. Two news articles citing the same press release ≠ 2 independent sources.
references/dossier-frameworks-and-structure.md — outline patterns per subject typereferences/source-triangulation-and-reliability.md — source assessment, triangulationreferences/fact-vs-inference-discipline.md — categorization + writing patternsresearch/litreview — academic literature searchc-level-advisor/ceo-advisor — strategic briefing patternsc-level-advisor/general-counsel-advisor — legal due diligence overlapmarketing/competitive-teardown — competitive intel anglebusiness-growth/customer-success-manager — account research patterns아직 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 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.
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.
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.