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".
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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.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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 patterns还没有评价。欢迎分享你的使用感受。
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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".
中文简介正在准备中。当前显示原文说明。
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.
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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."
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
中文简介正在准备中。当前显示原文说明。