Turn contracts, reports, and scanned forms into cited facts and proposed tasks. Preserve deadlines, explicit owners, source locations, and uncertainty, then create and verify records in an external tracker only within the user's approved scope.
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Extract cited obligations, deadlines, tasks from documents.
安装前可以查看交给 AI 代理的指令内容。
Turn documents into cited facts and proposed actions. Extraction is not legal advice, and low-confidence OCR or ambiguous language must remain visible. The pdf / pdf / docx skills own extraction mechanics; this skill owns what happens to the extracted content.
Don't use for: plain text extraction with no downstream structuring (load pdf directly).
Use read_file for local files and web_extract for URLs to identify files, versions, dates, page counts, language, scan quality, and the requested output schema. Detect duplicate/revised copies before analysis. Done when the authoritative or latest version is known or ambiguity is stated.
Load pdf, pdf, or docx. Extract text/tables while retaining file and page/section coordinates. For scans, record OCR confidence or visible quality issues. Done when every extracted field can cite its source location.
Separate:
Do not collapse "may," "should," and "must." Done when modality and uncertainty are preserved.
Cross-check dates, totals, repeated names, table sums, defined terms, and references to appendices. Surface contradictions rather than choosing silently. Done when key facts have consistency checks or explicit exceptions.
For each actionable obligation create outcome, owner if explicit, due date if explicit, dependency, acceptance condition, risk, and citation. Unknown owners/dates remain unresolved — never invented. Done when no proposed task relies on an unsupported inference.
Present structured facts, high-risk clauses, low-confidence fields, and proposed tasks for approval. Drafting is not creating: writing to any external tracker requires the user's explicit scope. Recommend professional review for legal, medical, tax, or safety-critical interpretation. Done when approved fields/actions are unambiguous.
Use the user's approved destination — notion, a calendar, a spreadsheet via xlsx, or another task tracker. Attach document/page provenance and avoid copying unnecessary sensitive text. Read records back from the provider and verify owner/date/link. If a write times out ambiguously, search for the expected record before retrying. Done when every approved action is verified.
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Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.
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创建用公式联动利润表、资产负债表和现金流量表的 Excel 模型，分阶段填入历史实际数据与预测假设，比较不同情景，并检查三张报表之间的一致性。
Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.
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Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
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Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.
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Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.
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