469건 · 관련도순
Resolve repository documentation authority, repair reading paths and explanations, or shape a bounded public projection. Maintain docs after a bounded change when indirect reader promises, generated or paired sources, version/evidence boundaries, or unclear authority need attention. Ordinary known updates stay in normal development; not for isolated prose polishing, code review, or broad runtime audits.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when removing AI-writing tells from deliverable prose — posts, articles, drafts. Triggers on 'make this sound less like AI', 'humanize this draft', 'this reads like ChatGPT wrote it'.
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When setting up the legal pack — captures jurisdiction, role, escalation and playbook into a plain-prose profile every legal skill reads. Triggers on "set up legal", "legal profile".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.
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Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manually run the extended DeepSeek Harness bilingual-document workflow, including generated briefings, delegated prose translation, whole-document translation, and scoped pairing verification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when auditing or fixing prose that reads like a leaked reasoning transcript — dead design-session citations such as (decision N), audit item codes, or §N of uncommitted drafts; change narration such as "used to", "no longer", "this cut"; stack or review vantage ("a later PR in this stack", "rejected in review"); reviewer-addressed justifications; control-flow narration; or hedged planning residue in comments, JSDoc, docs, or Agent Notes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when asked to study any writer or author, imitate article style, directly mimic a living or historical writer's prose style when requested, revise an imitation draft, or adapt Chinese articles for WeChat or Xiaohongshu.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing, editing, or reviewing code, READMEs, docs, commits, changelogs, or any repo content. Pre-2020 human style — terse, opinionated, restrained, with project voice. Blocks the LLM tics (emoji headers, "leverage", "robust", "seamless", "delve", defensive bloat, type-hint overkill, marketing prose, "Whether you're X or Y", verbose error messages) catalogued from cpython, requests, flask, git, ripgrep, express, kubernetes, serde, and the Go stdlib. Triggers on "anti-ai", "human-style", "no slop", "write it like a human", or any time code/docs should look hand-written.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Choose the design and the numbers a research run can honestly claim — write the structured METHOD RECORD (databases, date of access, search string, criteria, screening counts) that r0 validates, pick the claim strength a design can actually carry, record a statistic as data rather than prose, route to the reporting guideline the design owes, and decide what to preregister. Use when a run's question, design, search or statistics have to be settled before the writing, or when a claim needs a strength or a number attached to it.
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Write the document of a research run — plan the question and the outline with rs_plan, write prose and declare its claims together with rs_write, choose the claim strength the evidence actually holds, follow the section structure the run's kind maps to, read the draft back, and build a watermarked draft before the gated clean build. Use when a run's document has to be written, a section drafted, a claim bound to a source, or a build attempted.
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Conditional discipline for projects hosted on a git forge (GitHub, GitLab, Forgejo, …): branch↔PR mapping, self-contained PR prose, review-on-forge, merge consent, and the forge audit. Trigger when: - Opening, updating, reviewing, or merging a pull/merge request. - A campaign's integration branch is ready to surface on a forge. - Auditing forge-side presence at merge-consent or campaign CLOSE. - Prompt contains: /forge, pull request, PR body, merge request, forge discipline, PR review, stacked PR.
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Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture a POC's signed-off behaviour into its spec ledger (`<poc>/spec.json`) — the structured, walkable behaviour contract graduation freezes as authoritative. Run at the moment a behaviour gets signed off ("ok, this works"), while it's fresh — the same trigger as the HANDOFF.md update, but capturing the *behaviour as a testable entry* rather than the *decision as prose*. Use when a POC behaviour is approved, or asked to "capture this spec", "write the spec entry", "log this to the ledger".
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Create, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management workflows.
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Resolve, validate, and audit manuscript integrity and applicable requirements for academic manuscripts and paper artifacts. Use when Codex needs to determine active paper rules from manuscript state, paper type, venue, submission stage, or workflow mode; lint a LaTeX paper project; assess submission readiness; create or update a paper policy profile; explain why a rule is active; or validate the paper-policy registries. Do not use as the primary skill for prose drafting, peer review, rebuttal writing, citation discovery, or figure/table creation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compose a comparison newsletter from Ahmad's OpenMark saves — A vs B vs C, side-by-side, table-driven. Use when Ahmad wants to compare tools, models, frameworks, approaches, or platforms across his saves. Trigger phrases include "compare", "X vs Y", "head to head", "which should I use", "differences between", "trade-offs between", "/newsletter-comparison". Output centers a markdown table with consistent columns, supports it with short prose blocks, ends with a clear recommendation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Trace what an actual code change could break beyond its diff when interfaces, serialized data, database schemas, configuration, caches, lifecycle ordering, or cross-language consumers are involved. Use for explicit impact analysis; not local renames, prose edits, ordinary dependency bumps, or generic architecture discussion.
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Design, review, or report a measured performance comparison. Use for benchmark methodology, speedup or regression claims, and performance-based choices; not generic performance discussion, ordinary dependency bumps, local renames, or prose edits.
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Plan or implement retirement of a system, API, feature, persisted configuration, or database shape. Use for compatibility cutovers, explicit deprecation review, or an explicit unreleased replacement decision, including determining whether consumers or data exist; not ordinary dependency bumps, local renames, prose edits, or generic architecture discussion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.