「spr」の検索結果

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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Use when creating, grooming, planning, or closing a backlog item. Bridges backlog items to SAM planning with issue, project, and milestone tracking against the configured backend. Activates on interactive browsing with no arguments, loading an item by issue reference or title match to run grooming and SAM planning, autonomous unattended runs that substitute evidence-derived decisions for clarifying questions, a quick path for one-file fixes where full grooming is disproportionate, dismissing an item without completion under a required reason (duplicate, out of scope, superseded, wontfix, blocked), marking an item done with an evidence trail and required summary, or initializing GitHub tracking infrastructure for a project. Stops when the item already has a plan or is blocked on missing information.

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Use when assigning backlog items to a GitHub milestone. Args: {milestone-number} [P0|P1|P2|title-filter]. Uses backlog list to load items, shows items with GitHub Issue status, lets user select which to assign. Creates missing GitHub Issues for selected P0/P1 items, assigns all to the milestone, updates Project V2 Status to Backlog. Use after create-milestone to populate a sprint or release.

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Close a completed GitHub milestone. Args: {milestone-number}. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the GitHub milestone, updates Project V2 Status to Done for closed issues, and generates a completion summary. Use when a sprint or release is finished and needs to be officially closed.

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Transitions a GitHub milestone from planning to active execution. Use when the team is ready to begin a sprint or release cycle — bulk-transitions open issue labels from status:needs-grooming to status:in-progress, updates GitHub Projects V2 Status to In Progress, and confirms before applying changes. Requires milestone number as argument. Use after /group-items-to-milestone.

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Close a completed GitHub milestone and generate a completion summary. Use when a sprint or release is finished and needs to be officially closed. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the milestone, updates Project V2 Status to Done for closed issues, and produces a completion report.

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Use when querying, modifying, or converting JSON, YAML, TOML, XML, CSV, HCL, or INI with dasel v3. Complete reference for selectors, functions, conditionals, variables, spread operator, type casting, and format-specific patterns.

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Assign backlog items to a GitHub milestone by bridging per-item files to GitHub Issues. Use when populating a sprint or release milestone, after create-milestone, or when a milestone number is specified with optional P0/P1/P2 or title filter. Creates missing GitHub Issues for selected P0/P1 items, assigns all selected items to the milestone, and updates Project V2 Status to Backlog.

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Creates a GitHub milestone on the current repository and returns its number for downstream use. Use when starting a new sprint, release, or theme grouping of backlog items. No args triggers guided intake (title, due date, description); 'quick {title}' skips to description only. Checks for duplicates before creating. Returns milestone number for /group-items-to-milestone.

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Send feedback or bug reports to the Deepline team. Use when the user asks to report feedback or share a session, and proactively when a Deepline Play is disproportionately slow, appears stuck, or an ordinary outcome requires avoidable product steps.

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GTM prospecting, enrichment, outreach, and Deepline Play work/audits. Providers: adyntel,affinity,ai_ark,akta,allegrow,amplemarket,apify,attention,attio,aviato,bettercontact,bigquery,bluesky,bounceban,brave,browserbase,builtwith,cbinsights,clay,clickhouse,cloudflare,contactout,contextdev,crunchbase,crustdata-v3,customer_db,databricks,dataforseo,datagma,dealroom,deepline_ip_to_company,deepline_native,deeplineagent,discolike,dropleads,edges,emailbison,emailguard,enformion,enigma,exa,findymail,firecrawl,fireflies,firmable,forager,fullenrich,fundable,generic_http,getleads,gong,google_ads_audiences,google_workspace,govfiles,grain,hackernews,harvestapi,heyreach,hubspot,hunter,icypeas,instantly,intercom,ipqs,kaspr,kernel,leadmagic,lemlist,limadata,linkedin_ads_audiences,luma,lusha,meta_audiences,millionverifier,nooks,notion,openmart,opensosdata,openwebninja,outreach,parallel,peopledatalabs,pitchbook,podscan,polymarket,postgres,predictleads,prospeo,quickenrich,rapidapi,redshift,rocketreach,salesforce,salesforge,….

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Turns raw captures (dictated braindumps in a deliverable's inbox/, or recent macwhspr dictation-log entries) into proposed planning-wiki changes, shown as one unified diff. Classifies each statement as a decision, evidence claim, question, task, or dropped item, traces it to its capture paragraph, tags unconfirmed names, numbers, and citations UNRESOLVED, and writes decisions only with the author's assent. Edits only the planning wiki, never source files. Use when asked to intake, absorb notes, or process the inbox, or after dictating notes from a talk, meeting, or reading session.

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Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.

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Writes or reviews a pre-analysis plan before data collection: registry choice (OSF, AEA, AsPredicted, registered reports), locked models and decision rules, code run on simulated data, contingencies, and deviation records. Use when drafting or auditing a pre-registration or PAP, choosing a registry, or handling a deviation.

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JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot.

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Java coding standards for Spring Boot services: naming, immutability, Optional usage, streams, exceptions, generics, and project layout.

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Run the backend verification loop after Kotlin/Spring code changes — Kotlin style + unit tests via `gradlew verify`, plus integration tests when repos/migrations changed — and drive fixes until green. Use after editing backend code or when the user asks to verify/check the build.

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Scout a topic on YouTube. Searches YouTube Data API v3 for the most relevant videos, ranks them by views (or engagement, likes, recency, momentum, breakout), and writes a research workbook in the OWT-Social-Ads layout with Videos, Channels, and Summary sheets, plus optional Comments and Transcripts (hooks, replay hotspots) sheets. Can also append into an existing sheet. Use when the user says "scout <topic>", wants a spreadsheet of top YouTube videos on a topic, wants creator or hook research, or wants YouTube rows added to their social ads sheet.

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Run a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.

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Run the full content pipeline in one command — brief, write, gate, log. Progress updates between steps. 5-minute timeout with partial state recovery.

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把功能需求拆解为用户故事和可测试的验收标准时使用。适用于 PRD 第 7 节、Sprint 准备、给 QA 的测试输入。优先使用 INVEST 检查 + Given/When/Then 格式。

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管理交付节奏和 Cycle 时间盒时使用。适用于替代传统 Sprint、固定时间可变范围的项目、AI 工作流的快节奏管理。优先使用 Linear Cycles + Basecamp Shape Up + Cooldown 节奏。

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把项目目标分解为可执行工作包时使用。适用于项目启动、Sprint 规划、需求拆任务。优先使用 WBS（Work Breakdown Structure）逐层分解到 5~60 分钟粒度。

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需要快速交付且范围可协商时使用。适用于固定时间盒下的功能开发、避免无限延期、Sprint 替代方案。优先使用 Basecamp Shape Up 的 Pitch + Appetite + Cycle 方法。

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