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Agent Skill · 研究检索

deal-next-best-actions(deal Next.js 最佳 actions)

deal-next-best-actions 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

deal-next-best-actions 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,674 个 Skill 里排第 9,777。

总安装 13,615周安装 481GitHub Stars 公开资料未说明下载 4,036核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:deal-next-best-actions(deal Next.js 最佳 actions)
来源仓库:https://github.com/sixtysecondsapp/use60
仓库路径:skills/deal-next-best-actions
安装命令:
npx skills add https://github.com/sixtysecondsapp/use60 --skill 'Deal Next Best Actions'
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/sixtysecondsapp/use60 --skill 'Deal Next Best Actions'

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

deal-next-best-actions 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。它可生成基于阶段、活动模式、对话历史和外部情报的优先行动清单,诊断阻碍进展的因素并提供高概率推进交易的行动建议。

使用时建议结合来源仓库和原始 README 核验具体用法,安装前需确认权限范围、维护状态及是否触发联网、命令执行或文件读写。

安装命令为 npx skills add https://github.com/sixtysecondsapp/use60 --skill 'Deal Next Best Actions',适用于主流 AI 宿主环境。注意该技能归类为研究检索,建议在使用前进一步验证其适用场景和安全边界。

来源仓库内路径:skills/deal-next-best-actions

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/sixtysecondsapp/use60 --skill 'Deal Next Best…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

deal-next-best-actions 怎么安装?

在终端执行 npx skills add https://github.com/sixtysecondsapp/use60 --skill 'Deal Next Best Actions'。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

deal-next-best-actions 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

deal-next-best-actions 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

deal-next-best-actions 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

deal-next-best-actions 用的人多吗?

公开数据是累计安装 13,615 次,近一周安装 481 次,下载 4,036 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

deal-next-best-actions 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
deal-next-best-actions

deal Next.js 最佳 actions · 本页

13,615—需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Available Context & Tools

@_platform-references/org-variables.md @_platform-references/capabilities.md

Deal Next Best Actions

Goal

Generate a ranked, prioritized action plan for advancing a deal based on stage, activity patterns, conversation history, external intelligence, and capacity. This is not a generic checklist -- it is a 5-layer situational analysis that reads the deal's signals from CRM data, past transcripts, and external triggers, diagnoses what is blocking progress, and prescribes specific actions with the highest probability of moving this deal to close.

Why Prescriptive Actions Matter

The difference between a good rep and a great rep is not effort -- it is allocation:

  • Top-performing reps spend 65% of their time on deals they will win (Salesforce State of Sales, 2023). Average reps spread time evenly, wasting 40%+ on dead deals.
  • The #1 reason deals stall is inaction, not objections (Gong Labs, analysis of 70,000+ deals). 56% of "lost" deals were actually "abandoned."
  • Deals that receive a meaningful seller action every 5-7 business days close at 2.3x the rate of deals with gaps longer than 14 days (InsightSquared pipeline analytics).
  • The right action at the right time matters more than volume. A single well-timed executive introduction outperforms 10 follow-up emails (RAIN Group).
  • Reps who follow a next-best-action framework achieve 23% higher quota attainment than reps who rely on intuition alone (CSO Insights, 2022).

The goal is not "do more" -- it is "do the one thing that moves the needle most, right now."

Required Capabilities

  • CRM: To fetch deal data, stage, recent activity, and related records
  • Web Search: To discover company news, trigger events, and stakeholder changes that create action opportunities

Inputs

  • deal_id: The deal identifier (required)
  • user_capacity (optional): "busy" | "normal" | "available" -- affects action volume
  • organization_id: Current organization context (from session)

The 5-Layer Intelligence Model

Work through these layers in order. Each layer builds on the previous. Skip layers only when data is unavailable (see Graceful Degradation).

Layer 1: Deal Context (CRM Data)

Gather the foundational deal state from CRM:

  1. Fetch deal: execute_action("get_deal", {id: deal_id, include_health: true}) -- stage, value, close date, contacts, health score
  2. Fetch deal health score: Check deal_health_scores table for overall_health_score, risk_factors, risk_level, days_in_current_stage, sentiment_trend, meeting_count_last_30_days
  3. Fetch pipeline summary: execute_action("get_pipeline_summary", {}) -- overall pipeline context
  4. Fetch recent activities: execute_action("get_deal_activities", {deal_id, limit: 20}) -- meeting history, emails, calls
  5. Fetch tasks: execute_action("list_tasks", {deal_id}) -- existing planned actions
  6. Fetch relationship health: Check relationship_health_scores for primary contact -- overall_health_score, is_ghost_risk, ghost_probability_percent, days_since_last_contact

Health data integration: Use deal_health_score.overall_health_score and deal_health_score.risk_factors to inform action prioritization. Low health scores (< 50) should trigger rescue actions before advancement actions. High ghost risk on the primary contact should trigger multi-threading or channel-switching actions.

If any data call fails, proceed with available data. Note the gap and adjust recommendations accordingly.

Layer 2: Enrichment (Web Search)

Search for external intelligence that creates action opportunities:

  1. Company news: Search for "{company_name}" news -- funding rounds, acquisitions, leadership changes, product launches, layoffs, earnings reports within last 90 days
  2. Trigger events: Look for events that change buying urgency -- new executive hire (budget holder), competitor deal, regulatory change, expansion announcement
  3. Contact enrichment: For key stakeholders, check for role changes, promotions, public speaking, published content that reveals priorities
  4. Industry signals: Relevant market trends, competitor moves, or regulatory changes affecting the buyer's business

How to use enrichment in actions: A funding round means budget unlocked -- propose a larger deal. A leadership change means priorities may shift -- re-qualify. A competitor announcement creates urgency -- position against it. Every trigger event maps to a specific action (see references/action-library.md for the trigger-to-action mapping).

Layer 3: Historical Context (RAG Transcript Search)

Before generating actions, search meeting transcripts for conversation history that should ground recommendations:

  1. Commitments made: Search transcripts for promises by either side -- "I will send you...", "We agreed to...", "Next step is..."
  2. Concerns raised: Search for objections, hesitations, risk language -- "Our concern is...", "We are worried about...", "The challenge is..."
  3. Agreed next steps: Search for explicit action items from past meetings
  4. Competitive mentions: Search for competitor names or comparison language
  5. Buying signals: Search for positive intent -- "We would like to...", "When can we start...", "What does pricing look like..."
  6. Decision criteria: Search for how the buyer evaluates -- "What matters most is...", "We need to see...", "The key requirement is..."

How to use RAG context: Every action should reference specific transcript findings where available. Instead of "Send a follow-up email," say "Send a follow-up addressing Sarah's concern from the Jan 15 call about data migration timelines." Ground actions in real conversation history, not generic playbook advice.

Layer 4: Intelligence Signals (Health + Pattern Analysis)

Synthesize Layers 1-3 into diagnostic signals. Enhance the existing health and pattern analysis with RAG insights:

  • Health-informed prioritization: See the Action Prioritization Framework section below
  • Activity pattern analysis: See the Activity Pattern Analysis section below
  • RAG-enhanced signals: If transcripts reveal an unaddressed objection, that becomes a top-priority action. If a commitment was made and not fulfilled, flag it. If competitive mentions are increasing, address the competitive threat.

Layer 5: Action Strategy (Stage Playbooks + RAG-Grounded Specifics)

See references/action-library.md for the full action catalog with impact ratings, effort levels, and templates for each action type. See references/stage-playbooks.md for complete stage-by-stage playbooks with exit criteria, risk indicators, time limits, and worked examples.

Select and rank actions using the prioritization framework, but personalize them with specifics from Layers 2-3. Every action should be grounded in something concrete: a transcript quote, a trigger event, a health signal, or a pattern from the activity timeline.

Action Prioritization Framework

Health-Informed Prioritization

Before applying the Impact-Urgency-Effort matrix, check the deal's health score and risk factors:

  • Critical health (< 30): Prioritize rescue actions first (re-engagement, multi-threading, addressing root cause). Advancement actions are secondary until the deal is stabilized.
  • Warning health (30-60): Balance rescue and advancement. Address the top risk factor while maintaining momentum.
  • Healthy (60+): Focus on advancement actions. Optimize for speed to close.

Risk factor mapping to actions:

  • stage_stall -> Identify and remove the blocker, propose next milestone
  • no_activity -> Re-engagement action (value-add email or call)
  • sentiment_decline -> Address objection or concern, introduce reference customer
  • no_meetings -> Schedule discovery or check-in meeting

Ghost risk consideration: If primary contact has is_ghost_risk: true or ghost_probability_percent > 50, multi-threading becomes the #1 priority action regardless of other factors.

The Impact-Urgency-Effort Matrix

Every potential action is scored on three dimensions:

Impact (1-5): How much does this action advance the deal toward close?

  • 5: Directly creates a commitment or removes a blocker
  • 4: Builds significant momentum (demo, reference call, business case)
  • 3: Maintains engagement and advances understanding
  • 2: Administrative or preparatory
  • 1: Low-value activity

Urgency (1-5): What is the cost of delay?

  • 5: Window closing within 48 hours
  • 4: Overdue or time-sensitive (close date approaching, 14+ day gap)
  • 3: Should happen this week
  • 2: Important but not time-critical
  • 1: Can wait without consequence

Effort (inverted, 1-5): How easy is it to execute?

  • 5: Under 15 minutes
  • 4: 15-30 minutes
  • 3: 30-60 minutes
  • 2: 1-3 hours
  • 1: Half-day or more

Priority Score = Impact x Urgency x Effort (inverted)

  • Score 75-125: Urgent -- do today
  • Score 40-74: High -- do this week
  • Score 15-39: Medium -- schedule for next week
  • Score 1-14: Low -- batch with other work or delegate

Capacity Adjustment

CapacityMax ActionsFocus
busy1 (minimum_viable_action only)Highest-impact single action that takes <15 min
normal3-5Top actions across impact/urgency spectrum
available5-8Full action plan including preparation and optimization

When capacity is "busy," the minimum_viable_action MUST be executable in under 15 minutes and have the highest combined impact + urgency score.

Stage-Specific Action Playbooks

See references/action-library.md for the full action catalog and references/stage-playbooks.md for complete stage-by-stage playbooks with exit criteria, risk indicators, time limits, and worked examples.

Stage 1-2: Discovery / Qualification

Primary objective: Validate the deal is worth pursuing and establish the buying process. Top actions: Multi-thread into the account, quantify the pain, map the buying process, confirm BANT/MEDDIC criteria, send meeting recap with insights. Red flags: No response after discovery (48h), single-threaded, no budget/timeline mention, "just exploring" language.

Stage 3-4: Evaluation / Demo

Primary objective: Prove ${company_name}'s solution solves their specific problem better than alternatives. Top actions: Deliver customized demo, introduce reference customer, create business case/ROI model, engage economic buyer, identify and address competitor. Red flags: Demo keeps being postponed, no executive sponsor, evaluation criteria not shared.

Stage 5-6: Negotiation / Proposal

Primary objective: Remove obstacles between "yes" and signature. Top actions: Send proposal/contract, pre-wire negotiation, map procurement requirements, prepare concession strategy, create urgency with real constraint. Red flags: "Let me think about it" without next step, new stakeholders appearing late, radio silence after proposal.

Stage 7+: Closing

Primary objective: Get to signature and set up for successful implementation. Top actions: Resolve final objection, make signing frictionless, preview implementation plan, align on success metrics. Red flags: Contract sent but not opened, champion going quiet, close date pushed second time.

Activity Pattern Analysis

Read references/action-library.md for the complete re-engagement action catalog with templates for each silence duration.

Staleness Detection

  • 7+ days: Re-engage with value-add, NOT "just checking in."
  • 14+ days: Direct outreach to champion with specific question. If no response in 48h, call.
  • 21+ days: Breakup email. Paradoxically re-engages 15-20% of stalled deals (Gong).

Ghosting Detection

  • 2+ messages, no response: Switch channels (call, LinkedIn, text) or go around to different stakeholder.
  • Meetings keep rescheduling: Buyer is deprioritizing. Introduce constraint or new value.
  • "Let me get back to you" 3+ times: Address directly -- "Are you still considering this, or has something changed?"

Multithreading Analysis

  • 1 contact: Critical risk. Multi-threaded deals close at 3x the rate. Ask for introductions.
  • 2-3 contacts: Good but not safe. Identify who is missing (usually economic buyer or end-user champion).
  • 4+ contacts: Strong. Ensure stakeholder alignment on criteria and timeline.

Confidence Level

Assess the data richness across all 5 layers and assign a confidence level to the overall recommendation:

LevelCriteriaHow It Affects Output
HighCRM data complete + RAG transcripts found + web enrichment available. 3+ layers with rich data.Actions are specific and grounded. Rationales cite transcript quotes, trigger events, and health signals.
MediumCRM data available + at least one of RAG or web enrichment. 2 layers with data.Actions are informed but some are stage-generic. Flag which layers are missing.
LowCRM data only, or CRM data is sparse. Only 1 layer has meaningful data.Actions default to stage playbook recommendations. Prominently flag: "Limited data -- these are playbook defaults, not deal-specific recommendations. Enrich this deal for better actions."

Always include the confidence level in the output and explain what data informed the recommendation (and what was missing).

Graceful Degradation

Missing DataFallback BehaviorUser-Facing Note
CRM deal record incompleteProceed with available fields; first action = "Update deal stage and close date in CRM""Deal record is missing [fields]. Recommendations are based on available data."
No activity historyDefault to stage-appropriate actions from playbook"No activity history available. Recommendations based on deal stage only."
RAG returns no transcriptsProceed without transcript grounding; actions are stage-generic"No meeting transcripts found. Actions are not grounded in conversation history."
Web search fails or returns nothingProceed without trigger events; skip Layer 2"External enrichment unavailable. No trigger events detected."
Contact not enrichedSkip stakeholder mapping; recommend enrichment"Contact details are thin. Consider running enrichment before next outreach."
Health scores unavailableSkip health-informed prioritization; use stage + activity only"Health scoring unavailable. Prioritization based on stage and activity patterns."
Conflicting signals (verbal vs. behavioral)Prioritize behavioral signals over verbal ones"Buyer says [X] but activity shows [Y]. Behavioral data is more predictive."
10+ open tasks already existDo not add more; prioritize existing tasks"You have [X] open tasks. Complete or close the top 3 before adding new actions."
Deal appears dead (30+ days, past close date)Honest assessment + single reactivation action"This deal shows no activity in [X] days. Confirm viability before investing time."

Output Contract

Return a SkillResult with:

  • data.actions: Array of action objects (ranked by priority score):

- action_type: "email" | "call" | "meeting" | "task" | "crm_update" | "research" | "internal_alignment" | "reference_call" | "proposal" | "demo" - title: Action title (specific and actionable) - description: What to do (detailed enough to execute without follow-up questions) - priority: "urgent" | "high" | "medium" | "low" - priority_score: number (Impact x Urgency x Effort) - roi_rationale: Why this action matters -- connect to data, transcript quotes, or trigger events - estimated_time: Time estimate in minutes - deadline: Recommended deadline (ISO date) - owner: Suggested owner (user's name or role) - dependencies: Other actions this depends on (array of action titles or empty) - source_layer: Which intelligence layer informed this action (e.g., "RAG: Jan 15 call", "Web: funding round", "Health: ghost risk")

  • data.priorities: Summary of priority distribution (how many urgent/high/medium/low)
  • data.roi_rationale: Overall rationale for the action plan (2-3 sentences explaining the strategy)
  • data.minimum_viable_action: The single most important action if user is busy (must be <15 min effort)
  • data.stage_insights: Insights about deal stage and what typically works at this stage
  • data.rag_context_used: Array of specific transcript findings that informed actions (quote, date, speaker, relevance)
  • data.confidence_level: "high" | "medium" | "low" with explanation of data richness
  • data.trigger_events: Array of external events creating action opportunities (event, source, recommended_action, urgency_impact)

Quality Checklist

Before returning the action plan, verify:

  • Actions are ranked by priority score, not alphabetically or randomly
  • Every action has a specific ROI rationale tied to THIS deal's situation, not generic advice
  • Time estimates are realistic (not everything is "5 minutes")
  • The minimum_viable_action is genuinely the highest-impact thing the rep can do in under 15 minutes
  • Actions account for existing open tasks (no duplicates)
  • If activity gap is detected (7+ days), re-engagement is the #1 priority
  • If only 1 contact is engaged, multi-threading is in the top 3 actions
  • Actions match the deal stage (not recommending a discovery call for a deal in negotiation)
  • At least one action involves the BUYER doing something (not all seller-side)
  • Deadlines are specific dates, not "soon" or "next week"
  • If the deal has a close date, actions are aligned to that timeline
  • The overall rationale tells a coherent story about WHERE this deal is and WHAT moves it forward
  • RAG transcript findings are cited in action rationales where available (quote + date)
  • Trigger events from web search are connected to specific actions (not just listed)
  • Confidence level accurately reflects data richness across the 5 layers
  • Every data claim has a source (CRM field, transcript quote, web search result, health signal)

Error Handling

Deal not found or data incomplete

If the deal record is missing critical fields (stage, value, close date), return actions focused on data completion: "Update deal stage and close date in CRM" as the first action, then provide best-effort recommendations based on available data.

No activity history available

Without activity data, you cannot detect staleness or ghosting. Default to stage-appropriate actions and flag: "No activity history available. These recommendations are based on deal stage only."

Deal is in a very early stage with minimal data

If the deal is in Stage 1 with only a name and company, focus entirely on discovery: research the company, identify stakeholders, prepare discovery questions, schedule the first call.

Deal appears to be dead (no activity 30+ days, past close date)

Be honest: "This deal shows no activity in [X] days and the close date has passed. Before investing time, confirm with the buyer that the opportunity is still active." Provide a single reactivation action, not a full plan.

User capacity is "busy" but the deal is critical

Note the tension: "This is your most critical deal, but you indicated limited capacity. The minimum viable action is [X]. However, this deal is at risk if [Y] is not addressed this week. Consider delegating [Z] to free up 30 minutes."

Tone and Presentation

  • Be direct and specific. "Send a 3-bullet email to Sarah Chen addressing her data migration concern from the Jan 15 call" not "Consider reaching out to the prospect."
  • Explain the WHY in plain language. Reps follow advice they understand and believe.
  • Be honest about dead or dying deals. Saying "this deal may not be worth your time" is valuable advice.
  • Frame actions as experiments. "Try this because the data shows..." not "You must do this."
  • Acknowledge trade-offs. "This action takes 45 minutes, but it addresses the #1 blocker on your highest-value deal."
  • Ground in specifics. Reference transcript quotes, trigger events, and health signals -- not generic playbook advice.

平台分布

Codex

34.08%
按下载量换算1,375

Claude

28.37%
按下载量换算1,145

Cursor

19.43%
按下载量换算784

Gemini CLI

9.33%
按下载量换算377

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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