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sales-request-skill销售请求技巧

Agent Skill

sales-request-skill 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。

总安装

1,552

周安装

66

GitHub Stars

13

下载量

544
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:sales-request-skill(销售请求技巧)
来源仓库:https://github.com/sales-skills/sales
仓库路径:skills/sales-request-skill
安装命令:
npx skills add https://github.com/sales-skills/sales --skill sales-request-skill
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/sales-skills/sales --skill sales-request-skill

简介

用于查找、检索和筛选销售相关资源与信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 支持按关键词、场景或来源线索进行信息聚合与初步过滤,提升研究效率。
  • 通过 GitHub 安装后,结合具体任务调用,返回结构化候选列表供进一步处理。
  • 使用前应确认权限范围,避免触发不必要的联网或文件操作,注意数据脱敏要求。
  • 建议核对仓库维护状态,确保接口可用性与结果准确性。

SKILL.md

Request or Build a Missing Sales Skill

The user needs a sales, marketing, or GTM capability that doesn't have a skill yet. Help them contribute it or request it.

This skill always ends with a concrete action on GitHub:

  • Path A (Build): Create the skill files, commit, push, and open a pull request to sales-skills/sales
  • Path B (Request): File a GitHub issue on sales-skills/sales describing what's needed
  • Path C (Share Learnings): Scan local learnings, scrub PII, and open a GitHub issue for each skill with shareable discoveries

Do not stop at "here's what the PR/issue would look like" — actually create it using gh pr create or gh issue create.

Step 1: Confirm the gap

If $ARGUMENTS is provided, use it. Otherwise ask: "What sales, marketing, or GTM capability do you need that isn't covered by an existing skill?"

Verify the request fits the sales/marketing/GTM domain. If it's outside scope entirely (e.g., "build a database migration tool"), say so and suggest appropriate tools instead.

Check the existing skills by reviewing the routing table in skills/sales-do/SKILL.md and listing installed skills in ~/.claude/skills/ to make sure there isn't already a skill that covers this. If there's a close match, suggest it instead.

Summarize back to the user:

  • What they need: one sentence
  • Closest existing skill: what's close but doesn't quite fit
  • Category: which section it would belong in (Prospecting & Pipeline, Active Deals, Strategy & Content, Marketing & GTM, Research & Data, Creative & Design, etc.)

Step 2: Choose a path

Ask the user:

Would you like to: 1. Build the skill — I'll help you create it with proper structure and prepare a PR 2. Request the skill — I'll file a GitHub issue so the maintainers know it's needed 3. Share learnings — I'll scan your installed skills for discoveries, scrub personal details, and share them back to the repo

Path A: Build the skill

Use skill-creator if available

Check whether the /skill-creator skill is available. If available, delegate to it for the full create-test-iterate workflow.

When delegating to /skill-creator, provide this sales-specific context:

Repo conventions for this skill: - Naming: sales-<problem> for sales skills, descriptive names for marketing/GTM skills (e.g., cold-email, launch-strategy) - Descriptions should use phrases salespeople and marketers actually say — "write a cold email", "prep for a discovery call", "handle this objection" - Description must end with negative triggers: Do NOT use for X (use /alternative) - SKILL.md is the only required file — keep it focused and actionable - Skills should ask clarifying questions before acting (audience, stage, constraints) - Skills route through /sales-do — the description field determines when the router matches Skill structure: `` skills/<skill-name>/ ├── SKILL.md # Main instructions (required) ├── scripts/ # Deterministic operations (data fetching, validation, formatting) ├── references/ # Large reference material (>500 words — API docs, data models) ├── assets/ # Templates, examples, configuration files └── evals/ └── evals.json # Test cases (optional, generated by skill-creator or manually) ` **SKILL.md body pattern** (follow what other skills in this repo do): 1. Step to gather context (ask 2-4 questions about the user's specific situation) 2. Implementation steps with actionable output 3. Templates or frameworks relevant to the problem domain 4. Gotchas section with 3-5 common Claude failure points for this domain 5. Output formatting guidance 6. Next steps pointing to related skills **Key principles:** - **Don't state the obvious**: Focus on info Claude wouldn't know — internal conventions, domain gotchas, non-obvious patterns - **Avoid railroading**: Use "typically" instead of "always". Give Claude flexibility to adapt to the situation. - **Scripts**: If the skill involves deterministic operations (data fetching, formatting, validation), include scripts in scripts/ - **Progressive disclosure**: Move reference material >500 words to references/` directory

Then let skill-creator run its workflow.

If skill-creator is NOT available

Build the skill manually following the conventions above.

Write the SKILL.md

---
name: <skill-name>
description: "<What problem it solves>. Use when <trigger phrases the user would say>. Do NOT use for <X> (use /alternative)."
argument-hint: "[brief hint about expected arguments]"
license: MIT
metadata:
  author: sales-skills
  version: 1.0.0
---

Read 2-3 existing skills in skills/ to match the tone and structure. Key things to get right:

Description field — This is how the /sales-do router and Claude decide whether to use the skill. Be specific about trigger phrases. Include both what the skill does AND when to use it:

# Bad: too vague
description: "Help with sales emails"

# Good: specific triggers, covers edge cases
description: "Write and optimize cold outbound email sequences. Use when writing first-touch cold emails, building multi-step outreach sequences, A/B testing subject lines, or improving reply rates on existing campaigns."

Body — Should follow the question-first pattern: gather context about the user's situation before producing output. Include templates, frameworks, or examples that make the output immediately useful.

Test the skill

Generate an evals/evals.json file inside the new skill directory with 2-3 realistic test cases. Each eval should represent a prompt a salesperson or marketer would actually say, with assertions describing what a good response looks like.

{
  "skill_name": "<skill-name>",
  "evals": [
    {
      "id": 0,
      "prompt": "realistic user prompt a salesperson or marketer would say",
      "expected_output": "description of what a successful response looks like",
      "assertions": [
        {"name": "assertion_name", "description": "specific thing to check in the output"}
      ]
    }
  ]
}

Run the eval prompts with the skill active and verify the outputs pass the assertions. This matches the schema that /skill-creator uses, so evals work the same regardless of which build path created the skill.

Submit the PR

After creating the skill files, submit a pull request. Do all of these steps — don't stop at "here's what to do":

  1. Update skills/sales-do/SKILL.md — add a row to the appropriate routing table
  2. Update README.md — add a row to the appropriate catalog table
  3. Create a branch: git checkout -b add-<skill-name>
  4. Stage and commit: git add skills/<skill-name>/ evals/ skills/sales-do/SKILL.md README.md && git commit -m "Add <skill-name> skill"
  5. Push: git push -u origin add-<skill-name>
  6. Open the PR:
gh pr create \
  --repo sales-skills/sales \
  --title "Add <skill-name> skill" \
  --body "$(cat <<'EOF'
## Summary
- **Problem**: <what the user is solving>
- **Category**: <which section it belongs in>
- **Example invocation**: `/<skill-name> <example prompt>`

## Files
- `skills/<skill-name>/SKILL.md` — main instructions
- `skills/sales-do/SKILL.md` — routing table updated
- `README.md` — catalog table updated
EOF
)"

Return the PR URL to the user when done.

Path B: Request the skill

File a GitHub issue on the repo. Do not just draft it — actually submit it:

gh issue create \
  --repo sales-skills/sales \
  --title "Skill request: <skill-name>" \
  --body "$(cat <<'EOF'
## Problem
<What the user is trying to do, in their words>

## Category
<Which section this fits in: Prospecting, Active Deals, Strategy, Marketing, Research, Creative, etc.>

## Example use case
<A concrete scenario where this skill would help>

## Suggested trigger phrases
<2-3 phrases a salesperson or marketer might say that should route to this skill>
EOF
)"

Return the issue URL to the user when done.

Path C: Share learnings

Learnings accumulate in references/learnings.md files inside each installed skill as users discover API quirks, pricing changes, workarounds, and gotchas. This path scans those files, scrubs PII, and opens GitHub issues so individual discoveries can improve the skills for everyone.

Step C1 — Scan installed skills

Find all learnings files:

find ~/.claude/skills/*/references/learnings.md 2>/dev/null

Read each file. Skip:

  • Empty stubs (files with no content beyond the initial template)
  • Entries already marked as shared (<!-- shared:YYYY-MM-DD -->) or declined (<!-- declined:YYYY-MM-DD -->)

If the user mentioned a specific skill or platform (e.g., "I found some Apollo gotchas"), acknowledge it and note that the scan will surface that skill's learnings alongside any others found.

If no unshared learnings are found across any installed skill, tell the user:

No unshared learnings found. Learnings accumulate automatically as you use skills — when you discover API quirks, workarounds, or gotchas, they get appended to each skill's references/learnings.md. Come back after you've used some skills for a while.

Stop here if nothing is found.

Step C2 — Present and classify

For each skill that has unshared learnings, present a table:

#LearningGeneralizable?Reason
1...Yes/No...

Generalizable (share these):

  • API quirks, undocumented behavior, or endpoint changes
  • Pricing changes or limits not reflected in docs
  • Workarounds for platform bugs
  • Configuration gotchas or non-obvious defaults
  • Integration issues between tools
  • Rate limits, throttling, or quota details

Not generalizable (skip these):

  • Company names, team structures, internal workflows
  • Personal preferences or org-specific custom fields
  • Internal tool configurations specific to one organization
  • Account-specific negotiated pricing or contracts

Ask the user to confirm or override the classifications before proceeding.

Step C3 — Scrub PII

For each generalizable learning, find and replace personally identifiable information:

FindReplace with
Company or domain names[Company], [domain]
People names[Name]
Email addresses[email]
GitHub/social handles[handle]
Account, API, or workspace IDs[account-id]
Internal URLs or IP addresses[internal-url]
Negotiated or non-public pricingRemove entirely (keep only publicly documented pricing)
Customer names[customer]
Specific team names[team] (keep generic ones like "Sales", "Marketing", "Engineering")

Show before/after for each scrubbed learning and ask the user to confirm the scrubbed versions look correct before proceeding.

Step C4 — Open GitHub issue for review

Create one issue per skill (focused, independently mergeable). Do not auto-submit — open the pre-filled issue in the browser for user review.

For each skill with shareable learnings, build a GitHub issue URL:

  • Title: Learnings: sales-{skill-name}
  • Labels: learnings
  • Body: list each scrubbed learning in copy-paste-ready format, plus context about the skill version and submission method

URL-encode the title, body, and labels into a GitHub new-issue URL:

https://github.com/sales-skills/sales/issues/new?title=...&body=...&labels=learnings

Open each URL in the browser:

import subprocess
subprocess.run(['open', '-na', 'Google Chrome', '--args', '--profile-directory=Profile 6', url])

Never auto-submit via gh issue create for learnings — the user must review the scrubbed content before it goes public.

Step C5 — Mark as processed

After the user confirms they've submitted the issue(s), mark every learning that was reviewed:

  • Shared: append <!-- shared:YYYY-MM-DD --> to learnings the user submitted
  • Declined: append <!-- declined:YYYY-MM-DD --> to learnings the user classified as not generalizable or chose not to share

This prevents re-prompting the same learnings on future runs.

Quality checklist

Before submitting a new skill (via PR or skill-creator), verify:

  • Name follows repo conventions (sales-<problem> for sales, descriptive for marketing/GTM)
  • Description includes specific trigger phrases a user would actually say
  • Description ends with negative triggers: "Do NOT use for X (use /alternative)"
  • Frontmatter includes license: MIT and metadata: {author, version}
  • SKILL.md asks clarifying questions before producing output
  • Output is practical and actionable — not generic advice
  • Includes a ## Gotchas section with 3-5 common Claude failure points for this domain
  • Doesn't state the obvious — focuses on info Claude wouldn't know
  • Avoids railroading — uses "typically" instead of "always", gives Claude flexibility
  • Reference material >500 words moved to references/ directory
  • Deterministic operations (data fetching, formatting, validation) use scripts in scripts/
  • Routing table in sales-do/SKILL.md updated with new row
  • README.md catalog table updated with new row
  • evals/evals.json generated with 2-3 realistic test cases matching the skill-creator schema
  • Points to related skills where relevant

Related skills

  • /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.71%
按下载量换算178

Claude

31.02%
按下载量换算169

Cursor

20.91%
按下载量换算114

Gemini CLI

10.31%
按下载量换算56

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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