customer-reach

customer-reach is a skill for Claude Code from kimsanguine/hplan. It costs 106 tokens per session (1,363 once invoked), scanned A, original, MIT.

A Korean-language workflow for finding people to interview, drafting outreach, and preparing interview questions.

In plain words
What is it for?
It helps plan recruitment through LinkedIn, communities, surveys, or personal networks, and create outreach messages or interview-question sets.
Why use it?
It helps a product team reach suitable candidates and gather real customer statements before documenting user problems.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter.

Part of the discover plugin — 6 skills shipped together

Good fit It helps plan recruitment through LinkedIn, communities, surveys, or personal networks, and create outreach messages or interview-question sets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kimsanguine/hplan/customer-reach
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add kimsanguine/hplan --skill customer-reach
Clone the repo
git clone --depth 1 https://github.com/kimsanguine/hplan

Made for: Claude Code.

Or install discover, the plugin that ships this one along with the rest of its 6 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for customer-reach

README.md
[![agentmods](https://agentmods.dev/badge/skills/kimsanguine/hplan/customer-reach/github.svg)](https://agentmods.dev/skills/kimsanguine/hplan/customer-reach)
Your own site
<a href="https://agentmods.dev/skills/kimsanguine/hplan/customer-reach"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/customer-reach/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for customer-reach

Your own site · 80×15
<a href="https://agentmods.dev/skills/kimsanguine/hplan/customer-reach"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/customer-reach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,363 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00106 $0.01363
Opus 5 $0.00053 $0.00681
Sonnet 5 $0.00021 $0.00273
Haiku 4.5 $0.00011 $0.00136

Measured 10d ago against content hash 31295c673f2a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

customer-reach scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

discover/skills/customer-reach/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Core Goal

"고객 발화 3건"을 pain.md에 채우기 위한 인터뷰 대상자 확보 도구. 발견(plan/search) → 초안 생성(linkedin/community/survey) → 기록(pain.md) 순서로 사용.

Trigger Gate

Use This Skill When

  • "인터뷰할 사람 어떻게 찾아요?" → plan
  • "LinkedIn에서 DM 보내고 싶어요" → linkedin
  • "커뮤니티에 포스팅하려고요" → community
  • "설문지 만들어줘요" → survey
  • evidence-gate WARN → "실제 인터뷰 증거를 얻기 전에 이 스킬부터 시작하세요"
  • "인터뷰 약속을 잡았는데 뭘 물어봐야 할지 모르겠어" → --mode interview-questions

Route to Other Skills When

  • 인터뷰 결과를 기록할 때 → harness/pain.md 직접 작성 (커맨드 없음)
  • 기록 후 검증 → evidence-rubric / evidence-gate
  • 소크라테스 가정 심문 (인터뷰 전) → socratic-question
  • 인터뷰 결과를 pain.md에 기록한 후 → evidence-rubric (증거 품질 점검)

Instructions

mode: plan

  1. $ARGUMENTS의 ICP 설명을 읽어 타겟 확보 전략을 설계한다 (LLM)
  2. 3가지 채널 추천: LinkedIn / 커뮤니티(오픈채팅, Reddit, Discord) / 지인 네트워크
  3. 각 채널별 예상 성공률과 소요 시간 추정 (결정론: 고정 기준표 기반)
  4. harness/reach-plan.md에 저장

mode: linkedin

ICP에 맞는 LinkedIn cold DM 초안 생성:

안녕하세요 [이름]님,

저는 [내 소개 1줄]입니다.
[ICP의 특정 역할/상황]에서 [핵심 고충]을 어떻게 해결하시는지 15분 여쭤봐도 될까요?

보상 없이도 가능하지만, 원하신다면 [가치 교환 1줄]도 가능합니다.

ICP 분야에 따라 문구를 조정한다. 스팸 느낌 패턴 (긴급/할인/과장) 제거.

mode: community

커뮤니티 포스팅 초안:

  • 제목: "[ICP 역할] 분들께 15분 인터뷰 요청드립니다"
  • 내용: 무엇을 만드는지 1줄, 누구에게 물어보고 싶은지, 왜 당신의 의견이 중요한지
  • 플랫폼별 톤 조정: Reddit(영문/격식X) vs 카카오오픈채팅(한국어/친근함)

mode: survey

인터뷰 대체용 5문 이하 설문 초안:

  • Q1: 현재 [문제 영역]을 어떻게 해결하시나요? (객관식 4개)
  • Q2: 가장 번거로운 점은? (서술 or 객관식)
  • Q3: 기존 해결책에 얼마나 만족하시나요? (1-5)
  • Q4: 새로운 도구가 생긴다면 가장 원하는 기능 1가지는?
  • Q5: 인터뷰 참여 의향 (예/아니오 + 연락처 선택)

mode: interview-questions

인터뷰 약속을 잡은 후, 어떤 질문을 할지 설계합니다. socratic-question이 내 가정을 심문한다면, interview-questions는 고객에게 던질 질문을 설계합니다.

입력: $ARGUMENTS의 ICP 설명 + 검증하고 싶은 핵심 가정 (없으면 docs/brainstorm-assumptions.md에서 로드)

  1. JTBD(Jobs to Be Done) 프레임으로 핵심 질문 3~5개 생성 (LLM):

    • "마지막으로 이 문제를 겪은 게 언제인가요?" (사실 확인형)
    • "그때 어떻게 해결하셨나요?" (현재 우회책 확인)
    • "이 과정에서 가장 번거로운 부분은 뭔가요?" (pain 깊이 측정)
  2. 각 질문에 대해 인터뷰 지침 추가:

    • 좋은 답변 신호 (pain 실재 확인)
    • 나쁜 답변 신호 (pain 없거나 의례적 답변)
    • 후속 질문 힌트
  3. harness/interview-guide.md에 저장:

    # 인터뷰 가이드 — [ICP 설명]
    
    ## 핵심 가정
    - [검증할 가정 목록]
    
    ## 질문 세트
    ### Q1. [질문]
    - 좋은 답변 신호: ...
    - 나쁜 답변 신호: ...
    - 후속: ...
    

Read the full file on GitHub · 110 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 110 lines · 106 tokens per session scan A 31295c673f2a

Subscribe to this mod's changes

customer-reach is a skill published in the GitHub repository kimsanguine/hplan (2 stars, last pushed 24d ago), licensed MIT. It adds 106 tokens to every session and 1,363 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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