LEANSpark 广告投放与再营销|简诗 AI

SKILL商业运营
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围绕“LEANSpark 广告投放与再营销”提供具体执行方法,涵盖用户需求、方案设计、优先级和验证迭代。

一句话选型结论

按资料字段整理

LEANSpark 广告投放与再营销|简诗 AI是商业运营类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类商业运营 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_fccf428b/sales-leanspark
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

商业运营
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_fccf428b仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791737515
正文体量14.6 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
写运营方案

按目标产出活动方案与执行清单。

分析数据与指标

从报表里找问题、给结论。

整理客户与销售材料

把客户信息与话术整理成可直接用的材料。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在商业运营场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
LEANSpark 广告投放与再营销|简诗 AI当前 未标注权限 未标注 未标注 未标注 有
MX 股票筛选器 联网访问 ClawHub 市场 79.9 万 OpenClaw 有
交易 未标注权限 ClawHub 市场 58.1 万 OpenClaw 有
营销技巧 只读 ClawHub 市场 54.0 万 OpenClaw 有
百灵鸟好 联网访问 GitHub 仓库 52.6 万 Codex、Claude、Cursor 有
股票信息浏览器 接触敏感数据 ClawHub 市场 40.5 万 OpenClaw 有

SKILL.md 全文

14.6 KB
模型实际读到的说明书原文,来自技能仓库

LEANSpark Platform Help

LEANSpark (leanspark.ai) is "the decision layer for founders" — an AI startup-validation system
by Ash Maurya (creator of the Lean Canvas, author of Running Lean), built on his Lean Canvas +
Continuous-Innovation framework. Unlike the one-shot "score my idea" validators, it keeps persistent
memory
of your model, interviews, and experiments, runs two-week Plan-Do-Check-Act cycles over
~90 days, and has a Contradiction Detection engine that flags where your plan and your evidence stop
agreeing — ending each sprint in a Persevere / Pivot / Pause decision. It also exposes an OAuth
MCP server
so Claude Code / Cursor can read your validated model.

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

Ask only what you can't infer from the user's prompt:

  1. Where are you? (a) building/stress-testing a Lean Canvas, (b) staring at a seven-dimension result
    or a flagged contradiction and unsure what to do, (c) mid-cycle and deciding persevere/pivot/pause,
    (d) wiring the MCP server into Claude Code / Cursor, (e) weighing free tier vs a paid credits plan,
    (f) asking whether it has a REST API / how to get data out.
  2. What's the idea, in one sentence? (helps interpret the canvas and the seven-dimension stress test).
  3. What evidence have you got so far — a hunch, customer interviews, an experiment result, a landing
    page, a waitlist, or someone offered to pay?

Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.

Step 2 — Route or answer directly

LEANSpark is one validation system. Much of the thinking a founder needs is tool-agnostic — route it:

The user's real question Route to
Should I trust this? How do I actually validate? The evidence ladder, comparing validators /sales-idea-validation
Build the smoke-test / fake-door landing page the demand test runs on /sales-funnel
Grow a pre-launch waitlist and capture signups /sales-audience-growth
Run the ~10 customer interviews that feed the evidence side /sales-idea-validation
Reddit-based real-demand signal to cross-check the model /sales-trendseeker

When routing, give the exact command: "This is a validation-method question — run: /sales-idea-validation {the user's original question}"

Answer directly only for LEANSpark-specific mechanics (the 12-block canvas, the seven dimensions,
the contradiction engine, PDCA cycles, persevere/pivot/pause, credits & modes, the MCP server).

Step 3 — LEANSpark platform reference

Read references/platform-guide.md for the full reference — the 12-block canvas and seven-dimension
stress test, the persistent-memory / contradiction-detection / PDCA loop, credits & the Reactive /
Attentive / Proactive modes, pricing/plan gates, the MCP server (setup, scopes, the one write-back tool),
and how to get data out. Read references/leanspark-api-reference.md for the MCP surface verbatim.

Answer using only the relevant section — don't dump the whole guide.

Step 4 — Actionable guidance

  • The canvas, seven-dimension score, and contradiction flags are coaching over your inputs — not demand.
    LEANSpark's edge over pure-LLM validators is that it's built around running real experiments and
    interviews and tracking that evidence over 90 days — but a well-organized canvas with no real customer
    evidence fed in is still just a plan. Keep the structure and the flagged contradictions; take the
    go/no-go from a real behavior test (smoke test / pre-sale). Route that to /sales-funnel + /sales-audience-growth.
  • The Contradiction Detection engine only checks things you told it — it can't see that your evidence base is imaginary.
    It flags where your plan and your logged evidence disagree (e.g. positioning vs. interview feedback),
    which is genuinely useful — but "no contradictions" means your inputs are internally consistent, not
    that strangers want it. Treat a flagged contradiction as your next thing to go resolve with real customers.
  • Feed it real evidence, then let it compound. Its whole value proposition is persistent memory —
    log actual interviews and experiment results, not guesses. Tell the user the system is only as good as
    the evidence they put in; thin or invented inputs yield confident but empty coaching.
  • Read persevere/pivot/pause as a cycle verdict, not a final grade. Each two-week PDCA cycle ends in
    Persevere (evidence supports the current model — keep testing), Pivot (a core assumption failed — change
    the model), or Pause (stop/park). It's a checkpoint against pre-set success criteria, so anchor it on
    whether the experiment hit its threshold, not on how the AI "feels" about the idea.
  • Present all pricing as best-effort, and explain credits + modes. Plans are credit-metered (Free
    ~25 credits/mo, paid tiers up to ~1,600) and pricing may have changed — point to leanspark.ai/pricing.
    The "mode" scales with tier: Reactive (you drive) → Attentive (proactive nudges) → Proactive
    (daily briefings, autonomous campaigns, background watchers). The free Lean Canvas builder is usable
    without a card.
  • The MCP server is the only programmatic surface — there's no REST API or webhooks. To use LEANSpark
    data in Claude Code: claude mcp add --transport http leanspark https://leanspark.ai/mcp, then authorize
    via OAuth in the browser and pick which idea to expose. It's read-mostly (validated canvas,
    positioning, competitive evidence, voice profile) plus one write-back tool to save deliverables into
    your active experiment; each connection is scoped to a single idea and revocable in Settings → MCP.
  • Don't confuse it with the free five-minute canvas generators or one-shot scorers. LEANSpark is
    deliberately a longitudinal 90-day process built by Ash Maurya, the creator of the Lean Canvas
    (persistent memory + two-week PDCA cycles + contradiction detection), not a 60-second verdict — and it's
    credit-metered (always present its pricing as best-effort and point to leanspark.ai/pricing). When a
    user weighs it against a fast/free canvas generator, lead with that longitudinal-vs-one-shot contrast and
    the creator-of-the-Lean-Canvas authenticity, note the free Lean Canvas builder needs no card, and point
    them to /sales-idea-validation for the full comparison.

If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.

Gotchas

Best-effort from research (2026-07) — the AI-validator tool set churns fast; verify tiers, the modes, credits, and the MCP scopes at leanspark.ai.

  • A tidy canvas is not validated demand. Even from Ash Maurya, the creator of the Lean Canvas, LEANSpark
    coaches and tracks your evidence — it doesn't manufacture demand. Follow every green light with a real-behavior test.
  • Contradiction detection is bounded by your inputs. It surfaces conflicts between what you logged;
    it can't tell you that the whole evidence base is thin or imagined. Garbage in, confident coaching out.
  • It's a 90-day commitment, not an instant score. The value is the persistent memory + PDCA cadence
    over weeks; a founder wanting a one-shot number will find the deliberate pace a mismatch.
  • Credit-metered with tiered "modes." Free is ~25 credits/mo in Reactive mode; proactive/autonomous
    behavior (daily briefings, background watchers) is top-tier only. Confirm current credits/prices at leanspark.ai/pricing.
  • No REST API or webhooks — MCP only. The automation surface is the OAuth MCP server (read-mostly +
    one write-back tool), scoped to one idea. Don't plan a REST integration; there isn't one.
  • Persevere/Pivot/Pause depends on pre-set success criteria. If you don't define what "success" for a
    cycle looks like up front, the verdict drifts toward an AI opinion — set the threshold before the sprint.

Related skills

  • /sales-idea-validation — the tool-agnostic validate-before-building method and the full idea-validator comparison (start here to decide whether to trust any score or system)
  • /sales-funnel — build the smoke-test / fake-door landing page the real demand test runs on
  • /sales-audience-growth — grow a pre-launch waitlist and capture signups once you're testing demand
  • /sales-trendseeker — Reddit-based real-demand signal to cross-check LEANSpark's model
  • /sales-juma — the other tool in this cluster with a callable MCP server, if you're comparing MCP surfaces
  • /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 -a claude-code

Examples

Example 1: "LEANSpark flagged a contradiction between my plan and my interviews — now what?"

User says: "The Contradiction Detection thing says my positioning conflicts with my customer feedback. What do I do?"
Skill does: Explains the engine compares what you logged (positioning vs. interview evidence) and that
the flag is your top thing to go resolve with real customers — not a verdict on the idea. Suggests re-running
the affected experiment with a sharper customer/problem, and reminds the user that "no contradiction" only
means internal consistency, not demand — the go/no-go still comes from a smoke test / pre-sale
(/sales-funnel + /sales-audience-growth).
Result: The founder treats the contradiction as a concrete next experiment instead of a pass/fail grade.

Example 2: "Is LEANSpark worth it over a free five-minute lean-canvas generator?"

User says: "I can get a lean canvas in five minutes elsewhere for free — why pay for LEANSpark?"
Skill does: Frames LEANSpark as a longitudinal system (persistent memory + two-week PDCA cycles +
contradiction detection over ~90 days from the Lean Canvas creator), not a one-shot canvas or score — so
its value only shows up if you actually run experiments and log evidence over weeks. Notes the free Lean
Canvas builder is usable without a card, explains credits/modes as best-effort, and points to
/sales-idea-validation to compare it against the fast/free scorers.
Result: The user picks based on whether they'll commit to the process, not the price tag.

Example 3 (automation): "Can I pull my LEANSpark model into Claude Code — is there an API?"

User says: "I want my validated canvas and positioning available in Claude Code / Cursor. Does LEANSpark have an API or webhook?"
Skill does: States there's no REST API or webhooks — the only programmatic surface is the OAuth
MCP server
: claude mcp add --transport http leanspark https://leanspark.ai/mcp, then authorize in the
browser and select which idea to expose. Explains it's read-mostly (validated canvas, positioning,
competitive evidence, voice profile) plus one write-back tool to save a deliverable into the active
experiment, scoped to one idea and revocable in Settings → MCP — so you can generate on-brand copy grounded
in the validated model, but you can't script a full data export.
Result: The user connects the MCP server and reads their model into Claude Code instead of hunting for a REST endpoint.

Troubleshooting

The seven-dimension result / coaching feels generic

Symptom: The stress test or the AI's guidance reads bland and could apply to any idea.
Cause: LEANSpark coaches over the evidence you've logged; with only a one-line idea and no real
interviews or experiments in memory, it has nothing specific to reason from.
Solution: Feed it real evidence — log actual customer interviews and experiment results, answer the
canvas blocks concretely (especially early adopters and the problem), and let the persistent memory
compound over a cycle. Then cross-check the model against a real-demand signal (/sales-trendseeker) and
a smoke test.

"I connected the MCP server but can't get my full data out"

Symptom: The MCP tools read the model but there's no bulk export / no REST endpoint.
Cause: LEANSpark is UI + MCP only — the MCP server is read-mostly with a single write-back tool, and
there is no public REST API or webhooks.
Solution: Use the MCP reads for what they're for (grounding content/positioning in Claude Code/Cursor)
and the one write-back tool to save deliverables into your active experiment. For anything the MCP doesn't
expose, copy from the UI. Don't design a pipeline around a REST API that doesn't exist.

Ran out of credits / confused by Reactive vs Proactive

Symptom: The tool stops doing work, or the "mode" behaves differently than expected.
Cause: Plans are credit-metered and the mode is tier-gated — Free (~25 credits/mo) is Reactive
(you initiate); proactive nudges and autonomous/always-on behavior are higher tiers only.
Solution: Confirm current credits and tier behavior at leanspark.ai/pricing. Budget credits for real
work (logging evidence, running cycles) rather than idle chat, and upgrade only if you genuinely want the
Attentive/Proactive daily-coaching behavior — the free Lean Canvas builder needs no card.

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常见问题

5 问
答案由本站依据条目字段整理
LEANSpark 广告投放与再营销|简诗 AI怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_fccf428b/sales-leanspark。安装方式为未标注。装完刷新技能列表或重开客户端。

LEANSpark 广告投放与再营销|简诗 AI支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

LEANSpark 广告投放与再营销|简诗 AI权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

LEANSpark 广告投放与再营销|简诗 AI收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

LEANSpark 广告投放与再营销|简诗 AI用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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