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Agent Skill · 研究检索

market-finder(市场发现者)

market-finder 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。

market-finder 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,权限为接触敏感数据;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 73,675 个 Skill 里排第 64,698。

总安装 294周安装 12GitHub Stars 45下载 94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:market-finder(市场发现者)
来源仓库:https://github.com/nimbleway/agent-skills
仓库路径:skills/market-finder
安装命令:
npx skills add https://github.com/nimbleway/agent-skills --skill market-finder
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/nimbleway/agent-skills --skill market-finder

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。

market-finder 实现高效的市场信息检索与筛选功能。适合根据关键词、行业或时间范围快速定位目标内容。可返回候选列表供进一步人工判断,降低信息过载风险。通过 npx skills add 安装,支持 Codex、Claude、Cursor、Gemini CLI 等宿主环境。

建议在使用前确认其是否触发联网请求及响应延迟情况。

来源仓库内路径:skills/market-finder

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/nimbleway/agent-skills --skill market-finder;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

    它需要接触敏感数据,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

market-finder 怎么安装?

在终端执行 npx skills add https://github.com/nimbleway/agent-skills --skill market-finder。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

market-finder 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。

market-finder 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

market-finder 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。

market-finder 用的人多吗?

公开数据是累计安装 294 次,近一周安装 12 次,下载 94 次,GitHub 45 星。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。

market-finder 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
market-finder

市场发现者 · 本页

29445接触敏感数据未明确有提醒

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

Market Finder

Market intelligence powered by Nimble Web Search Agents.

User request: $ARGUMENTS

Before running any commands, read references/nimble-playbook.md for Claude Code constraints (no shell state, no &/wait, sub-agent permissions, communication style).


Instructions

Step 0: Preflight

Run the preflight pattern from references/nimble-playbook.md (5 simultaneous Bash calls: date calc, today, CLI check, profile load, index.md load).

Also simultaneously:

  • mkdir -p ~/.nimble/memory/{reports,market-finder/checkpoints}
  • Check for existing checkpoints: ls ~/.nimble/memory/market-finder/checkpoints/ 2>/dev/null

From the results:

  • CLI missing or API key unset -> references/profile-and-onboarding.md, stop
  • Profile exists -> note industry keywords if any. Apply smart date windowing from references/nimble-playbook.md. Market-finder tweak: in quick refresh mode, skip enrichment and only discover new metros.
  • No profile -> fine. Market-finder doesn't require onboarding. Proceed to Step 1.

Step 1: Parse Request & Detect Mode

Parse $ARGUMENTS for business type, geography, qualifiers, and mode detection.

Mode detection

Check $ARGUMENTS for a reference list. Read references/audit-mode.md for the full detection signals and parsing rules.

SignalMode
Google Sheet URL, CSV path, or inline list of 3+ businessesAudit
Explicit audit language ("audit my list", "compare against", "gap analysis")Audit
No reference list providedDiscovery (default)

If a reference list is present but intent is ambiguous, ask: "Want me to audit your list against fresh discovery, or use it as a starting point?"

If audit language is detected but no reference list is provided, ask: "You mentioned auditing — please provide your list (Google Sheet URL, CSV file path, or paste inline)." Do not proceed with Audit mode until a reference list is received.

Extract fields
FieldRequiredSource
Business type / verticalYesUser input ("dentists", "SaaS CRM tools")
GeographyYes (except SaaS)User input ("Florida", "Austin TX", "nationwide")
Reference listAudit mode onlyGoogle Sheet URL, CSV path, or inline
Qualification criteriaOptionalUser input ("must have website", "10+ reviews")
Output preferenceOptionalUser input ("quick summary", "full dataset")

If both type and geography are clear from $ARGUMENTS, confirm briefly and proceed: "Finding dentists in Florida..." (or "Auditing your list against dentists in Florida..." in Audit mode)

If partial or ambiguous, ask one combined question (counts as 1 of max 2 AskUserQuestion prompts):

Use AskUserQuestion with up to 3 questions:

  1. Vertical -- "What type of business?" with options: Healthcare, SaaS/Software, Restaurants/Food, Legal/Financial, Auto/Home Services, Other
  2. Geography -- "What geography?" (free text or: City, State, Region, Nationwide)
  3. Depth -- "Quick scan or comprehensive discovery?"

Skip questions already answered by $ARGUMENTS.

Depth modes (determines how much work each step does):

DepthDiscoveryEnrichmentVerificationDistribution
Quick scanAll sources, 1 passSkip (or top 5 only)Top 5 entitiesOffer
ComprehensiveAll sources + fallback retriesFullAll entitiesOffer

Step 2: Vertical Detection & Preset Loading

Read references/vertical-presets.md and match the user's business type against preset trigger keywords.

MatchAction
Clear matchLoad that preset's WSA routing and query pattern
Partial matchConfirm: "This looks like Healthcare. Use healthcare presets?"
No matchUse Custom preset with user's keywords
SaaS matchSwitch to non-geographic pipeline (no geo-tiling)

Note which discovery WSAs and enrichment WSAs the preset specifies.

Step 3: Geographic Scoping

Skip this step for SaaS vertical (no geography needed).

Geography levelTiling strategy
CitySingle query, no tiling
Metro areaSingle query per WSA
StateTile by top 5-10 metros in the state
RegionTile by states, then top metros per state
NationwideTile by all states, then top metros per state

Estimate API calls: metros * discovery_wsas * (1 + enrichment_ratio) where enrichment_ratio is ~0.3. Follow the Scaled Execution pattern from references/nimble-playbook.md to choose execution tier (individual / batch / multi-batch / confirmation gate):

Estimated API calls: ~1,560 (50 states x 8 metros x 3 WSAs + enrichment)
This is a nationwide search. Proceed? [Y/n]

Derive a slug for checkpointing: lowercase, hyphenated, includes vertical + geo (e.g., dentists-florida, saas-crm-tools, hvac-nationwide).

Step 4: Check for Existing Checkpoint

Follow the Checkpointing & Resume pattern from references/memory-and-distribution.md.

Check: cat ~/.nimble/memory/market-finder/checkpoints/{slug}/discovery.json 2>/dev/null

  • Checkpoint found -> offer: "Found previous run ({N} entities from {date}). Resume and fill gaps, or start fresh?"
  • No checkpoint -> proceed to Step 5

Step 5: WSA Discovery & Execution

5a: Discover available WSAs

For each target domain in the selected vertical preset, discover current WSAs:

nimble agent list --search "{domain}" --limit 20

Run these searches simultaneously (one per target domain). From the results:

  1. Filter by entity_type (SERP for discovery, PDP/Profile for enrichment)
  2. Prefer managed_by: "nimble" over managed_by: "community"
  3. If no WSA found for a domain, mark it for nimble search fallback
  4. If no WSAs found for ANY domain, fall back entirely to nimble search for all metros

Then validate each discovered WSA's input params:

nimble agent get --template-name {discovered_name}

Cache the discovered WSA names + params for the rest of the run.

5b: Geographic discovery (all except SaaS)

For each metro in the tiling plan, run the discovered WSAs simultaneously:

nimble agent run --agent {maps_wsa} --params '{...validated params...}'
nimble agent run --agent {yelp_wsa} --params '{...validated params...}'

Run tertiary domain WSAs only if the preset includes them AND primary + secondary return < 10 combined unique results for that metro.

Choose execution tier per the Scaled Execution pattern in references/nimble-playbook.md (based on total estimated calls from Step 3).

5c: SaaS discovery (non-geographic)

SaaS skips WSA discovery. Run the two-pass search queries defined in the SaaS preset from references/vertical-presets.md:

  • Pass 1 -- Product discovery: G2, Capterra, general, ProductHunt, GitHub
  • Pass 2 -- Financial discovery: Crunchbase, funding news, market landscape

Both passes run simultaneously. Pass 2 is critical -- without it, funding and traction data will be missing or wrong.

5d: Fallback

If no WSA was found for a target domain, or if a WSA fails for any metro:

nimble search --query "[type] in [metro]" --max-results 20 --search-depth lite

After discovery:

  1. Parse all results into a unified entity list
  2. Deduplicate following the Entity Deduplication pattern from references/nimble-playbook.md: place_id -> domain -> fuzzy name + city
  3. Track source_count per entity (how many WSAs/sources found it)
  4. Save checkpoint: ~/.nimble/memory/market-finder/checkpoints/{slug}/discovery.json

Step 6: Enrichment

Run enrichment using the WSAs discovered in Step 5a for the preset's enrichment target domains. Prioritize entities with the highest source count first. Choose execution tier per Scaled Execution in references/nimble-playbook.md.

nimble agent run --agent {enrichment_wsa} --params '{...validated params...}'

Only run enrichment WSAs that apply to the current vertical's enrichment targets (see references/vertical-presets.md). Skip entities without the required ID/URL for the enrichment WSA.

Save checkpoint: ~/.nimble/memory/market-finder/checkpoints/{slug}/enrichment.json

Step 6b: Financial Verification (SaaS vertical)

For SaaS entities, verify funding claims before reporting. Never label a company's funding stage without a source.

For each entity in the top results (top 5 in quick scan, all in comprehensive):

nimble search --query "{company name} funding raised series" --max-results 5 --search-depth lite
  • Source found: Use the sourced amount and date
  • No source found: Display "Undisclosed" -- never guess "Early stage" or "Bootstrapped"

This step prevents publishing unverified financial claims. It's fast (one search per entity, lite depth) and catches recent funding rounds that directory sites miss.

Step 7: Deduplication & Scoring

Final deduplication: Run a final dedup pass across all phases following the Entity Deduplication pattern from references/nimble-playbook.md. Merge fields from multiple sources into a single record per entity.

Discovery strength scoring (skill-specific, varies by vertical):

Geographic verticals (Healthcare, Restaurants, Legal, Auto/Home, Custom):

LevelCriteria
High3+ sources OR 2+ sources with reviews > 50
Medium2 sources OR 1 source with reviews > 10
Low1 source only, few/no reviews

SaaS vertical (funding + directory presence matter more than review count):

LevelCriteria
HighVerified funding > $10M OR 3+ directory sources OR 1000+ G2 reviews
MediumVerified funding < $10M OR 2 sources OR 100+ G2 reviews
Low1 source only, no verified funding, few reviews

Display as: *** High, ** Medium, * Low

Step 7b: Audit Comparison (Audit mode only)

Skip this step in Discovery mode.

Read references/audit-mode.md for the full matching algorithm, normalization rules, and output template.

  1. Parse reference list — detect format (Google Sheet / CSV / inline), extract records, normalize to {name, domain, city, state, phone} per the parsing rules in references/audit-mode.md
  2. Run three matching layers in order (domain → name+city → phone). Once an entity matches at any layer, stop. Track which layer produced the match.
  3. Categorize every entity:

- matched — in both reference list and discovery results - discovered_only — found by discovery, not in reference list - reference_only — in reference list, not found by discovery

  1. Calculate coverage score — matched / reference_count × 100

Proceed to Step 8 with the categorized results.

Step 8: Output

# Market Finder: [Business Type] in [Geography]
*Found [N] businesses | [Date] | Strength: [H] High, [M] Medium, [L] Low*

## Summary
- **Total discovered:** [N] unique businesses across [M] metros
- **Geographic breakdown:** [top 5 metros by count]
- **Source coverage:** [list each source used with entity counts]

## Top Results (High Strength)

| # | Name | Location | Rating | Reviews | Strength | Sources |
|---|------|----------|--------|---------|----------|---------|
| 1 | Acme Dental | Miami, FL | 4.8 | 312 | *** High | Maps, Yelp, BBB |
| 2 | WidgetCo Health | Orlando, FL | 4.6 | 89 | *** High | Maps, Yelp |
...

## All Results by Geography

### Miami, FL ([n] businesses)
[Table of businesses in this metro]

### Orlando, FL ([n] businesses)
[Table of businesses in this metro]
...

## What's Missing
[Data gaps: metros with low coverage, entities without websites, etc.]

SaaS output variant (when vertical is SaaS, replace "All Results by Geography" with tier-based grouping):

## Players by Tier

### Pure-Play (dedicated to this vertical)
| # | Name | Domain | Funding | Key Metric | Strength | Sources |
...

### Adjacent (feature overlap from larger platforms)
| # | Name | Domain | Funding | Key Metric | Strength | Sources |
...

### Open Source
| # | Name | Repo | Stars | Key Metric | Strength | Sources |
...

Source links are mandatory. Every entity must have at least one clickable source URL (Google Maps link, Yelp listing, website, BBB profile, G2 page, or GitHub repo).

Audit output variant (when in Audit mode, replace the Discovery output above): Use the audit output template from references/audit-mode.md. Key sections: Summary with coverage score, Matched table, Discovered Only table (expansion candidates), Reference Only table (coverage gaps), and "What This Means" interpretation.

Step 9: Save to Memory

Make all Write calls simultaneously:

Discovery mode:

  • Report -> ~/.nimble/memory/reports/market-finder-{slug}-{date}.md
  • Entity data -> ~/.nimble/memory/market-finder/{slug}/entities.json

Audit mode:

  • Report -> ~/.nimble/memory/reports/market-finder-audit-{slug}-{date}.md
  • Structured data -> ~/.nimble/memory/market-finder/{slug}/audit-{date}.json (all three categories with match metadata)

Both modes:

  • Profile -> update last_runs.market-finder in ~/.nimble/business-profile.json (only if profile exists)
  • Follow the wiki update pattern from references/memory-and-distribution.md: update index.md rows for all affected entity files, append a log.md entry for this run.
  • Clean up checkpoint (complete run) or keep (partial run)

Step 10: Share & Distribute

Always offer distribution -- do not skip this step. Follow references/memory-and-distribution.md for connector detection, sharing flow, and source links enforcement.

Notion: full results table as a dated subpage. Slack: TL;DR with total count + top 10 entities only.

Step 11: Follow-ups

Discovery mode follow-ups:

  • "Tell me more about #N" -> show full detail for that entity
  • "Filter by [criteria]" -> re-filter existing results
  • "Expand to [new geography]" -> add metros and re-run discovery
  • "Export as CSV" -> generate CSV from entities.json
  • "Run enrichment on all" -> extend enrichment beyond top entities
  • "Audit against my existing list" -> switch to Audit mode with this run's results
  • "Looks good" -> done

Audit mode follow-ups:

  • "Export discovered-only as CSV for outreach?" -> CSV of expansion candidates
  • "Investigate reference-only gaps?" -> targeted searches for reference_only entries
  • "Run company-deep-dive on new discoveries?" -> deep research on discovered_only
  • "Re-run with a different geography?" -> audit the same list against a new area
  • "Looks good" -> done

Sibling skill suggestions:

Next steps: - Run company-deep-dive for a full 360 profile on any business from this list - Run competitor-positioning to compare top players in this market - Run local-places for neighborhood-level discovery with social enrichment and maps

Sub-Agent Strategy

For large jobs, nimble agent run-batch handles WSA parallelism server-side (see Scaled Execution in references/nimble-playbook.md). Sub-agents are useful for preparing batch inputs and processing results, not for running individual WSA calls.

Use nimble-researcher agents (agents/nimble-researcher.md) when:

  • Building metro query lists for large geographies (one agent per state)
  • Processing and deduplicating batch results in parallel

Follow the sub-agent spawning rules from references/nimble-playbook.md (bypassPermissions, batch max 4, fallback on failure).


Agent Teams Mode (Dual-Mode)

Check at startup: echo $CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS

Team mode (flag set): Spawn teammates for parallel phases:

  • Discovery teammate(s): Run all discovery WSAs across metro batches
  • Enrichment teammate: Run enrichment WSAs for top entities
  • Lead (you): Coordinate, scope, deduplicate, score, generate output

Solo mode (flag not set): Standard sequential flow from Steps 5-8.


Error Handling

See references/nimble-playbook.md for the standard error table (missing API key, 429, 401, empty results, extraction garbage). Skill-specific errors:

  • WSA not found: Skip silently and rely on other discovery sources. Log which WSAs were unavailable.
  • Search 500/timeout: Retry once without --focus flag. If still failing, retry with a simplified query. Log the failure but don't skip the entire search category -- partial data is better than none.
  • No results for metro: "No [type] found in [metro]. Skipping to next metro." Don't abort the entire job for one empty metro.
  • Ambiguous business type: "Did you mean [option A] or [option B]?" (e.g., "practice" could be medical, dental, legal)
  • SaaS with geography: If the selected preset has no geo-tiling but the user specified a geography, offer it as a search qualifier instead.
  • Reference list parse failure: If Google Sheet extraction returns garbage or CSV is malformed, ask the user to paste the data inline instead.
  • Empty reference list: If parsed list has 0 valid records, warn and offer to switch to Discovery mode.
  • No matches found: If all three matching layers produce zero matches, report it — a 0% coverage score is valid and informative.

平台分布

Codex

36.83%
按下载量换算35

Claude

29.23%
按下载量换算27

Cursor

16.91%
按下载量换算16

Gemini CLI

9.75%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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