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Agent Skill · 研究检索

competitor-intel(竞争对手英特尔)

competitor-intel 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。

competitor-intel 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 73,674 个 Skill 里排第 62,001。

总安装 343周安装 14GitHub Stars 45下载 110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:competitor-intel(竞争对手英特尔)
来源仓库:https://github.com/nimbleway/agent-skills
仓库路径:skills/competitor-intel
安装命令:
npx skills add https://github.com/nimbleway/agent-skills --skill competitor-intel
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 competitor-intel

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。

用于查找竞争对手情报和市场分析相关资料。适合在 Codex、Claude、Cursor、Gemini CLI 中基于关键词获取信息。可配合来源仓库核验具体内容,但需注意权限和操作风险。安装方式:通过 npx 从 GitHub 仓库添加,建议先确认网络访问权限。注意:搜索结果可能涉及商业敏感信息,使用时应遵守合规要求。

来源仓库内路径:skills/competitor-intel

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 competitor-inte…;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

competitor-intel 怎么安装?

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

competitor-intel 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。

competitor-intel 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。

competitor-intel 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。

competitor-intel 用的人多吗?

公开数据是累计安装 343 次,近一周安装 14 次,下载 110 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。

competitor-intel 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
competitor-intel

竞争对手英特尔 · 本页

34345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

Competitor Intelligence

Real-time competitive intelligence powered by Nimble's web data APIs.

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).

From the results:

  • CLI missing or API key unset → references/profile-and-onboarding.md, stop
  • Profile exists → read ~/.nimble/memory/competitors/index.md to identify which competitor files exist and their last-updated dates. If the index doesn't exist (first run or upgrade), fall back to reading all ~/.nimble/memory/competitors/*.md directly — the index is an optimization, not a gate. Then load the relevant competitor files for known signals (used for dedup in Steps 3 + 5). Follow cross-references ([[path/entity]] links) to load related context. Determine mode using smart date windowing from references/nimble-playbook.md:

- Full mode: first run OR last run > 14 days ago - Quick refresh: last run < 14 days ago - Same-day repeat: if last_runs.competitor-intel is today, check if a report already exists at ~/.nimble/memory/reports/competitor-intel-[today].md. If so, ask: "Already ran today. Run again for fresh data?" Don't silently re-run. - Skip to Step 2

  • No profile → Step 1

Note: Step 2 (WSA Discovery) runs after onboarding but before any research.

Step 1: First-Run Onboarding (2 prompts max)

Prompt 1 — ask in plain text (NOT AskUserQuestion with options):

"What's your company's website domain? (e.g., acme.com)"

Verify — make two Bash calls simultaneously:

  • nimble search --query "[domain]" --include-domain '["[domain]"]' --max-results 3 --search-depth lite
  • nimble search --query "[domain] company" --max-results 5 --search-depth lite

Prompt 2 — confirm company + choose competitor method (use AskUserQuestion):

I found that [Company] ([domain]) is [brief description]. Is this right? And how should I find your competitors? - Yes — find competitors for me - Yes — I'll list them myself - Wrong company — let me clarify

If "find competitors", make three Bash calls simultaneously:

  • nimble search --query "[Company] competitors" --max-results 10 --search-depth lite
  • nimble search --query "[Company] vs" --max-results 10 --search-depth lite
  • nimble search --query "[Company] alternatives" --max-results 5 --search-depth lite

Propose the list. Once the user confirms, create the profile and start Steps 2+3. When creating the profile, also ask for or infer each competitor's domain and the user's industry keywords. See references/profile-and-onboarding.md for the full profile schema (company, competitors with domains/categories, industry_keywords, integrations, preferences).

Step 2: WSA Discovery

For each competitor domain and the user's domain, discover available WSAs:

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

Run one search per domain simultaneously. From the results, filter for WSAs with entity_type matching SERP or PDP, prefer managed_by: "nimble", and validate each with nimble agent get --template-name {name}. Cache discovered WSA names + params for the run. Use discovered WSAs alongside nimble search in Steps 3-4 for richer data. If no WSAs found, continue with nimble search alone.

Step 3: Research the User's Company

Use --include-domain to avoid noise from generic company names. Make two Bash calls:

  • nimble search --query "product updates OR changelog OR releases" --include-domain '["[company-domain]"]' --start-date "[start-date]" --max-results 5 --search-depth lite
  • nimble search --query "[UserCompany] news" --focus news --start-date "[start-date]" --max-results 5 --search-depth lite

Fallback if < 3 results: nimble search --query "blog" --include-domain '["[company-domain]"]' --max-results 5 --search-depth lite

Step 4: Parallel Research Per Competitor (sub-agents)

Read references/competitor-agent-prompt.md for the full agent prompt template. Follow the sub-agent spawning rules from references/nimble-playbook.md (bypassPermissions, batch max 4, explicit Bash instruction, fallback on failure).

Spawn nimble-researcher agents (agents/nimble-researcher.md) with mode: "bypassPermissions". Customize the prompt template with each competitor's name, domain, start-date, known signals from memory (loaded in Step 0), and any discovered WSA names from Step 2 so agents can use them for enrichment.

Call estimation & Scaled Execution: Before launching agents, estimate total API calls: ~6 searches per competitor × N competitors + ~2 industry searches + extractions. For 2+ competitors (12+ calls), tell agents to use extract-batch for page extractions instead of individual calls. See the Scaled Execution pattern in references/nimble-playbook.md for tier selection.

Also run industry searches directly (not in sub-agents), using industry_keywords from the business profile:

  • nimble search --query "[industry_keyword] AI agents OR automation" --focus news --start-date "[start-date]" --max-results 5 --search-depth lite
  • nimble search --query "[industry_keyword] regulation OR compliance OR pricing" --focus news --start-date "[start-date]" --max-results 5 --search-depth lite

Step 5: Deep Extraction

Extract signals that need date verification OR richer detail. See references/nimble-playbook.md → "Signal Date Validation" → "Verification Budget" for the full rules.

Must extract:

  • All P1 signals (funding, M&A, leadership) — need confirmed details AND date verification
  • Any signal with DATE_CONFIDENCE: LOW — event date needs verification from page content
  • Any signal where SOURCE_TYPE: DERIVATIVE — confirm the event date from the actual page content

Extract if useful:

  • P2 signals where the snippet lacks a date or key detail

Skip: P3 signals with DATE_CONFIDENCE: HIGH.

Make one Bash call per URL, all simultaneously:

nimble extract --url "https://..." --format markdown

For extraction failures, follow the fallback in references/nimble-playbook.md.

When reading extracted content, determine the actual event date from the article body (not just the page header date). Look for: explicit dates tied to the event, temporal language ("last September", "in Q3"), and datelines.

Step 5.5: Signal Validation

Before building the report, validate every signal's freshness. See references/nimble-playbook.md → "Signal Date Validation" for the full pattern.

For each signal from Step 3, classify it:

CheckResultAction
EVENT_DATE within freshness window + not in memoryNEWInclude
EVENT_DATE within window + updates a known signalUPDATEDInclude as update
EVENT_DATE outside freshness windowSTALEDrop — old event, new article
DATE_CONFIDENCE: LOW + couldn't verify in Step 4UNCERTAINDrop with note

P1 corroboration (mandatory) — any P1 signal with NEEDS_CORROBORATION: true MUST be corroborated before it can enter the report. This is a hard gate, not a suggestion.

For each flagged P1, run:

nimble search --query "[Company] [event summary]" --max-results 5 --search-depth lite

Look for the primary source (company blog, press release, official filing). If the primary source dates the event outside the freshness window, reclassify as STALE. If no primary source is found, reclassify as UNCERTAIN and drop.

Drop rules:

  • Event date is outside the freshness window → STALE
  • Only sourced from derivative/aggregator sites with no corroborating primary or major outlet → UNCERTAIN, drop unless verified via extraction
  • Content clearly describes a past event (temporal language like "last year", "back in Q3", "months ago") with event date outside the window → STALE

After validation, you should have a clean list of NEW and UPDATED signals only.

Step 6: Analysis & Output

Full mode (first run or > 14 days since last) — structured briefing:

  • TL;DR — 3-5 P1 signals, most recent first, every one dated with source
  • Per competitor — "Recent" and "Older Context" subsections, "Where They Win vs. Where You Win" table, "What This Means" (1-2 sentences)
  • Industry Trends — signals from industry searches
  • Your Company Update — releases/news from Step 2
  • Cross-Competitor Patterns — converging trends
  • What This Means for [Company] — strategic implications + suggested actions

Quick refresh mode (last run < 14 days) — short format:

  • New Signals — dated, with competitor name, priority, and clickable source URL
  • Nothing New — list competitors with no new signals
  • Action Items — only if something requires attention

Core rules:

  • Every signal MUST have a verified event date. Only events that happened within the freshness window qualify as new signals — older events are background context.
  • Only include signals classified as NEW or UPDATED in Step 5.5. STALE and UNCERTAIN signals have already been dropped.
  • Deduplicate against ~/.nimble/memory/competitors/*.md — only surface NEW findings.
  • Say "nothing notable this period" rather than padding with fluff.
  • P3 signals: mention briefly or omit if report is long.

Step 7: Save & Update Memory

Only persist signals that passed Step 5.5 validation (classified as NEW or UPDATED). Do not write STALE or UNCERTAIN signals to competitor memory files.

Make all Write calls simultaneously:

  • Report → ~/.nimble/memory/reports/competitor-intel-[date].md (save the full briefing, not a summary — this is the local source of truth)
  • Per competitor → append validated signals to ~/.nimble/memory/competitors/[name].md (use the format documented in references/memory-and-distribution.md). Add [[path/entity]] cross-references for relationships discovered during research (e.g., key people → [[people/name]], related competitors → [[competitors/name]]).
  • Profile → update last_runs.competitor-intel in ~/.nimble/business-profile.json
  • 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.

Step 7.5: Synthesis Page Generation

If 3+ competitors were researched in this run, OR the existing ~/.nimble/memory/synthesis/competitive-landscape.md has stale source timestamps (source entity files were updated since generation), generate or refresh the synthesis page.

Use the nimble-analyst agent (agents/nimble-analyst.md) with mode: "bypassPermissions" to synthesize patterns across all competitor files. The agent should read all ~/.nimble/memory/competitors/*.md files and produce a competitive-landscape.md following the format in references/memory-and-distribution.md — market map, feature comparison, pricing comparison, key patterns, and strategic implications. Cite source entity files with [[competitors/name]] links.

Also append any unanswered questions to ~/.nimble/memory/backlog.md (e.g., competitors where key data like pricing or funding is missing).

After generating, update index.md with the synthesis page entry.

Step 8: 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.

Step 9: Follow-ups

  • Go deeper on a competitor → more focused searches
  • Skip a competitor → update preferences.skip_competitors
  • Add a competitor → update competitors, create memory stub
  • "Looks good" → done

Sibling skill suggestions:

Next steps: - Run competitor-positioning to analyze how competitors present themselves online - Run company-deep-dive for a full 360 profile on any competitor from this report - Run meeting-prep if you're meeting with someone at a competitor

Agent Teams Mode (Dual-Mode)

Check at startup: echo $CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS

Team mode (flag set): Spawn full teammates instead of sub-agents:

  • Lead (you): Assign competitors, synthesize the final briefing
  • One teammate per competitor: Uses references/competitor-agent-prompt.md with discovered WSAs — teammates can message each other when they find overlapping signals
  • Devil's Advocate (optional): Challenges findings, looks for blind spots
  • Lead synthesizes a cross-validated briefing with higher confidence

Solo mode (flag not set): Standard sub-agent flow from Step 3.


Error Handling

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

  • Search 500: Retry once without --focus flag. If still failing, retry with a simplified query (shorter terms, no date filter). Log the failure but don't skip the competitor.
  • Search timeout: Retry once, then skip that call and continue — consistent with the playbook's timeout policy.

平台分布

Codex

37.11%
按下载量换算41

Claude

31.59%
按下载量换算35

Cursor

19.55%
按下载量换算22

Gemini CLI

9.52%
按下载量换算10

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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