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influencer-discovery

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Influencer Discovery

达人挖掘

Find the right influencers for your brand by searching across platforms, screening for audience fit and authenticity, and building a tiered candidate list ready for scoring.
通过跨平台搜索、受众匹配度与真实性筛查，构建可直接用于评分的分层候选名单，为你的品牌找到合适的达人。

Quick Start

快速开始

Find 20 influencers in [niche] for [brand/product]
Find influencers in [niche] with 50K-200K followers on TikTok and Instagram,
based in [location], engagement above 4%, who have worked with brands like [brand]
Find 20 influencers in [niche] for [brand/product]
Find influencers in [niche] with 50K-200K followers on TikTok and Instagram,
based in [location], engagement above 4%, who have worked with brands like [brand]

Skill Contract

技能协议

  • Reads: brand/product, niche or category, target platforms, follower range, engagement floor, location/language, audience demographics, exclusions; prior 
    entity-registry
    brand profile and any 
    audience-mapper
    output if present in memory; existing roster records under 
    memory/creators/
    (dedupe the candidate pool against creators already rostered by creator-registry).
  • Writes: only with separate exact authorization, discovery results to 
    memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md
    — search criteria, candidate pool stats, per-influencer profiles, tiered shortlist with preliminary triage signals. Roster-worthy shortlisted creators (verified handles, contact path, audience stats) go as one-line updates to 
    memory/events/creators.ndjson
    only via a separately authorized 
    operation: propose
    request to 
    registry-events.py
    — only 
    creator-registry
    writes canonical records under 
    memory/creators/
    .
  • Promotes: only with separate exact authorization, durable facts (top-tier handles, confirmed niche/platform mix, competitor-saturated creators) to 
    memory/hot-cache.md
    .
  • Done when:
    • The required search criteria are present; otherwise stop with 
      NEEDS_INPUT
      and name the missing criteria without fabricating candidates.
    • A candidate pool exists with at least the requested count screened past follower, engagement, and brand-safety filters.
    • Each shortlisted influencer has a profile with metrics, audience read, and a preliminary discovery-triage signal that is not a STAR Suitability score.
    • A tiered shortlist (must-reach / strong / consider) is compiled with next-step pointers.
  • Primary next skill: fit-scorer — score and rank the discovered candidates with weighted criteria.
  • 读取内容：品牌/产品、细分领域或品类、目标平台、粉丝量范围、最低互动率、地区/语言、受众人群特征、排除条件；若内存中存在此前的
    entity-registry
    品牌档案以及
    audience-mapper
    输出结果，也会读取；同时读取
    memory/creators/
    下的现有达人记录（与creator-registry已收录的达人进行候选池去重）。
  • 写入权限：仅在获得单独明确授权后，将挖掘结果写入
    memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md
    ——包含搜索条件、候选池统计数据、单个达人档案、带有初步分流信号的分层候选名单。值得纳入名录的候选达人（已验证账号、联系渠道、受众数据）仅能通过向
    registry-events.py
    发送单独授权的
    operation: propose
    请求，以单行更新的形式写入
    memory/events/creators.ndjson
    ——只有
    creator-registry
    可在
    memory/creators/
    下写入标准记录。
  • 缓存推广：仅在获得单独明确授权后，将关键信息（顶级达人账号、已确认的细分领域/平台组合、竞品合作密集的达人）写入
    memory/hot-cache.md
    。
  • 完成条件：
    • 所需搜索条件全部齐全；若存在缺失，终止操作并返回
      NEEDS_INPUT
      ，明确列出缺失的条件，不得编造候选达人。
    • 生成的候选池至少包含符合要求数量的达人，且已通过粉丝量、互动率及品牌安全筛选。
    • 每位候选达人的档案包含数据指标、受众分析以及初步挖掘分流信号（非STAR适配性评分）。
    • 已整理出分层候选名单（必联系/优质/考虑），并标注后续行动指引。
  • 推荐后续技能：fit-scorer——使用加权标准对挖掘出的候选达人进行评分与排名。

Handoff Summary

交接摘要

Emit the standard shape from skill-contract.md §Handoff Summary Format.
按照skill-contract.md §Handoff Summary Format的标准格式输出。

Data Sources

数据源

This family has no live integrations required (Tier 1): the skill works with only the inputs the user provides. Ask the user for niche, platforms, follower band, engagement floor, location, and exclusions, then reason over what they supply plus any public handles they share.
Where a tool could sharpen results, use 
~~
connector placeholders:
  • ~~influencer database
    — bulk discovery, follower/engagement metrics, audience demographics.
  • ~~social platform analytics
    — native creator-marketplace data, trending sounds, related accounts.
  • ~~CRM
    — import the shortlist and dedupe against existing partners.
  • ~~audience overlap
    — estimate creator-audience vs. brand-audience match.
Keyless candidate-card metadata (oEmbed): YouTube (
https://www.youtube.com/oembed?url=<video-url>&format=json
), TikTok (
https://www.tiktok.com/oembed?url=<post-url>
), and X (
https://publish.twitter.com/oembed?url=<post-url>
) return a post's title, author name/handle, and thumbnail with no key — enough to auto-fill a candidate's profile row from pasted links instead of hand-copying. Metadata only: no follower or engagement metrics, so those stay 
~~influencer database
or manual export — except YouTube, below.
Measured YouTube metrics (free key): 
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" channel @handle
returns the real displayed subscriber count, total views, and video count, and 
youtube.py videos @handle --limit 10
adds per-video views/likes/comments — upgrading a YouTube candidate's profile row from Estimated to Measured. Free 
YOUTUBE_API_KEY
(10,000 units/day; one channel check ≈ 1–3 units). ToS boundary: vet a named shortlist, don't build a bulk creator database — quota extensions are refused for competitive harvesting. See scripts/connectors/README.md.
See CONNECTORS.md for the free/keyless recipe per category and the opt-in MCP layer. None are required — every step degrades to user-supplied inputs.
本技能无需实时集成（一级工具）：仅基于用户提供的输入运行。需向用户确认细分领域、平台、粉丝量区间、最低互动率、地区及排除条件，结合用户提供的信息及分享的公开账号进行分析。
如需工具优化结果，可使用
~~
连接器占位符：
  • ~~influencer database
    ——批量挖掘、粉丝/互动数据、受众人群特征。
  • ~~social platform analytics
    ——原生创作者市场数据、热门音效、相关账号。
  • ~~CRM
    ——导入候选名单并与现有合作伙伴去重。
  • ~~audience overlap
    ——估算创作者受众与品牌受众的匹配度。
无需密钥的候选卡片元数据（oEmbed）：YouTube（
https://www.youtube.com/oembed?url=<video-url>&format=json
）、TikTok（
https://www.tiktok.com/oembed?url=<post-url>
）和X（
https://publish.twitter.com/oembed?url=<post-url>
）无需密钥即可返回帖子标题、作者名称/账号、缩略图——足以通过粘贴的链接自动填充候选达人的档案行，无需手动复制。仅包含元数据：无粉丝量或互动率指标，因此这些数据仍需依赖
~~influencer database
或手动导出——YouTube除外，详情如下。
可测量的YouTube数据（免费密钥）：执行
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" channel @handle
可返回真实显示的订阅量、总播放量和视频数量，执行
youtube.py videos @handle --limit 10
可添加单个视频的播放量/点赞量/评论量——将YouTube候选达人的档案行从“估算”升级为**“实测”。免费
YOUTUBE_API_KEY
（每日10,000单位；单次频道查询≈1–3单位）。服务条款边界：仅针对指定候选名单**进行审核，不得构建批量创作者数据库——用于竞品挖掘的配额扩展申请将被拒绝。详情请见scripts/connectors/README.md。
各品类的免费/无密钥数据方案及可选MCP层请见CONNECTORS.md。所有工具均为可选——若无法使用，所有步骤可降级为仅基于用户提供的输入运行。

Instructions

操作步骤

Each step has a fill-in block in references/templates.md — copy the matching block. This skill does not compute a STAR Suitability score; any per-influencer score in step 4 is only a discovery-triage signal that fit-scorer replaces with a typed evidence read downstream.
  1. Define search criteria. Capture brand, goal, audience definition, budget/follower tier, platforms, engagement floor, location/language, exclusions, and the required/preferred parameter table. If any required criterion is missing, stop with 
    NEEDS_INPUT
    ; offer audience-mapper only when the user wants help defining the audience. Step 1 template.
  2. Conduct the search. Work hashtags, similar-accounts, competitor mentions, and platform-native discovery; log any tool queries used. Step 2 template.
  3. Initial screening. Filter the pool on follower range, engagement, recency, relevance, and brand safety; tally red flags (suspected fake followers, controversy, competitor exclusivity, inactivity). These are discovery signals, not verified STAR failures or vetoes; unsupported applicable evidence remains Unknown for downstream scoring. Per-platform reading cues: references/platform-vetting.md. Step 3 template.
  4. Build influencer profiles. For each qualified creator, fill the profile (basics, metrics, audience, content, partnership history, contact, preliminary discovery-triage signal). Do not emit a STAR Suitability score from partial coverage. For a deep single-creator read with a contact waterfall, use references/creator-dossier.md. Step 4 template.
  5. Compile the discovery report. Roll profiles into summary stats, by-platform and by-tier breakdowns, the three-tier shortlist, mix recommendation, and next steps. Step 5 template.
  6. Add insights. Note niche content trends, the competitive picture, and recommendations for future searches. Step 6 template.
Return the discovery report inline. Saving the report, caching the shortlist, and submitting each roster-worthy creator as 
operation: propose
are three separate operations and each requires exact authorization; without it, offer the eligible path and write nothing. After a vetted shortlist exists, hand it with dated evidence to fit-scorer. 
fit-scorer
records the S1-S10 evidence read; creator-content-auditor alone determines verified STAR vetoes and renders the gate verdict.
每个步骤在references/templates.md中都有填充模板——复制对应模板即可。本技能不计算STAR适配性评分；步骤4中的单个达人评分仅为挖掘分流信号，后续将由fit-scorer替换为基于证据的标准化评分。
  1. 定义搜索条件：记录品牌、目标、受众定义、预算/粉丝层级、平台、最低互动率、地区/语言、排除条件，以及必填/可选参数表。若缺失任何必填条件，终止操作并返回
    NEEDS_INPUT
    ；仅当用户需要协助定义受众时，推荐使用audience-mapper。步骤1模板。
  2. 执行搜索：通过话题标签、相似账号、竞品提及、平台原生挖掘功能进行搜索；记录使用的所有工具查询。步骤2模板。
  3. 初步筛查：根据粉丝量范围、互动率、活跃度、相关性及品牌安全筛选候选池；统计风险信号（疑似虚假粉丝、争议事件、竞品排他合作、账号 inactive）。这些仅为挖掘信号，并非已验证的STAR不合格或否决项；无支撑的相关证据将标记为“未知”，供下游评分使用。各平台审核指引：references/platform-vetting.md。步骤3模板。
  4. 构建达人档案：为每位合格的创作者填充档案（基础信息、数据指标、受众分析、内容特征、合作历史、联系方式、初步挖掘分流信号）。不得基于部分信息生成STAR适配性评分。如需深度分析单个创作者并获取联系渠道，使用references/creator-dossier.md。步骤4模板。
  5. 整理挖掘报告：将档案汇总为统计摘要、分平台及分层明细、三级候选名单、组合建议及后续步骤。步骤5模板。
  6. 添加洞察分析：记录细分领域内容趋势、竞品情况及未来搜索建议。步骤6模板。
直接返回挖掘报告内容。保存报告、缓存候选名单及提交合格达人至名录（
operation: propose
）为三个独立操作，每项均需明确授权；若无授权，仅说明可行路径，不执行任何写入操作。候选名单审核通过后，将带有日期的证据移交至fit-scorer。
fit-scorer
记录S1-S10证据评分；仅creator-content-auditor可判定已验证的STAR否决项并给出最终审核结果。

Compact Example

简洁示例

User: "Find 15 micro-influencers (10K-100K followers) in sustainable fashion for a new eco clothing brand."
Output: 43 candidates surfaced, 15 pass the declared discovery filters with preliminary triage signals above 18/25. Top candidate @sustainablestyle_sarah (47K IG + 23K TikTok, 5.2% ER, prior eco-brand partners) has a 24/25 discovery signal; shortlist tiered into 5 high-engagement leads, 7 mid-tier, 3 rising stars. The report is returned inline, then save, promotion, and registry-proposal permissions are offered separately. Full walkthrough in references/templates.md.
用户："为新环保服装品牌寻找15位可持续时尚领域的微型达人（粉丝量10K-100K）。"
输出：筛选出43位候选达人，其中15位通过预设挖掘筛选，初步分流信号得分高于18/25。顶级候选达人@sustainablestyle_sarah（Instagram 47K粉丝 + TikTok 23K粉丝，互动率5.2%，曾与多个环保品牌合作）的挖掘信号得分为24/25；候选名单分为5位高互动潜力达人、7位中阶达人、3位潜力新星。直接返回报告内容，随后分别询问保存报告、缓存候选名单及提交至名录的授权。完整流程示例请见references/templates.md。

Reference Materials

参考资料

  • references/templates.md — all step fill-in blocks (criteria, search, screening, profile, report, insights), the worked example, tips, and the "what/when" overview.
  • references/platform-vetting.md — per-platform creator playbooks (X/LinkedIn/TikTok/YouTube/Reddit) feeding screening and profiling in steps 3-4.
  • references/creator-dossier.md — structured per-creator dossier from public data, with a contact-discovery waterfall.
  • skill-contract.md — shared contract and Handoff Summary format.
  • state-model.md — memory tiers and save-path conventions.
  • CONNECTORS.md — free/keyless data recipes and opt-in MCP layer.
  • STAR benchmark at references/star-benchmark.md — scoring framework that fit-scorer applies downstream.
  • Siblings in the scout phase: fit-scorer, audience-mapper, trend-spotter.
  • references/templates.md——所有步骤的填充模板（条件、搜索、筛查、档案、报告、洞察）、示例、技巧及操作概览。
  • references/platform-vetting.md——各平台创作者审核指南（X/LinkedIn/TikTok/YouTube/Reddit），用于步骤3-4的筛查及档案构建。
  • references/creator-dossier.md——基于公开数据的结构化创作者档案，包含联系渠道挖掘流程。
  • skill-contract.md——通用协议及交接摘要格式。
  • state-model.md——内存层级及保存路径规范。
  • CONNECTORS.md——免费/无密钥数据方案及可选MCP层。
  • STAR基准：references/star-benchmark.md——fit-scorer后续使用的评分框架。
  • 同属挖掘阶段的相关技能：fit-scorer、audience-mapper、trend-spotter。

Next Best Skill

推荐后续技能

Primary: fit-scorer — score and rank the discovered candidates with weighted criteria before outreach.
Alternates (same influencer family):
  • competitor-tracker — when discovery surfaced competitor-saturated creators and you want to map the competitive field first.
  • audience-mapper — when the target audience is still fuzzy and criteria need sharpening before a re-search.
Termination: Maintain a visited-set. If a skill has already been invoked this session, stop and report chain-complete rather than re-invoking it. Max chain depth is 3 hops from the originating request; stop and summarize when reached.
首选：fit-scorer——在联系达人前，使用加权标准对挖掘出的候选达人进行评分与排名。
替代选项（同属达人工具家族）：
  • competitor-tracker——当挖掘出大量与竞品合作的达人时，可先分析竞品合作格局。
  • audience-mapper——当目标受众仍不清晰时，可先优化搜索条件再重新搜索。
终止规则：维护已访问技能集合。若当前会话中已调用过某技能，终止流程并报告流程完成，不得重复调用。最大流程深度为初始请求后的3跳；达到深度后终止并汇总结果。

Related Skills

相关技能

  • audience-mapper - Define who to reach
  • fit-scorer - Score and rank discovered influencers
  • competitor-tracker - Find competitor influencers
  • outreach-manager - Contact discovered influencers
  • audience-mapper - 定义目标受众
  • fit-scorer - 对挖掘出的达人进行评分与排名
  • competitor-tracker - 寻找竞品合作达人
  • outreach-manager - 联系挖掘出的达人