跳到正文
cccskills
免费在 GitHub 公开

brand-mention-monitor

Monitor public news, social platforms, and forums for new brand mentions. Remove previously reported results, classify sentiment, and send a Slack digest with response drafts for negative or notable mentions, leaving public replies to people.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Daily brand-mention monitoring loop for {{brand_terms}}. Searches news, social platforms, and forums for new mentions, dedupes against the ledger of mentions already reported, classifies sentiment and notability, and posts a digest with suggested response drafts to {{slack_channel}} — never posts, replies, or comments anywhere.

集成工具SlackReddit

实测评分 未实测

查看安装方法

适用场景

  • Track new brand mentions each day
  • Identify negative or widely seen mentions
  • Prepare response drafts for human review
  • Run a manual sweep of current brand mentions

包含的文件(1)

  • SKILL.md5.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

<skill name="brand-mention-monitor"> <overview> Watch what's being said about the brand across the open web without turning into either silence or an alert firehose. A daily cron re-prompts a persistent session that searches news, social platforms, and forums for {{brand_terms}}, dedupes each candidate against the ledger of mentions already reported, classifies sentiment and notability, and posts one digest to {{slack_channel}} — a one-line rollup for routine mentions, a full callout with a suggested response draft for anything negative or notable.

Proactive and schedule-driven; read-only against the public web, with a single Slack channel as the only output. It never replies, comments, or publishes anywhere — a suggested response is always a draft for a human to send. </overview>

<when-to-load> - The daily cron fires the brand-monitor run. - A human asks for a manual sweep of current brand mentions. - The brand terms or watch scope need updating (a new alias, product name, or common misspelling starts showing up). </when-to-load> <workflow>

Step 0 — Orient and resume

cat memory/brand-monitor-log.md 2>/dev/null || echo "(no ledger yet — first run)"

Read every mention already reported before searching. If this is the first run ever, there is nothing to dedupe against — search, classify, log everything, and report normally.

Step 1 — Load the brand terms

{{brand_terms}} is the source of truth for what counts as a mention this run: brand name, aliases, product names, and common misspellings, one per line. Search for exactly these terms — no more, no less.

Step 2 — Search the web

Use the sandbox's built-in web search across news sites, social platforms (X/Twitter, LinkedIn, Reddit, etc.), and forums (Reddit threads, Hacker News, industry forums). Run one search per term in {{brand_terms}}, plus a news-specific pass. No login, no connector credential — public search only.

Step 3 — Fetch and normalize each candidate

Fetch the full text (or a substantial excerpt) of anything that looks like a genuine mention rather than an unrelated result. Capture per mention: URL, source name/platform, published date, author/handle if public, and the text that mentions the brand.

Step 4 — Dedupe against the ledger

Compute a stable fingerprint per mention (the URL, or a hash of URL plus the first few hundred characters). Compare against memory/brand-monitor-log.md. Anything already logged is skipped — this run only classifies and reports mentions that are new.

Step 5 — Classify sentiment and notability

For every new mention, call the sentiment: positive, neutral, or negative, with a one-line reason. Flag as notable anything that is:

  • Negative in sentiment.
  • High reach (large follower count, high upvotes/comments/shares).
  • From an influential source (press outlet, verified or large account).
  • Factually wrong about the brand in a way that could spread.

Everything else is routine — still logged, but reported as a one-line rollup, not a full callout.

Step 6 — Draft a suggested response for flagged mentions

For each notable or negative mention, draft exactly one suggested response: calm, on-brand, addressing the specific concern raised. Label it clearly — "Suggested response (draft — needs human approval before sending)." Never post it, reply with it, or publish it anywhere. It exists only inside the Slack digest for a person to read, edit, and send themselves if they choose to.

Step 7 — Compose and post the digest

One message to {{slack_channel}} per run:

  • A count of new mentions found and the sentiment breakdown.
  • For each notable/negative mention: source link, a short excerpt, the sentiment and why it was flagged, and the suggested response draft.
  • Routine new mentions: a one-line rollup (source + one-line gist), not a full callout each.
  • A quiet run (no new mentions): a brief note, not a wall of "nothing found."

Step 8 — Update the ledger

Append today's mentions to memory/brand-monitor-log.md — fingerprint, URL, source, sentiment, notable flag, and whether it was reported — plus a dated run-log line summarizing what was posted.

</workflow> <ledger-format> Lives at `memory/brand-monitor-log.md`. Per mention: fingerprint (URL or URL+content hash), source/platform, date first seen, sentiment classification, notable flag (y/n), and whether it was reported in the digest. Below that, dated **Run log** entries: how many new mentions were found, the sentiment breakdown, and which ones got a suggested response draft. </ledger-format> <guardrails> - **Monitor and alert only.** The agent never replies, comments, or posts on any external platform — not the source of a mention, not anywhere else — under any circumstance. - **Draft, not send.** A suggested response is always a labeled draft inside the Slack digest, for a human to review, edit, and send themselves. The agent never sends it on its own. - **Read-only against the web.** Public search and fetch only — no login, no auth, no write of any kind to any external site. - **One output surface.** {{slack_channel}} is the only place this agent writes. It has no access to internal systems and takes no action beyond posting the digest. - **Brand terms are code.** Changes to the watch scope go through the brand terms file and a reviewed change request, not an ad hoc edit mid-run. - **Secrets scoped.** No credential is needed for public search and fetch today. If a future source requires auth, that credential is encrypted in the secrets manager and injected at runtime — scoped to this agent's grant. </guardrails> </skill>

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Review Slack access requests against Okta role and team information, then prepare narrowly scoped GitHub or AWS IAM permissions. Apply changes only after an eligible person's approval and record the grant, reasoning, approver, and timestamp.

中文简介正在准备中。当前显示英语简介。

  • Reviewing GitHub repository access requests from Slack
  • Preparing AWS IAM permissions for a specific task
  • Routing sensitive access requests for additional review
kortix-ai/suna2万2026年10月24日 更新

Research companies and people before outreach or meetings. Combines business profiles, recent news, hiring signals, and key contacts into a sourced brief with talking points and suggested questions, adding relationship history when CRM data is connected.

中文简介正在准备中。当前显示英语简介。

  • Research a prospective customer before outreach
  • Prepare background information for meeting attendees
  • Compare a competitor's products and positioning
kortix-ai/suna2万2026年10月24日 更新

Review daily Google Ads and Meta Ads results for budget pacing, declining performance, and unusual activity. Get prioritized findings with supporting numbers and concrete recommendations for a marketing team to assess and carry out.

中文简介正在准备中。当前显示英语简介。

  • Checking whether campaign budgets are being spent on schedule
  • Investigating rising acquisition costs or falling ad returns
  • Finding ads and keywords that spend without converting
kortix-ai/suna2万2026年10月24日 更新

Automate website tasks such as navigating pages, filling forms, clicking buttons, taking screenshots, and extracting data. The skill also supports web app testing, with specialized guides for Electron desktop apps, Slack, and cloud browsers.

中文简介正在准备中。当前显示英语简介。

  • Automate form entry and page navigation
  • Capture screenshots and extract page data
  • Explore web apps for bugs
kortix-ai/suna2万2026年10月24日 更新

Process emailed vendor invoices by extracting details, comparing them with purchase orders and invoice history, and recording duplicates, overcharges, or missing orders. Summarize results in Slack for human review; payment approval and execution remain with people.

中文简介正在准备中。当前显示英语简介。

  • Checking incoming invoices against purchase orders
  • Finding duplicate invoices and likely resends
  • Reviewing unit prices and remaining order balances
kortix-ai/suna2万2026年10月24日 更新

billing

免费

Guide changes to Kortix billing code, admin controls, and pricing screens. Explain how plans, trials, enterprise contracts, and access exceptions should work, where pricing is defined, and which audited routes must handle account changes.

中文简介正在准备中。当前显示英语简介。

  • Update billing logic and its specification together
  • Review how trials and enterprise contracts are handled
  • Find the code that defines prices and allowances
kortix-ai/suna2万2026年10月24日 更新

查看 kortix-ai 的全部技能

报告此技能的问题