本文へ移動
cccskills
無料GitHub で公開

feedback-clustering

Turns feedback from Plain, public reviews, and a Slack channel into deduplicated, quantified themes, and reconciles each theme against one Linear issue in {{linear_team}} — never prioritizing, assigning, or closing.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

<skill name="feedback-clustering"> <overview> Feedback about the same underlying request shows up worded differently in Plain support threads, public reviews, and a Slack channel, and never gets counted together. This skill turns a run's raw feedback into themes — deduplicated, quantified, and backed by real quotes — and reconciles each theme against exactly one Linear issue in {{linear_team}}, so a request a hundred people made looks different from a one-off.

Fresh session each run. There is no ledger — the existing Linear issues in {{linear_team}} are the running state to reconcile against. </overview>

<when-to-load> - The scheduled cron fires the feedback sweep. - A human asks for the current feedback themes, or why a piece of feedback did or didn't land in a given theme. </when-to-load> <workflow>

Step 1 — Read the existing themes in Linear first

Before touching any source, pull the current state of {{linear_team}} so new feedback is reconciled, not duplicated:

  • List open issues in {{linear_team}}, with their title, description, and current quote/count body.
  • Note each issue's theme signature — the core request it represents — not just its title text.

Step 2 — Gather support threads from Plain (read-only)

Pull recent threads (since the last run's approximate window, or the last 24–48h if that's unknown). For each thread, extract:

  • The specific request or complaint, in the user's own words.
  • A candidate quote — the clearest single sentence expressing it.
  • Enough context (account, thread link) to trace it back later.

Ignore threads that are pure support (bug already fixed, question already answered) with no underlying feature request or recurring complaint.

Step 3 — Gather public reviews (read-only)

Fetch the current reviews from {{review_sources}} (G2, app-store listings, or whatever is configured). For each new review since the last visible one:

  • Extract the specific ask or complaint, not star-rating alone.
  • Take the reviewer's own phrase as the candidate quote.
  • Skip pure praise with no actionable request, and skip reviews already reflected in an existing Linear issue's quotes.

Step 4 — Gather messages from the feedback channel (read-only)

Read {{feedback_channel}} for messages where a team member is relaying something they heard from a user (not internal chatter). Treat these the same as a support thread: extract the request and a quote, with the relaying message as the source.

Step 5 — Cluster into themes

Group everything gathered in Steps 2–4 into themes. Two mentions are the same theme when they ask for the same underlying capability or fix, even if:

  • The wording is completely different ("can't bulk export" vs. "no way to download everything at once").
  • They come from different sources (a Plain thread and a G2 review can be the same theme).
  • One is more specific than the other (a general complaint and a precise technical ask can still be the same root request — cluster on intent, not surface detail).

Two mentions are different themes when they'd require different work to resolve, even if they sound superficially similar (e.g. "slow page load" on the dashboard vs. "slow page load" on export — different root cause, keep separate unless you can confirm otherwise).

Step 6 — Pick the representative quote and title

For each theme:

  • Quote — the clearest, most specific verbatim quote from any mention in the theme. Prefer a quote that names the concrete capability over a vaguer one.
  • Title — a short, action-oriented issue title describing the requested capability or fix, not the complaint's tone (e.g. "Bulk export for workspace data", not "Users are annoyed about exporting").

Step 7 — Reconcile against Linear

For each theme from Step 5:

  • Matches an existing issue (same theme signature from Step 1): add the new quote(s) to its quote list, increment its mention count, and note the new source(s). Don't create a duplicate.
  • No match — create a new issue in {{linear_team}} with the title, an opening set of quotes, a mention count, and the source(s) each quote came from.

Every issue body should always show: representative quotes (a small curated set, not every mention verbatim), a running mention count, and which sources (Plain / reviews / Slack) contributed.

Step 8 — Stop

Report the set of Linear issues created or updated this run. Do not set priority, assign an owner, or close any issue — even one that looks resolved or clearly a duplicate of another; leave that judgment to a human.

</workflow> <guardrails> - **Read-only sources.** Plain, the review sources, and {{feedback_channel}} are read-only. The only write in this skill is creating or updating a Linear issue. - **One issue per theme.** Never file a second issue for a theme that already has one — reconcile against Step 1's list first, every run. - **People decide priority.** The agent never sets priority, assigns an owner, or closes an issue. It quantifies and describes; humans weigh themes against the roadmap. - **Quotes over volume.** Keep a curated set of representative quotes per issue, not an ever-growing dump of every mention. - **Scoped secrets.** Plain, review-source, Slack, and Linear access is brokered server-side; no raw credential is ever pasted into chat. - **No memory between runs.** Each run is a fresh session; the current state of {{linear_team}} in Linear is the only carryover, not an internal ledger. </guardrails> </skill>

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Policy runbook for turning a Slack access request into a least-privilege, policy-checked GitHub or AWS IAM grant. Covers the role-to-grant mapping, extra-scrutiny cases, the approval handshake, and how an applied grant gets logged.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

Get a complete picture of any company or person before outreach. This skill always works with web search, and gets significantly better with enrichment and CRM data.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

Daily read-only ad-performance runbook — budget pacing, CPA/ROAS drift, underperforming ads and keywords, and anomaly detection across Google Ads and Meta Ads, plus how to rank and word optimization recommendations for {{alert_channel}}.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

Extraction, PO matching, duplicate detection, and overcharge tolerance for processing incoming vendor invoices from {{invoice_label}} against the POs and ledger in {{ap_ledger}}. Load this before extracting a single invoice so every one is checked to the same standard and only genuine exceptions reach a human, with no invoice ever scheduled for payment by the agent.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

kortix-ai/suna2万2026年10月12日 更新

kortix-ai のスキルをすべて見る

このスキルの問題を報告する