# 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. - Skill: `kortix-ai/feedback-clustering` (Agent Skill) - Install (CLI): `npx skillmds@latest add kortix-ai/feedback-clustering` - Raw SKILL.md: https://api.skillmd.com/api/skills/kortix-ai/feedback-clustering/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: kortix-ai (https://skillmd.com/u/kortix-ai) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/kortix-ai/feedback-clustering --- 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. - 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. ## 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. - **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.