agentleFS
link7373link7373/agentic-pmo/.claude/skills/capture-feedback/SKILL.md

capture-feedback

Skill1 starsChanged 3 months agoMITView on GitHub

Capture and triage inbound signals — customer feedback, support themes, sales asks, stakeholder requests, ideas, bug trends — into the intake register, then route each to discovery, backlog, roadmap, or decline. Use whenever new input arrives or for a triage sweep. Dispatches the product-manager.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

/capture-feedback — The PMO's front door

When to use

Whenever inbound arrives (feedback, requests, ideas, support/bug themes), or for a regular triage sweep of open intake.

Dispatches

product-manager (lead/triage). Consult discovery-researcher for items needing validation; product-owner for items going straight to backlog.

Inputs

The raw signal(s), knowledge/intake.md, knowledge/product-context.md (goals/OKRs), knowledge/roadmap.md.

Steps

  1. Capture each signal into knowledge/intake.md: date, source, raw request, type.
  2. Classify type (problem / idea / request / bug / insight) and link the goal/OKR it relates to (if any).
  3. Look for themes — does this match a recurring pattern? Update the themes section if so.
  4. Triage decision per item:
    • Explore → /run-discovery (unvalidated problem worth investigating)
    • Backlog → /groom-backlog (clear, valuable, ready to shape)
    • Roadmap → /build-roadmap (strategic, larger than a story)
    • Now/quick → top of backlog (small, urgent, clear value)
    • Decline / Watch → record with a reason (so it doesn't keep re-entering)
  5. Route and record where each item went and its status.

Methods

knowledge/methods/discovery-and-validation.md (signal strength), knowledge/methods/prioritization-frameworks.md.

Output

Updated knowledge/intake.md; items routed to the right skill/agent. Log consequential decline/accept decisions to knowledge/decision-log.md. Follow standards/document-standards.md.


Created by Colin Beck<br> LinkedIn: https://www.linkedin.com/in/beckcolin/<br> GitHub: https://github.com/link7373

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.