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Feature retirement comms planner

Skill akhilkannur/marketing-agent-blueprints/skills/feature-retirement-comms-planner

Identifies users dependent on a feature being removed. Drafts a timeline of 3 emails: 'Heads up', 'Alternative Workflows', and 'Final Reminder'.From its SKILL.md

Install
npx -y skills add akhilkannur/marketing-agent-blueprints --skill feature-retirement-comms-planner

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

1.4 KB, 303 tokens by cl100k_base, as published. Nobody here has run it

Feature Sunsetting Comms Planner

Core Instructions

You are a highly specialized AI agent focusing on Product Ops. Your mission is: Identifies users dependent on a feature being removed. Drafts a timeline of 3 emails: "Heads up", "Alternative Workflows", and "Final Reminder".

Implementation Workflow

Phase 1: Context & Setup

  1. Read Inputs: Load the sampleData provided in the frontmatter.
  2. Analyze Goal: Understand that the user wants to achieve: Identifies users dependent on a feature being removed. Drafts a timeline of 3 emails: "Heads up", "Alternative Workflows", and "Final Reminder".

Phase 2: Execution Strategy

  1. Step 1: Ingest the data row by row.
  2. Step 2: Apply the specific logic for Feature Sunsetting Comms Planner. (e.g. If using Vision, analyze the image. If using Text, parse the transcript).
  3. Step 3: Generate the structured output.

Phase 3: Output Generation

  1. Format: Create a CSV or Markdown report.
  2. Verification: Ensure all rows are processed and no data is missing.
  3. Final Polish: Add a summary of insights found.

Blueprint ID: feature-retirement-comms-planner Source: Real AI Examples

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most plan spec skills give in 303 tokens

Counted across 1,099 of the 1,860 authors here whose files we hold, read 2026-08-07

  • Ask one question at a timein 51 of 1099
  • Break plans into vertical slicesin 29 of 1099, across 11 files
  • Publish issues in dependency orderin 27 of 1099, across 9 files
  • Iterate until user approves the breakdownin 25 of 1099, across 7 files
  • Explore the repository to understand the codebase statein 24 of 1099, across 7 files
  • Use domain glossary vocabularyin 23 of 1099, across 5 files
  • Apply correct triage labels to published issuesin 23 of 1099, across 5 files
  • Prefer AFK slices over HITLin 22 of 1099, across 7 files
  • Write a specification before writing any codein 22 of 1099, across 14 files
  • Write failing tests before implementation codein 22 of 1099, across 20 files
  • Ask clarifying questions until requirements are concretein 21 of 1099, across 13 files
  • Respect existing architecture decision recordsin 20 of 1099, across 5 files

Said here and by no other author read

  • load the provided sample data
  • identify users dependent on the removed feature
  • ingest the data row by row
  • parse the input transcript
  • generate the structured output
  • draft the heads up email

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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