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codexkit-follow-up-draft-builder

Draft concise follow-up messages, nudges, reminders, and recap emails from open decisions, stale requests, or recent meetings. Use when the work is repetitive business writing with known context. Do not use for sensitive legal language, high-stakes negotiation, or board-level communication without review.

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  • SKILL.md2.7 KB
  • agents/openai.yaml178 B

SKILL.md(원문)

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Follow-Up Draft Builder

Purpose

Write short operational follow-ups that move work forward without overthinking.

When to use

  • someone needs a reminder, nudge, or recap
  • open decisions need a clean ask and due date
  • the task is routine communication, not persuasion strategy

When not to use

  • the message carries legal, HR, or regulatory risk
  • tone calibration matters more than speed

Inputs

  • source context or prior thread summary
  • target audience
  • goal of the message
  • preferred tone if known

Procedure

  1. Identify the ask, owner, and desired timing.
  2. Strip background detail down to only what the receiver needs.
  3. Draft the message with a clear subject or opening line.
  4. End with one explicit next step.
  5. Provide a shorter variant if the user likely needs chat or Slack format.

Output

  • follow-up message draft
  • optional short-form version

Definition of done

  • the request is clear in one read
  • the recipient knows what action is needed
  • the draft is brief enough to send with minor edits

Examples

  • "Write a reminder to finance for the missing close evidence."
  • "Draft a polite follow-up to the vendor on the contract intake questions."

Quality Criteria

  • Trigger conditions and input requirements are unambiguous
  • Each automated step produces a verifiable output
  • Error handling and fallback paths are defined
  • Manual override points are documented

Verification (4C)

CheckQuestion
CorrectnessDoes the workflow produce the expected output for the defined inputs?
CompletenessAre all trigger conditions, edge cases, and error paths handled?
Context-fitIs the automation appropriate for the frequency and criticality of this task?
ConsequenceIf this ran unattended and failed silently, what would the downstream impact be?

Edge Cases

  • Input format varies unexpectedly — Add a normalization step at entry. Alert the operator on format mismatches.
  • Downstream system is unavailable — Queue the output and retry with exponential backoff. Alert after N failures.
  • Partial execution completes — Ensure idempotency — re-running from the start produces the same result without duplication.

Changelog

  • v1.0.0 — Initial release

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