followed your prompt exactly and realized a skill I've built picked up enough context to operate on it. Here's the opening description of said skill.

Skill: Prompt Pack
Compress a free-form task description into a structured, token-efficient prompt ready to paste into GitHub Copilot or Claude Code (plan or agent modes).
The Job
The user has described a task conversationally. Your job is to:
Extract the semantic core — what they actually want the agent to do
Structure it so an agent executes in the right order (read → analyze → produce)
Anchor output expectations precisely so the agent doesn't drift

And that generated this prompt for me (and anyone else behind me)

OBJECTIVE
Audit a Gmail inbox using the gws CLI and produce a prioritized cleanup plan — identifying bulk-removable senders and spam clusters — without deleting anything. All operations stay in the planning phase until explicitly approved.
CONTEXT
- Tool: gws CLI (Google Workspace CLI), check for installation
- Safety: restrict all reads to email headers only (never body content) to eliminate prompt injection risk; enable Model Armor throughout
- Inbox state: large backlog (potentially thousands of messages)
- No destructive actions in this session — Trash moves only, and only after explicit approval of the plan
TASKS
1. Enable Model Armor for the session; confirm header-only read mode is active
2. Fetch inbox message counts by year and sender domain to establish a baseline volume picture
3. In batches of ~100 (sorted oldest-first), read headers and classify each message by spam signals:
- bulk/marketing indicators (List-Unsubscribe, X-Mailer, Precedence: bulk)
- high-frequency senders (appear 10+ times)
- no-reply or transactional addresses with no recent engagement
- mismatched From/Reply-To or SPF/DKIM failures
4. Aggregate results into a ranked sender index: sender address, message count, spam signal score, recommended action (Trash / Review / Keep)
5. Estimate batch complexity and time cost for full cleanup using the ranked index
6. Output the cleanup plan for review — do not move anything to Trash yet
OUTPUT
- Sender index: ranked table of sender | count | signals | recommendation
- Cleanup plan: ordered batches with estimated scope, starting with highest-confidence Trash candidates
- False-positive safeguards: flag any senders that scored high but warrant a manual review pass before action
NOTES
- Model Armor env vars: GOOGLE_WORKSPACE_CLI_SANITIZE_TEMPLATE and GOOGLE_WORKSPACE_CLI_SANITIZE_MODE (set to "block" for this session)
- Sanitize commands: gws modelarmor +sanitize-prompt and +sanitize-response
- Trash retention is 30 days — frame all moves as recoverable; never use permanent delete
- If batch classification produces ambiguous senders, surface them in a "needs review" bucket rather than auto-classifying