wrap-up

$npx mdskill add alirezarezvani/claude-skills/wrap-up

The explicit close-out. Confirm what's live, regenerate the shareable overview, and name the next 1–2 upgrades so the founder leaves with a clear roadmap. The `./my-agent/` folder keeps working after the session ends.

SKILL.md

.github/skills/wrap-upView on GitHub ↗
---
name: wrap-up
description: Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says "wrap up", "close this out", "what do I own now", "give me the summary", "recap the agent", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained ./my-agent/agent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.
license: MIT
metadata:
  version: 2.11.2
  author: Alireza Rezvani
  tags: [cma, wrap-up, closeout, inventory, overview, upgrades, next-directions]
  compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# Wrap-up — close it out

The explicit close-out. Confirm what's live, regenerate the shareable overview,
and name the next 1–2 upgrades so the founder leaves with a clear roadmap. The
`./my-agent/` folder keeps working after the session ends.

## Workflow

1. **Inventory what they own.**
   ```bash
   python3 scripts/primitives_inventory.py \
     --sheet ./my-agent/build-sheet.json --goal ./my-agent/goal.json
   ```
   Tables agent / environment / session / memory / outcome / deployment and the
   phases completed.
2. **Regenerate the overview page.**
   ```bash
   python3 scripts/overview_page.py \
     --sheet ./my-agent/build-sheet.json --out-dir ./my-agent \
     --status live --loop-shape cron-loop --last-verdict satisfied
   ```
   Self-contained `agent-overview.html` (inline CSS, theme-aware, no external
   assets) — shareable as-is.
3. **Suggest the next moves.**
   ```bash
   python3 scripts/upgrade_suggester.py --sheet ./my-agent/build-sheet.json --top 2
   ```
   Ranks recorded deferrals (v1 before v2, real-integration first) plus standing
   hardening (tighten networking, pin the agent version, nest an outcome).
4. **Finalize.** Ensure `NEXT-DIRECTIONS.md` is current (Phase-4 tool), then
   `goal_state.py advance` → `phase=done`.

## Hard rules

- **Recap what's actually live** — read it from the sheet + goal state, never
  assert primitives that weren't created.
- **The overview is single-file** — no external assets, so it shares cleanly.
- **Every next move names the exact mechanism.**

## Forcing-question library (recommend + cite)

1. "Confirm what's live vs still a plan?" *Recommend:* inventory from the sheet.
   *Cite:* this SKILL. 
2. "Which single upgrade has the highest payoff?" *Recommend:* the top-ranked v1
   deferral. *Cite:* upgrade_suggester ranking.
3. "Is the overview page current?" *Recommend:* regenerate after any change.
   *Cite:* this SKILL.

## Tools

- `scripts/primitives_inventory.py` — recap every owned primitive.
- `scripts/overview_page.py` — regenerate single-file agent-overview.html.
- `scripts/upgrade_suggester.py` — next 1–2 upgrades with mechanisms.

More from alirezarezvani/claude-skills

SkillDescription
a11y-auditAccessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
ab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
ad-creativeWhen the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
adversarial-reviewerAdversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
aeoAnswer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
agent-decision-receiptsMint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
agent-harnessTurn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workflow-builder), N-agent tournaments on one task (agenthub), single-file metric optimization (autoresearch-agent), or discovering published loop recipes (loop-library).
agent-launcher-orchestratorUse when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
agent-memoryUse when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.