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avad-autoplan

Auto-review pipeline — reads the full CEO, design, and eng review skills from disk and runs them sequentially with auto-decisions using 6 decision principles.

Skill by agwacom

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/agwacom-avad-autoplan

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Auto-review pipeline — reads the full CEO, design, and eng review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope) at a final approval gate. One command, fully reviewed plan out. Use when asked to "auto review", "autoplan", "run all reviews", "review this plan automatically", or "make the decisions for me". Proactively suggest when the user has a plan file and wants to run the full review gauntlet without answering 15-30 intermediate questions.

Use cases: design, data

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/avad-autoplan

What's inside

Skill name
avad-autoplan
Description
Auto-review pipeline — reads the full CEO, design, and eng review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope) at a final approval gate. One command, fully reviewed plan out. Use when asked to "auto review", "autoplan", "run all reviews", "review this plan automatically", or "make the decisions for me". Proactively suggest when the user has a plan file and wants to run the full review gauntlet without answering 15-30 intermediate questions.
License
MIT
Instructions
394 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • allowed-tools: must be a space-separated string

Files (2)

  • SKILL.md
  • SKILL.md.tmpl

SKILL.md (first lines)

<!-- AUTO-GENERATED from SKILL.md.tmpl — do not edit directly -->
<!-- Regenerate: bun run gen:skill-docs -->

# /avad-autoplan — Auto-Review Pipeline

One command. Rough plan in, fully reviewed plan out.

/avad-autoplan reads the full CEO, design, and eng review skill files from disk and follows
them at full depth — same rigor, same sections, same methodology as running each skill
manually. The only difference: intermediate AskUserQuestion calls are auto-decided using
the 6 principles below. Taste decisions (where reasonable people could disagree) are
surfaced at a final approval gate.

---

## The 6 Decision Principles

These rules auto-answer every intermediate question:

1. **Choose completeness** — Ship the whole thing. Pick the approach that covers more edge cases.
2. **Boil lakes** — Fix everything in the blast radius (files modified by this plan + direct importers). Auto-approve expansions that are in blast radius AND < 1 day CC effort (< 5 files, no new infra).
3. **Pragmatic** — If two options fix the same thing, pick the cleaner one. 5 seconds choosing, not 5 minutes.
4. **DRY** — Duplicates existing functionality? Reject. Reuse what exists.
5. **Explicit over clever** — 10-line obvious fix > 200-line abstraction. Pick what a new contributor reads in 30 seconds.
6. **Bias toward action** — Merge > review cycles > stale deliberation. Flag concerns but don't block.

**Conflict resolution (context-dependent tiebreakers):**
- **CEO phase:** P1 (completeness) + P2 (boil lakes) dominate.
- **Eng phase:** P5 (explicit) + P3 (pragmatic) dominate.
- **Design phase:** P5 (explicit) + P1 (completeness) dominate.

---

## Decision Classification

Every auto-decision is classified:

**Mechanical** — one clearly right answer. Auto-decide silently.
Examples: run evals (always yes), reduce scope on a complete plan (always no).

**Taste** — reasonable people could disagree. Auto-decide with recommendation, but surface at the final gate. Two natural sources:
1. **Close approaches** — top two are both viable with different tradeoffs.
2. **Borderline scope** — in blast radius but 3-5 files, or ambiguous radius.

---

## What "Auto-Decide" Means

Auto-decide replaces the USER'S judgment with the 6 principles. It does NOT replace
the ANALYSIS. Every section in the loaded skill files must still be executed at the
same depth as the interactive version. The only thing that changes is who answers the
AskUserQuestion: you do, using the 6 principles, instead of the user.

**You MUST still:**
- READ the actual code, diffs, and files each section references
- PRODUCE every output the section requires (diagrams, tables, registries, artifacts)
- IDENTIFY every issue the section is designed to catch
- DECIDE each issue using the 6 principles (instead of asking the user)
- LOG each decision in the audit trail
- WRITE all required artifacts to disk

**You MUST NOT:**
- Compress a review section into a one-liner table row
- Write "no issues found" without showing what you examined
- Skip a section because "it doesn't apply" without stating what you checked and why
- Produce a summary instead of the required output (e.g., "architecture looks good"
  instead of the ASCII dependency graph the section requires)

"No issues found" is a valid output for a section — but only after doing the analysis.
State what you examined and why nothing was flagged (1-2 sentences minimum).
"Skipped" is never valid for a non-skip-listed section.

---

## Phase 0: Intake + Restore Point

### Step 1: Capture restore point

Before doing anything, save the plan file's current state to an external file:

```bash
SLUG=$(git remote get-url origin 2>/dev/null | sed 's|.*[:/]\([^/]*/[^/]*\)\.git$|\1|;s|.*[:/]\([^/]*/[^/]*\)$|\1|' | tr '/' '-')
mkdir -p ~/.avadbot/projects/$SLUG
BRANCH=$(git rev-parse --abbrev-ref HEAD 2>/dev/null | tr '/' '-')
DATETIME=$(date +%Y%m%d-%H%M%S)
echo "RESTORE_PATH=$HOME/.avadbot/projects/$SLUG/${BRANCH}-autoplan-restore-${DATETIME}.md"
```

Write the plan file's full contents to the restore path with this header:
```
# /avad-autoplan Restore Point
Captured: [timestamp] | Branch: [branch] | Commit: [short hash]

## Re-run Instructions
1. Copy "Original Plan State" below back to your plan file
2. Invoke /avad-autoplan

## Original Plan State
[verbatim plan file contents]
```

Then prepend a one-line HTML comment to the plan file:
`<!-- /avad-autoplan restore point: [RESTORE_PATH] -->`

### Step 2: Read context

- Read CLAUDE.md, TODOS.md, git log -30, git diff against the base branch --stat
- Discover design docs: `ls -t ~/.avadbot/projects/$SLUG/*-design-*.md 2>/dev/null | head -1`
- Detect UI scope: grep the plan for view/rendering terms (component, screen, form,
  button, modal, layout, dashboard, sidebar, nav, dialog). Require 2+ matches. Exclude
  false positives ("page" alone, "UI" in acronyms).

### Step 3: Load skill files from disk

Read each file using the Read tool. Before reading each sibling skill file, verify
it exists. If a skill file is missing, skip that review phase and note it in the log.

**CEO review skill:**
1. Construct path: `${CLAUDE_SKILL_DIR}/../avad-plan-ceo-review/SKILL.md`
2. Read the file. If it does not exist, skip Phase 1 and log: "CEO review skill not found — skipped."

From agwacom/avadbot, MIT.

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Last commit (this folder)
Mar 24, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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avad-autoplan skill. Agent Mart. https://agentmart.dev/skills/agwacom-avad-autoplan (last updated 2026-10-24)