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
Chalk Labs
Need help deploying avad-autoplan?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
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.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: design, data
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
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.
More from agwacom/avadbot
- 0.406score
avad-help
agwacom
Skill hub: shows all available avadbot skills, helps pick the right one, and checks for updates.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-browse
agwacom
Fast headless browser for QA testing and site dogfooding.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-careful
agwacom
Safety guardrails for destructive commands.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-debug
agwacom
Systematic debugging with root cause investigation.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-design-consultation
agwacom
Design consultation: understands your product, researches the landscape, proposes a complete design system (aesthetic, typography, color, layout, spacing…
SkillDFreshness: DClaude Code +9 - 0.406score
avad-design-review
agwacom
Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-document-release
agwacom
Post-ship documentation update.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-freeze
agwacom
Restrict file edits to a specific directory for the session.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-guard
agwacom
Full safety mode: destructive command warnings + directory-scoped edits.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-plan-design-review
agwacom
Designer's eye plan review — interactive, like CEO and Eng review.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-unfreeze
agwacom
Clear the freeze boundary set by /avad-freeze, allowing edits to all directories again.
SkillDFreshness: DClaude Code +9 - 0.403score
avad-hello
agwacom
Simple greeting skill for testing plugin installation and updates.
SkillDFreshness: DClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Mar 24, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/agwacom/avadbot
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from agwacom
- 0.406score
avad-browse
agwacom
Fast headless browser for QA testing and site dogfooding.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-careful
agwacom
Safety guardrails for destructive commands.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-debug
agwacom
Systematic debugging with root cause investigation.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-design-consultation
agwacom
Design consultation: understands your product, researches the landscape, proposes a complete design system (aesthetic, typography, color, layout, spacing…
SkillDFreshness: DClaude Code +9 - 0.406score
avad-design-review
agwacom
Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them.
SkillDFreshness: DClaude Code +9 - 0.406score
avad-help
agwacom
Skill hub: shows all available avadbot skills, helps pick the right one, and checks for updates.
SkillDFreshness: DClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get avad-autoplan running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
avad-autoplan skill. Agent Mart. https://agentmart.dev/skills/agwacom-avad-autoplan (last updated 2026-10-24)