autoplan
by garrytanautoplan is a workflow automation skill that runs a full review pipeline on an existing plan. It reads review skills from disk, applies decisions in sequence, and surfaces only the borderline issues for final approval. Use it when you want a repeatable autoplan skill for plan review, not a generic summary.
This skill scores 68/100, which means it is list-worthy but best presented with clear cautions. The repository shows a real, specific workflow for auto-running multiple review skills and surfacing final approval decisions, so directory users can tell what it does and when to invoke it. However, the install decision is weakened by missing supporting files and some placeholder/WIP signals, so users should expect a fairly self-contained but not fully polished skill package.
- Specific triggerability: the description names exact use cases like “auto review,” “autoplan,” and “run all reviews,” making it easier for an agent to invoke correctly.
- Operational workflow is substantial: the body is large and describes a full review pipeline that reads CEO, design, eng, and DX review skills, runs them sequentially, and escalates borderline decisions.
- Useful decision guidance: it includes decision principles, a final approval gate, and proactive suggestions for users with a plan file, which adds leverage beyond a generic prompt.
- Repository support is thin: there are no scripts, references, resources, rules, assets, or readme files, so users get limited external guidance for adoption.
- Placeholder/WIP markers appear in the skill, which suggests some sections may be incomplete or generated rather than fully production-hardened.
Overview of autoplan skill
autoplan is a workflow automation skill for running a full review gauntlet on a plan without forcing the user through every intermediate question. It is best for agents and power users who already have a plan file or draft and want the autoplan skill to apply multiple review lenses, make routine decisions automatically, and only escalate the borderline calls that need judgment.
What users care about most is speed with control: autoplan is designed to read the review skills from disk, execute them in sequence, and surface final approval issues instead of turning every step into a conversation. That makes it a strong fit when you need a repeatable review pipeline, not just a generic “analyze this” prompt.
What autoplan is for
Use autoplan when the job is to auto-review a plan, not to author one from scratch. The primary value is reducing back-and-forth on well-scoped review work while still preserving a final gate for taste decisions and disagreements.
Why this skill is different
Compared with an ordinary prompt, autoplan encodes the review path, the decision principles, and the trigger language for the workflow. That matters when you want consistent outcomes across repeated reviews and less guesswork about which skills or checks should run first.
Best-fit readers
This autoplan skill is a good fit for agents handling plan files, workflow automation setups, or review-heavy repositories where the user wants “run all reviews” behavior. It is a weaker fit if you only need a one-off opinion, a lightweight summary, or a fully manual review conversation.
How to Use autoplan skill
Install and load it correctly
For an install-oriented autoplan install flow, add the skill in the host environment that supports GitHub skills, then confirm the skill path is available before using it. The repository content points to the skill living under autoplan/, with SKILL.md as the main entry and SKILL.md.tmpl as the generation source.
Give autoplan the right input
autoplan works best when you provide a concrete plan file, a target review scope, and the expected review mode. Strong inputs look like this: “Review plan.md with autoplan and apply the full review pipeline,” or “Run autoplan on this design plan and surface only blocking issues.” Weak inputs like “look at this” do not tell the skill what artifact to process or how aggressive the review should be.
Read these files first
Start with SKILL.md for triggers, decision flow, and allowed tools. Then inspect SKILL.md.tmpl if you want to understand how the generated skill body is assembled. If you are adapting autoplan for your own workflow, those two files are more useful than skimming the entire repo because there are no helper scripts or extra reference folders to lean on.
Practical workflow tips
The most useful autoplan usage pattern is: provide the artifact, specify whether you want full automation or final approval only, and name any hard constraints up front. If the plan depends on a particular repo policy, safety rule, or team convention, include that in the prompt so autoplan does not have to infer it from context. This is especially important for Workflow Automation setups where missing guardrails can produce overconfident “approve” decisions.
autoplan skill FAQ
Is autoplan a replacement for manual review?
No. autoplan is meant to automate the routine parts of a review pipeline, not eliminate human judgment. It is strongest when the main work is sequencing checks and making repeatable decisions, with only edge cases left for approval.
When should I not use autoplan?
Do not use autoplan if you need a simple summary, a single-pass critique, or a highly creative response with no fixed review process. It is also a poor fit when the input is too vague to map to a review artifact, because the skill is built around running an explicit autoplan guide rather than improvising a new process.
Is autoplan beginner friendly?
Yes, if the user can name the file or plan to review. The skill reduces process friction, but beginners still benefit from giving a clear goal, such as “review this draft for correctness and scope,” instead of asking for a general “automatic review.”
How does autoplan compare with a normal prompt?
A normal prompt may work once, but autoplan gives you a repeatable review route with triggers, sequencing, and escalation behavior already defined. That makes it better for teams or agents that want consistent autoplan for Workflow Automation rather than ad hoc prompting.
How to Improve autoplan skill
Feed it a tighter review brief
The biggest quality gain comes from specifying the artifact, the review intent, and the acceptable output shape. For example, say whether you want “approve, revise, or reject,” whether the review should be strict or lenient, and whether the result should prioritize correctness, scope control, or delivery speed.
Reduce ambiguity before you invoke it
autoplan performs best when the plan file is already structured and the objective is explicit. If your draft mixes strategy, implementation, and open questions, split those apart first or call them out in the prompt; otherwise the skill may spend effort resolving uncertainty instead of reviewing the plan itself.
Watch for common failure modes
The main risks are over-automation, missing project-specific constraints, and treating borderline judgment as a yes/no check. If you see those in the first pass, refine the input with your standards, add the non-negotiable rules, and re-run autoplan on the corrected brief.
Iterate after the first pass
Use the first output to identify what the skill inferred incorrectly, then encode that missing context in the next run. This is the fastest way to improve autoplan skill results: keep the workflow, but make the review contract sharper each time.