gza-plan-improve

REVIEW · 65
Registry indexed

Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an implementation-ready plan

Verified installs0
Stars11
Version1.0.0
Quality57/100 · Promising
Trust65/100 · Sandbox only
Audit76/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add mhawthorne/gza --skill gza-plan-improve

Maintenance

fresh

Pushed today

Risk

Needs review

Low GitHub adoption signal

GitHub quality

11

57/100 Quality · 73/100 Trust

Coverage tags

ResearchResearch agentsautomationagent-skill

Review notes

Low GitHub adoption signal · Quality score needs review

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
57

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
65

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
76

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

11 GitHub stars

Repo activity

11 stars, 1 forks

Maintenance

Pushed today

License

MIT

Install

npx skills add mhawthorne/gza --skill gza-plan-improve

Install safety

standard package or runtime install path

Permission surface

shell or command execution

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 11 GitHub stars
  • Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Navigate pages

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add mhawthorne/gza --skill gza-plan-improve
Policy
review
Human review
yes

Trust and risk

Trust
65/100
Audit
76/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add mhawthorne/gza --skill gza-plan-improve

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • High-risk permission hints: Shell or command execution
  • Quality score needs review

Agent safety v2

48/100 · Avoid automatic install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • High-risk permission hints: Shell or command execution
  • Low GitHub adoption signal

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install mhawthorne-gza-plan-improve

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use gza-plan-improve in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mhawthorne-gza-plan-improve/install
Install command: npx skills add mhawthorne/gza --skill gza-plan-improve
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use gza-plan-improve for this task. Review https://www.openagentskill.com/api/skills/mhawthorne-gza-plan-improve/install, then install with: npx skills add mhawthorne/gza --skill gza-plan-improve

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

58/100

Browser automation

Platforms

Claude Code

Audit report

Needs review · 76/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Browser automation

Prototype with this skill first; keep a fallback candidate ready.

58
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Browser automation

Trust label

Prototype first

Install path

Command ready

Use when

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 57/100 quality profile
  • 4 OpenAgentSkill engagement events

review first

  • Low GitHub adoption signal

Implementation path

  1. 1Install it in a sandbox agent and run one Browser automation task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

65
OpenAgentSkill Trust Score

GitHub adoption

FIX

11 GitHub stars

Stars/forks activity

FIX

11 stars, 1 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 11 GitHub stars
  • Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

57
GitHub stars
11
Freshness
Today
Install ready
Yes
License
MIT
Review before install: Low GitHub adoption signal

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: gza-plan-improve description: Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an implementation-ready plan allowed-tools: Read, Bash(uv run gza show:*), Bash(uv run gza log:*), AskUserQuestion version: 1.0.0 public: true ---

# Gza Plan Improve

Refine a draft plan through a deliberate question loop. Use this when the user has a rough plan, an incomplete completed plan task, or a draft that needs sharper scope, acceptance criteria, sequencing, risks, and test strategy before implementation begins.

## Inputs

Accept one of these inputs:

- Preferred: a full prefixed plan task ID (for example, `gza-1234`) - Also supported: pasted draft plan text - Optional: extra constraints, related task IDs, or notes about what feels weak

If the user provides neither a full prefixed plan task ID nor draft plan text, ask for the current draft or plan task first.

Use the full prefixed task ID for all `gza` commands.

## Goal

Produce an improved plan, not just a score.

The skill should: - identify the highest-leverage gaps in the current draft - ask concise questions to close those gaps - confirm assumptions explicitly instead of guessing - rewrite the plan into a cleaner, more implementation-ready shape - call out any remaining blockers or open questions

This is different from `/gza-plan-review`: - `/gza-plan-review` decides `Go` / `No-go` - `/gza-plan-improve` actively helps the user strengthen the plan first

## Process

### Step 1: Gather the current plan and context

If the input is a full prefixed plan task ID, inspect it with:

```bash uv run gza show <TASK_ID> uv run gza log <TASK_ID> ```

Use that output to extract: - task type and status - original prompt - current plan/report content - nearby context from logs that explains uncertainty, blockers, or assumptions

If the task is not found or is not a `plan` task, stop and explain the mismatch.

If the input is draft text instead of a task ID, use the provided draft as the working plan.

### Step 2: Diagnose the weakest parts first

Evaluate the draft against these plan dimensions:

1. Problem framing - Is the user problem or objective specific? - Does the draft explain why the work matters?

2. Scope and boundaries - What is explicitly in scope? - What is explicitly out of scope? - Which files, modules, systems, or surfaces are likely affected?

3. Acceptance criteria - What observable outcomes define success? - Are edge cases and failure modes named? - Would an implementer know when the work is done?

4. Risks and unknowns - What could cause rework, delay, or the wrong design choice? - Which unknowns need decisions, investigation, or validation?

5. Dependencies and sequencing - Are prerequisites, approvals, related tasks, or external systems identified? - Is the execution order clear enough to avoid backtracking?

6. Test strategy - Which tests or verification modes are required? - Which regressions must be guarded against?

Rank the gaps and focus on the smallest set of questions that will most improve the plan.

### Step 3: Run a targeted question loop

Use AskUserQuestion to ask concise, high-value follow-up questions.

Rules for the question loop: - Ask only what materially improves the plan - Prefer 1 to 4 questions per round - Ask about the biggest uncertainty first - Confirm assumptions explicitly when the draft implies something but does not state it - Stop asking once the remaining gaps are minor or clearly flagged as open questions

Good question themes: - exact success criteria - scope boundaries and non-goals - risky edge cases - sequencing and dependency order - test expectations - operator-facing docs/help/config impact when relevant

### Step 4: Rewrite the plan

Produce a revised plan with clear headings and direct language. Prefer a structure like:

```text Plan: <short title>

Objective - <what problem is being solved>

Scope - In scope: <items> - Out of scope: <items>

Assumptions / Inputs - <assumptions confirmed with user>

Acceptance Criteria 1. <testable success condition> 2. <testable success condition>

Implementation Outline 1. <step> 2. <step> 3. <step>

Risks / Unknowns - <risk + mitigation or follow-up>

Dependencies - <task/system/approval + status>

Test Strategy - <unit/integration/e2e/manual verification as relevant>

Open Questions - <only unresolved items that genuinely remain> ```

Do not preserve vague wording from the original draft if it can be made concrete.

### Step 5: Close with readiness and next action

After presenting the improved plan, summarize:

- what materially changed - any blockers or unresolved questions that still matter - whether the plan now looks ready for `/gza-plan-review` or direct implementation follow-up

If the plan came from a task and is now strong enough, recommend:

```bash uv run gza show <TASK_ID> uv run gza log <TASK_ID> ```

and then `/gza-plan-review` for a final quality gate if needed.

If the plan is still too ambiguous after refinement, say so plainly and list the missing decisions.

## Important notes

- Keep the interaction collaborative and specific; avoid broad brainstorming unless the user asks for it. - Prefer rewriting the plan over merely criticizing it. - Do not invent technical constraints, dependencies, or acceptance criteria that were not supported by the draft or user answers. - If the user is really trying to create a new gza task rather than improve a plan draft, prefer `/gza-task-draft`. - If the user wants a final `Go` / `No-go` decision on a completed plan task, prefer `/gza-plan-review`.

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 21, 2026
Published
Aug 21, 2026

Decision snapshot

Fallback candidate

58
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

76
Needs review
Security
82/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for gza-plan-improve, ready for a manual X post.

Curator note
gza-plan-improve: Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an im...

11 stars

https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve?ref=x
Open X draft
Optional reply with install command
Listing + install path for gza-plan-improve:
https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve?ref=x

Install: npx skills add mhawthorne/gza --skill gza-plan-improve

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
mhawthorne
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

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Owner claim

Claim this skill listing

This Registry indexed listing is attributed to mhawthorne but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

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Author

M

mhawthorne

@mhawthorne

Platform fit

Health signals

GitHub stars
11
Quality score
31/100
Last GitHub push
Aug 21, 2026
Framework hints
Unknown
OpenAgentSkill views
4
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

65
  • GitHub adoption11 GitHub starsFIX
  • Stars/forks activity11 stars, 1 forks; issue activity unavailable in current metadataFIX
  • Recent maintenancePushed todayPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surfaceINFO