gza-plan-improve
Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an implementation-ready plan
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
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
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
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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.
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.
Suited tasks
- Browser automation workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Navigate pages
Suited agents
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-improveDo 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
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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-improveAgent 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 JSON
/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mhawthorne-gza-plan-improve/install
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.
Install handoff
/api/skills/mhawthorne-gza-plan-improve/install
LLM text format
/api/skills/mhawthorne-gza-plan-improve/install?format=text
Find alternatives
/api/skills/search?q=gza-plan-improve&limit=3
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-improveRegistry 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.
Manifest
/api/registry/manifest/mhawthorne-gza-plan-improve
LLM text
/api/registry/manifest/mhawthorne-gza-plan-improve?format=text
Install alias
/api/registry/install/mhawthorne-gza-plan-improve
Recommend
/api/registry/recommend?task=Use%20gza-plan-improve%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 76/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Browser automation
Prototype with this skill first; keep a fallback candidate ready.
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
- 1Install it in a sandbox agent and run one Browser automation task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 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.
GitHub adoption
FIX11 GitHub stars
Stars/forks activity
FIX11 stars, 1 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSMIT
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.
Workflow fit
Use this skill in these scenarios
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Verify behavior
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Workflow fit
Add it to a complete workflow
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
Frontend and UI
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
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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
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 82/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- 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
Scenario-led draft for gza-plan-improve, ready for a manual X post.
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
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
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- mhawthorne
- Source
- mhawthorne/gza
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner 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
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve/audit)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)Author
mhawthorne
@mhawthorne
Tags
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
- 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
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