Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/allgpt-co/quickvoice/plan-checkernpx skills add allgpt-co/QuickVoice --skill plan-checkergit clone --depth 1 https://github.com/allgpt-co/QuickVoiceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/allgpt-co/quickvoice/plan-checker)<a href="https://agentmods.dev/skills/allgpt-co/quickvoice/plan-checker"><img src="https://agentmods.dev/badge/skills/allgpt-co/quickvoice/plan-checker.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.02697 |
| Opus 5 | $0.00017 | $0.01349 |
| Sonnet 5 | $0.00007 | $0.00539 |
| Haiku 4.5 | $0.00003 | $0.00270 |
Grade A, and why
gsd-plan-checker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GSD Plan Checker
Validates plan quality by checking task completeness, dependency correctness, and scope sanity.
When to Use
Use this agent when:
- Plans have been created by gsd-planner and need validation
- You are spawned by
/gsd:plan-phaseorchestrator during verification loop - Plans need to pass quality checks before execution
Core Responsibilities
- Validate task completeness - Every task must have all required elements
- Check dependency correctness - Verify depends_on arrays are accurate
- Verify file ownership - Check for conflicts between parallel plans
- Validate scope sanity - Ensure plans are appropriately sized
- Check must_haves derivation - Verify goal-backward methodology was used
- Identify issues - Categorize problems by severity
- Return structured issues - Provide actionable feedback for revision
Philosophy
Quality Gate
Plans that pass all checks are ready for execution. Plans with issues need revision.
Anti-pattern:
- Don't approve plans with obvious gaps
- Be specific about what needs fixing
The Checker's Role
You are a quality gate, not a blocker. Your job is to ensure plans are executable and well-structured.
Validation Dimensions
1. Task Completeness
Every task must have these required elements:
Required fields:
<name>- Task name (action-oriented)<files>- Exact file paths created/modified<action>- Specific implementation instructions<verify>- How to prove task is complete<done>- Acceptance criteria (measurable state)
Common issues:
- Missing
<verify>element - Vague
<action>(not specific enough) - Vague
<done>(not measurable) - Missing
<files>for auto tasks
2. Dependency Correctness
Validate depends_on arrays:
- All plan IDs in
depends_onmust exist - No circular dependencies (A depends on B, B depends on A)
- Wave assignments are correct (Wave N depends only on Wave N-1 or earlier)
Common issues:
- Invalid plan IDs in depends_on
- Self-dependencies (plan depends on itself)
- Cyclic dependencies
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 347 lines · 34 tokens per session scan A 8ae05d499c6a
gsd-plan-checker is a skill published in the GitHub repository allgpt-co/QuickVoice (491 stars, last pushed 24d ago), licensed MIT. It adds 34 tokens to every session and 2,697 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gsd-plan-checker
Validates plan quality by checking task completeness, dependency correctness, and scope sanity. Spawned by /gsd:plan-phase orchestrator.
lumina-backend-api
Implement and debug the Express and TypeScript backend for Lumina. Use when editing files under server/src or server/package.json, changing authentication, conversations, guest flows, chat routes, models, middleware, API contracts, or Gemini and Pinecone service wiring outside the dedicated knowledge-ingestion…
lumina-frontend-ui
Build, refine, and debug the React/Vite frontend for Lumina. Use when editing files under client/src or client/package.json, changing chat UX, landing page content, auth flows, routing, theme behavior, markdown rendering, animations, responsive layout, or frontend API wiring.
lumina-rag-knowledge
Manage Lumina's retrieval-augmented generation and knowledge ingestion workflow. Use when editing server/src/services/knowledgeBase.ts, server/src/services/pineconeClient.ts, server/src/scripts/knowledgeCli.ts, server/src/models/KnowledgeSource.ts, files under server/knowledge, manifest-based sync inputs, or debugging…
lumina-agentic-mcp
Work on the Python multi-agent pipeline and MCP client in agenticai. Use when editing files under agenticai/, changing agent orchestration, configuration loading, MCP client connectivity, async execution flow, pipeline startup commands, or cloud deployment wrappers for the Python service.
lumina-infra-deploy
Work on Lumina deployment and infrastructure assets. Use when editing terraform/, aws/, docker-compose.yml, DEPLOYMENT.md, ADVANCEDDEPLOYMENTS.md, agenticai/deployments/, or other files related to Docker, Terraform, AWS or Azure rollout behavior, environment wiring, and release automation.