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/migoxlab/webqa-agent/plannpx skills add MigoXLab/webqa-agent --skill plangit clone --depth 1 https://github.com/MigoXLab/webqa-agentWhat 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 | $0.00012 | $0.00922 |
| Opus 5 | $0.00006 | $0.00461 |
| Sonnet 5 | $0.00002 | $0.00184 |
| Haiku 4.5 | $0.00001 | $0.00092 |
Grade A, and why
plan 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 2d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Skill
Structure your approach before acting. Decompose broad objectives into sequenced steps with completion criteria at key milestones.
When to Use
- The task describes a multi-step workflow (login, search, checkout, etc.)
- The objective is broad ("test fundamental functionalities")
- You are unfamiliar with the page structure
- The task involves state that carries across steps (form data, cart items)
Skip this skill for single-action tasks ("click the login button").
Planning Phases
Phase 1: Understand the Objective
- What is the end goal? What does success look like?
- Which pages/features are involved? Ignore unrelated ones.
- What data flows through the workflow? (e.g., search query -> results -> detail page)
Phase 2: Sequence the Steps
Order steps as a continuous workflow, each following from the previous outcome.
- Aim for 4-8 steps for substantial tasks, fewer for simple ones. Each step is one reasoning unit — typically 1-3 tool calls that achieve a single sub-goal.
- Use semantic intent, not element selectors:
- Good:
"Click the 'Submit' button below the form" - Bad:
"Click element #btn_47"or"Click at coordinates (400, 300)"
- Good:
- For each step, state what and why:
- Good:
"Enter a search query to test the search flow" - Bad:
"Type 'hello world' in the search box"
- Good:
- Mark dependencies between steps: if step 4 depends on data from
step 2's outcome, say so explicitly.
- Example:
"Step 4 uses the product name found in step 2's results"
- Example:
Phase 3: Place Completion Checkpoints
Insert a checkpoint every 3-5 steps at key milestones.
For each checkpoint, define what to verify (the completion condition), not how to verify it:
- Good:
"Verify: search results page loaded with matching items" - Bad:
"take_snapshot and check for a results container"
The completion condition answers: what state proves this milestone was reached? The execution cycle (system prompt Step 4) handles the how.
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.
- 2d ago First seen · 121 lines · 12 tokens per session scan A 6a4d2fb5ab1b
plan is a skill published in the GitHub repository MigoXLab/webqa-agent (228 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 922 once invoked, about $0.0001 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
gtplanner
使用 GTPlanner 生成项目规划和技术架构文档。当用户需要: (1) 生成 PRD 或产品需求文档 (2) 规划项目架构 (3) 技术选型和推荐 (4) 创建详细设计文档 (5) 分析项目可行性 时触发此 skill。支持中英日西法五种语言。 触发词:"PRD"、"项目规划"、"架构设计"、"技术方案"、"设计文档"。.
document-query
Use when reading, extracting, summarizing, comparing, or answering questions over local or remote documents, code files, PDFs, Office files, HTML/text files, large text-heavy files, and fallback OCR for document images or scans when vision tools are unavailable or insufficient.
browser-qa
Drive a real headless browser against a locally-served web UI and capture screenshot evidence, reporting step-level PASS/FAIL/BLOCKED with extracted DOM text and console errors. Use when a phase on the docker QA image must exercise a rendered UI and attach screenshots — it complements the text-evidence verify/qa-test…
qa-test
Run an automated QA flow against a CLI or a locally-served app and report step-level pass/fail with evidence. Use when asked to QA-test a feature, exercise a flow end-to-end, or smoke-test what a PR changed.
use-mobile-device
Drive iOS and Android devices using the mobile-device-mcp tools (screenshot, tap, uitree, scroll, typetext, runcode, etc.). Use when MCP device-control tools are available and the user wants to interact with a mobile device.
ship-pr
Ship one pull request, monitor hosted CI and review feedback, follow PR policy, and request authorization before merging and cleaning up.