uat-generate
Discover MCP tools and generate a phased UAT plan with agent-executable test specifications
What this skill does
# UAT Generate
Generate a phased User Acceptance Test plan by discovering tools from connected MCP servers and producing agent-executable test specifications.
## Usage
```bash
# Generate UAT plan from all connected MCP servers (default: --mode mcp)
/uat-generate
# Explicit MCP mode
/uat-generate --mode mcp
# Target specific server
/uat-generate --mode mcp --server gitea
# Quick smoke test plan
/uat-generate --execution-mode quick
# Full comprehensive plan
/uat-generate --execution-mode full
# Custom output path
/uat-generate --output .aiwg/testing/uat/my-plan.md
# Interactive — ask questions about scope and priorities
/uat-generate --interactive
```
## Parameters
### --mode (default: mcp)
Test mode. Currently only `mcp` is supported. Future modes: `api`, `ui`.
### --server (optional)
Target a specific MCP server by name. If omitted, discovers tools from all connected servers.
### --output (optional)
Output path for the generated plan. Default: `.aiwg/testing/uat/plan-{server}-{timestamp}.md`
### --execution-mode (default: standard)
Controls the breadth of generated tests:
| Mode | Description |
|------|-------------|
| `quick` | Preflight + 1 happy path per tool. Minimal, fast. |
| `standard` | Happy paths + key edge cases + negative tests. Balanced. |
| `full` | All paths including exhaustive negative tests and E2E chains. Comprehensive. |
### --interactive (optional)
Prompt for scope decisions before generating:
- Which tool categories to include/exclude
- Priority ordering of phases
- Whether to generate negative tests
- Custom test data values
## Workflow
### Step 1: Discover MCP Tools
Enumerate all tools from connected MCP servers:
```
Discovering MCP tools...
Server: gitea (mcp__gitea__*)
Found 78 tools
Server: filesystem (mcp__fs__*)
Found 12 tools
Total: 90 tools across 2 servers
```
For each tool, capture:
- Tool name and description
- Parameter schema (required/optional, types, defaults)
- Return type description
- Any documented constraints or side effects
### Step 2: Categorize Tools
Group tools into logical categories:
```
Categories:
Repository Management: 15 tools (create, get, list, update, delete, fork, ...)
Issue Tracking: 12 tools (create, get, list, edit, comment, close, ...)
User & Org: 8 tools (get_user, search_users, get_orgs, ...)
Actions/CI: 14 tools (list_runs, get_job, dispatch, ...)
Labels & Milestones: 9 tools (create_label, edit_label, ...)
Wiki: 5 tools (create, get, update, delete, list)
Release & Tags: 8 tools (create_release, list_tags, ...)
File Operations: 5 tools (get_file, create_file, update_file, ...)
Misc: 2 tools (get_version, get_user_info)
```
### Step 3: Design Phases
Apply the standard phase structure:
1. **Phase 0: Preflight** — Connectivity, auth, server version
2. **Phase 1: Seed Data** — Create test entities needed by later phases
3. **Phases 2-N: Per-Category** — One phase per tool category
4. **Phase N+1: E2E Chains** — Cross-category workflows
5. **Phase N+2: Cleanup** — Delete all seeded data
### Step 4: Generate Test Specs
For each tool in each phase, generate test cases following the `uat-test-case.md` template:
- **Happy path**: Standard usage with valid inputs
- **Edge cases**: Boundary values, empty strings, max lengths (standard and full modes)
- **Negative tests**: Missing required params, invalid types, unauthorized access (standard and full modes)
- **E2E chains**: Multi-tool workflows (full mode only)
### Step 5: Wire Variables
Connect phases via stored variables:
```markdown
Phase 1 (Seed): create_repo → Store: TEST_REPO_NAME
Phase 3 (Issues): create_issue(repo: ${TEST_REPO_NAME}) → Store: ISSUE_INDEX
Phase 4 (Comments): create_issue_comment(index: ${ISSUE_INDEX})
Phase N (Cleanup): delete_repo(name: ${TEST_REPO_NAME})
```
### Step 6: Output Plan
Write the complete UAT plan to the output path. Display summary:
```
UAT Plan Generated
Server: gitea
Tools: 78
Phases: 12
Test cases: 165
Negative tests: 32 (isolated)
Estimated duration: ~20 minutes
Output: .aiwg/testing/uat/plan-gitea-20260227.md
Review the plan, then execute with:
/uat-execute .aiwg/testing/uat/plan-gitea-20260227.md
```
## Plan Output Format
The generated plan follows the `uat-phase.md` template. See `templates/uat-phase.md` for the full format.
## Error Handling
### No MCP Servers Connected
```
No MCP servers detected.
To use UAT-MCP, you need at least one MCP server connection.
Check your MCP configuration and try again.
```
### Server Discovery Fails
```
Failed to discover tools from server: {name}
Error: {details}
Continuing with remaining servers...
```
### Zero Tools Found
```
No tools discovered from {server_name}.
The server is connected but exposes no tools.
This may indicate a configuration issue.
```
## References
- Agent: @$AIWG_ROOT/agentic/code/addons/uat-mcp/agents/uat-planner.md
- Template: @$AIWG_ROOT/agentic/code/addons/uat-mcp/templates/uat-phase.md
- Schema: @$AIWG_ROOT/agentic/code/addons/uat-mcp/schemas/uat-plan.yaml
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.