Agent skill
at-prompt-creator
Create agent-team orchestrated prompt bundles (orchestrator + sub-prompts) and store them through prompt-manager.
Install this agent skill to your Project
npx add-skill https://github.com/cruzanstx/daplug/tree/main/skills/at-prompt-creator
SKILL.md
at-prompt-creator
Create an orchestrated prompt set for complex work:
- 1 main orchestrator prompt
- N focused sub-prompts that can run independently
Use this skill when the user asks for /create-at-prompt.
Inputs
- Task description (same quality bar as
/create-prompt) - Optional destination folder under
./prompts/(nevercompleted/)
Resolve Helpers
PLUGIN_ROOT=$(jq -r '.plugins."daplug@cruzanstx"[0].installPath' ~/.claude/plugins/installed_plugins.json)
PROMPT_MANAGER="$PLUGIN_ROOT/skills/prompt-manager/scripts/manager.py"
CONFIG_READER="$PLUGIN_ROOT/skills/config-reader/scripts/config.py"
Use prompt-manager for all prompt writes/reads. Do not create files manually.
Workflow
1. Analyze Complexity and Decompose
Break the task into sub-prompts only when at least one of these is true:
- Work can be parallelized across different file areas/components.
- There are distinct specialist concerns (backend, frontend, tests, infra, docs).
- The task benefits from explicit planner/executor/validator separation.
Keep sub-prompts single-purpose and executable in isolation.
2. Draft Sub-Prompts First
Create sub-prompts first so orchestrator delegation can reference exact prompt IDs.
For each sub-task:
python3 "$PROMPT_MANAGER" create "<subtask-name>" --folder "$FOLDER" --content "$SUB_PROMPT_CONTENT" --json
Each sub-prompt must include:
- Clear
<objective> - Narrow
<scope> - Explicit
<output>files - Prompt-local
<verification>criteria
3. Create Orchestrator Prompt
After sub-prompts exist, create one orchestrator prompt that references them.
Use this template structure:
<objective>
Coordinate multi-agent execution for the parent task using existing sub-prompts.
</objective>
<orchestration>
<phase name="plan">
<!-- Claude native planning and risk check -->
- Confirm assumptions, dependencies, and execution strategy.
- Draft exact Task() orchestration with explicit escalation paths.
</phase>
<phase name="execute" strategy="parallel|sequential">
<!-- Delegation to sub-prompts via /run-prompt -->
<delegate prompt="228a" model="opencode" flags="--worktree" />
<delegate prompt="228b" model="codex" flags="--worktree --loop" />
</phase>
<phase name="validate">
<!-- Claude native integration + merge criteria -->
- Verify outputs are consistent and conflict-free.
- Resolve overlaps before final handoff.
</phase>
</orchestration>
<merge_criteria>
- All sub-prompts completed or explicitly triaged.
- No unresolved file conflicts.
- Validation checks pass.
</merge_criteria>
<output>
- Final integrated summary
- Recommended /run-at-prompt group syntax
</output>
The orchestrator body should include explicit Task() delegations so it is executable:
Task(
subagent_type: "at-monitor",
model: "haiku",
run_in_background: true,
prompt: "Launch /run-prompt 228a --model opencode --worktree and return Execution Report format."
)
Create orchestrator prompt:
python3 "$PROMPT_MANAGER" create "<task-name>-orchestrator" --folder "$FOLDER" --content "$ORCHESTRATOR_CONTENT" --json
4. Emit Execution Options (Decision Tree)
After prompt creation, present:
- Run orchestrator prompt directly:
/run-prompt <orchestrator-id> --model claude
- Run sub-prompts with explicit groups:
/run-at-prompt "220,221 -> 222" --model codex --worktree
- Run sub-prompts with auto dependency inference:
/run-at-prompt "220 221 222" --auto-deps --dry-run
Recommend --worktree for any parallel execution. Recommend --loop for high-risk prompts.
Orchestrator Quality Rules
- Plan phase stays Claude-native (research + dependency checks).
- Execute phase delegates via
/run-promptonly; no ambiguous free-form delegation. - Validate phase is explicit about pass/fail and merge gates.
- Keep model tiering explicit in delegation metadata where possible.
- Every delegate should be independently runnable.
Naming Conventions
- Use concise, kebab-case names.
- Suggested naming:
<task>-orchestrator<task>-backend<task>-frontend<task>-tests<task>-docs
Failure Handling
- If decomposition is unclear, ask 1-3 targeted clarifying questions.
- If a prompt creation call fails, stop and report exact stderr from prompt-manager.
- Never silently skip sub-prompt generation.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
at-prompt-runner
Orchestrate multi-prompt execution with phase groups, optional auto-deps, and model-tiered agent roles.
prompt-finder
Find and resolve prompt files in ./prompts/ directory. Use when user asks to find a prompt, list available prompts, locate prompt by number or name, or check what prompts exist.
cli-detector
Detect installed AI coding CLIs and local model providers; outputs a cached JSON inventory for routing (/detect-clis).
prompt-executor
Execute prompts from ./prompts/ directory with various AI models. Use when user asks to run a prompt, execute a task, delegate work to an AI model, run prompts in worktrees/tmux, or run prompts with verification loops.
ai-usage
Check AI CLI usage/quota for Claude Code, OpenAI Codex, Google Gemini CLI, and Z.AI. Use when user asks about remaining quota, usage limits, rate limits, or wants to check how much capacity is left.
config-reader
Read and manage daplug configuration from CLAUDE.md using <daplug_config> blocks, with legacy fallback and migration support.
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