skills/agently-prompt-management/SKILL.md
Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
npx skillsauth add agentera/agently-skills agently-prompt-managementInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the core problem is how prompt state should be structured before one request or request family runs.
input(...), instruct(...), info(...), and output(...) over concatenated prompt strings${...} placeholders in prompt files and inject them through mappings at load time.request.output instead of rebuilding it ad hoc in Python.format(...) or string concatenation when prompt mappings already fitreferences/overview.mdtools
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
data-ai
Use when the user needs Agently TaskDAG / Dynamic Task, model-generated or app-submitted DAG planning, TaskDAG validation, DynamicTaskResolver handlers, TaskDAGExecutor execution, or the Agently.create_dynamic_task compatibility facade. TaskDAG is the DAG foundation capability; Dynamic Task is the convenience facade over it and uses TriggerFlow as the execution substrate.
tools
Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, Execution Environment lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.
development
Use when the user is shaping Agently request-side behavior: model setup, settings files, prompt management, structured output, response reuse, streaming consumption, session memory, embeddings, knowledge-base indexing, retrieval, or retrieval-backed answers within one request family.