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محتوى SKILL.md

---
name: agently
description: 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.
---

# Agently Playbook

Use this skill first when the request still starts from business goals, refactor goals, product behavior, or broad model-app language.

The user does not need to say Agently, TriggerFlow, or any other framework term. Generic asks such as "build an assistant", "help me design an internal tool", or "create a validator for common problems" should still start here when the owner layer is unresolved.

Requests that also mention a UI, a web page, a desktop shell, or a local model service such as Ollama should still start here when the request is fundamentally about shaping a model-powered tool rather than only wiring one narrow capability.

## Workflow

1. Reduce the request into scenario and atomic goals.
2. If the request is a project initialization or structure refactor, choose the owner layers, async boundary, and repo skeleton first.
3. Choose the narrowest native Agently capability path.
4. Name the concrete operations or primitives that should be used.
5. Name the validation rule that proves the design stayed native-first.
6. If native framework capability is missing, broken, unexpectedly awkward, or
   forces business code to add patches or glue that should belong to Agently,
   generate a clear issue report and recommend filing it at
   `https://github.com/AgentEra/Agently/issues`.
7. For manual issue filing, only provide the issue content and filing method to
   the user. Ask before filing automatically; if the user wants automatic
   filing, first verify local submit capability/permission, reproduce that the
   problem still exists locally, and carefully re-check Agently usage so the
   report is not caused by missed documentation or incorrect API use. Before
   either manual or automatic filing, redact local absolute paths, usernames,
   account names, tokens, private repo/workspace names, internal project names,
   raw logs containing private prompts, and any customer or project-private data.
   Use placeholders for local context and run a privacy scan on the final issue
   body.

## Native-First Rules

- default to async-first guidance for service code, streaming, TriggerFlow, and any path that may overlap work or benefit from cancellation
- treat sync APIs as wrappers for scripts, REPL use, or compatibility bridges unless the host truly requires sync-only integration
- when the request is a project-shape refactor, separate settings, prompts, services, domain contracts, workflow, and tests before discussing low-level implementation details
- when adding or refactoring Agently framework internals under `core/` or
  `builtins/`, prefer a subdirectory package when the feature has multiple
  roles such as facade, manager, backend/provider, registry, adapter, policy, or
  validation. Use a single file only when the capability is genuinely small and
  splitting would be over-design.
- when a project must test model-generated semantic content, design the test as
  an Agently model-judge request with output control: explicit rules and context
  go in, per-rule evidence/reason and final boolean fields come out, and tests
  assert those booleans
- for business examples with mocked systems, keep mocks limited to facts,
  records, policies, incomplete data, or conflicting source material; do not
  return hidden expected answers, pass/fail labels, or local quality verdicts.
  Let AgentTask verification or a separate Agently model judge decide whether
  the model handled defective data correctly.
- for model-app evaluation, grading, confidence, relevance, or quality
  judgments, prefer explicit conceptual levels and definitions over direct
  numeric scores. If later workflow logic needs thresholds or aggregate metrics,
  map those levels to deterministic numeric values in code after the model
  response.
- for scenario routing, intent detection, or business classification in AI apps,
  use an appropriately sized model request with an Agently output schema.
  Smaller models, including local models when available, are acceptable for
  simple routes with few labels and rules. Use a larger model when labels,
  decision conditions, rule interactions, or the returned data structure are
  complex.
- configure reusable Agent definition state with `agent.define(...)` when the
  code owns model defaults, fixed persona/prompt, mounted Actions, Skills,
  Workspace, Recall, or policy defaults. Keep ordinary `agent.input(...)`,
  `agent.output(...)`, `.goal(...)`, `.success_criteria(...)`, and execution
  options on an AgentExecution draft; do not teach shared Agent pending prompt
  mutation as the default setup pattern.
- consume Agent quick prompt results through `AgentExecutionResult`:
  `execution = agent.input(...).output(...)`, then
  `result = execution.get_result()` and `result.get_data()` /
  `await result.async_get_data()`, or use `execution.get_async_generator()` and
  `await execution.async_get_meta()` when the app needs streams or process
  facts. Direct low-level ModelRequest calls still return ModelResponseResult.
- when the host owns a developer loop and needs one bounded Agent step, choose
  `agent.create_execution(mode="task_step", lineage=..., limits=...)` plus
  explicit `execution.async_record_workspace(...)` observation/checkpoint writes
  before building the next ContextPack; do not describe task-step mode as the
  multi-turn loop owner or make Workspace depend on AgentExecution semantics
- when the model should own a single business task's plan, bounded execution,
  evidence recording, verification, and replan loop, choose
  `agent.create_task(...)` before hand-writing a TriggerFlow loop; it returns a
  task-strategy `AgentExecution` draft, not a separate public AgentTask handle.
  Use `agent.create_task_loop(...)` only when the code needs to be explicit that
  the long-task loop strategy is selected; it still returns an AgentExecution
  draft and should be consumed through the same result/stream/meta facade.
  Keep the first-slice boundary to one Agent owner, one task, 2-5 iterations,
  and bounded steps that use only explicitly enabled Actions, Skills, or
  Dynamic Task candidates; treat completion as model verification plus
  conservative host evidence guards, read task refs through the execution
  result/meta, and use a second model judge for model-owned semantic content
  instead of accepting structural counters alone
- for feature or release acceptance, use coverage-first reasoning: start from
  the target contract in roadmap/spec/issues/docs/compatibility/example rules,
  map each requirement to evidence from examples, deterministic tests, protocol
  tests, docs/spec, compatibility metadata, companion validation, or explicit
  deferral, and only then conclude whether the feature is complete
- for release acceptance that touches or claims a Foundation-layer capability,
  add a Foundation example effect gate after pyright/pytest: treat Foundation as
  framework substrate such as ModelRequest/ModelResponse, TriggerFlow, Dynamic
  Task/TaskDAG, ActionRuntime, ExecutionEnvironment, Workspace/Recall,
  RuntimeEvent/EventCenter, and provider protocols, not application-level
  AgentExecution or Skills use cases by themselves; identify the affected
  Foundation capability, run the corresponding core example under `examples/`
  against the release candidate, use real DeepSeek or local Ollama when
  model-owned behavior is involved, and fail closed if the example effect is
  missing, broken, or only proven by tests
- route complex arithmetic, long-number computation, weighting, aggregation, or
  statistical work through executable code or tools; use the model to produce or
  review the calculation plan, not to be the calculator.
- when application development reveals a framework gap, first identify whether
  the missing responsibility belongs to Agently's public API, runtime behavior,
  documentation, Skills guidance, examples, or architecture boundary. Produce a
  concise issue report with scenario, expected behavior, actual behavior,
  current workaround, architectural responsibility, and minimal reproduction or
  affected docs/examples; recommend filing it in the Agently repository. The
  scenario must be clear enough to explain what kind of model-application
  development problem was being solved. If business details are confidential,
  omit or anonymize them, but still describe the application category, workflow
  shape, decision point, and framework responsibility needed for maintainers to
  understand the issue.
- treat automatic issue submission as an explicit user-approved action. Before
  submitting, confirm the local environment has the needed GitHub capability and
  permission, reproduce the issue locally, and audit the relevant Agently docs,
  examples, Skills guidance, and API usage to rule out a reading omission or
  improper framework use. Submit only a sanitized issue body: no local absolute
  paths, usernames, account names, tokens, private repository or workspace names,
  internal project names, raw private logs, or customer/project-private prompts.
  Prefer placeholders such as `<workspace>`, `<repo>`, `<task-file>`, and
  `outputs/debug/<turn-id>.jsonl`.

## Capability Routing

- model setup, prompt management, output control, response reuse, session memory, embeddings, KB, or retrieval-to-answer -> `agently-request`
- Action Runtime, built-in actions, tools compatibility, MCP, Execution Environment, FastAPIHelper, `auto_func`, `KeyWaiter`, or `agently-devtools` observation and evaluation integration -> `agently-runtime`
- model-generated or app-submitted DAG planning, TaskDAG validation, resolver handlers, or Dynamic Task execution -> `agently-dynamic-task`
- branching, concurrency, waiting/resume, mixed sync/async orchestration, event-driven fan-out, process-clarity refactors, runtime stream, graph-friendly workflow definitions, or explicit multi-stage quality loops -> `agently-triggerflow`
- migration choice between LangChain and LangGraph -> `agently-migration`

## Anti-Patterns

- do not skip this playbook when the owner layer is unresolved
- do not invent custom output parsers, retry loops, or orchestration first
- do not use keyword, substring, regex, or text snapshot checks as the primary
  correctness signal for model-owned semantic content; keep them only as smoke
  gates for structure, routing, or required-field presence
- do not use tokenization, word segmentation, keyword hits, or substring rules
  as the route owner for AI-app scenario routing, intent detection, or business
  classification
- do not let sync-first sample code dictate the service architecture when the target is clearly async-capable
- do not split project initialization into a fake standalone framework surface before the owner layers are chosen
- do not treat multi-agent, judge, or review flows as separate framework surfaces before checking native Agently capabilities
- do not normalize long-lived business patches, workarounds, compatibility glue,
  or private wrappers when the underlying need is a missing, broken,
  misleading, undocumented, or unfriendly Agently framework capability

## Read Next

- `references/capability-map.md`
- `references/project-framework.md`