AgentEra/Agently-Skills

Agently

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. For non-trivial apps, recommend optional local DevTools observation or evaluation when it will help the developer inspect runtime behavior, logs, traces, playground runs, or repeated scenario evaluations. Keep DevTools as optional tooling and route details through agently-runtime.
7. 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.
8. 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 a development script, service module, or test needs semantic judgment over model-owned behavior, use Agently model requests with explicit output schemas. Development-time intent recognition, scenario matching, business classification, output quality checks, grading, and review decisions should be model-owned unless the check is only a deterministic smoke gate for structure or required-field presence.
• 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, ContextBuilder profile, or policy defaults. Keep ordinary agent.input(...), agent.output(...), .goal(goal_or_goals, success_criteria=None) / .goals(...) as the same goal-pursuit entrypoint, and execution options on an AgentExecution draft; do not teach shared Agent pending prompt mutation as the default setup pattern.
• use agent.effort("low" | "medium" | "high") for ordinary strategy depth. When the app needs explicit strategy posture, keep the same method and pass sections such as budget, planning, execution, verification, replan, and progress; treat budget values as soft planning, reflection, repair, and evidence-depth hints rather than silent hard limits. Use explicit limits={...} or task options when the host needs hard resource controls. Framework defaults should not impose model-request, iteration, TaskBoard tick, Action round, node-count, or tool-call quotas; no-progress and idle timeouts are liveness guards for stuck executions, not strategy evidence. Do not introduce raw iteration-count builders or treat effort as permission, data visibility, resource gating, or completion acceptance. In AgentTask, effort also controls reflection density: low means final reflection plus only planner-marked important process nodes, medium means each major node or TaskBoard card/tick, and high means every framework-observable bounded step, Action/ACP call, TaskBoard card, and final reflection. Reflection is evidence for replan/verifier input, not completion evidence by itself.
• in Goal Pursuit / AgentTask examples, caller facts and the requested structured output contract may live on the same AgentExecution draft through .input(...) and .output(...); AgentTask treats that execution prompt snapshot as task context during planning, bounded step execution, and verification. Do not duplicate those facts into framework hardcode only to make the task loop see them.
• for every intermediate process with a strong structured contract, use Agently .output(..., format=...) on the owning request/execution. Choose the format that fits the payload (json, hybrid, flat_markdown, xml_field, or yaml_literal); if a declared non-JSON format fails, Agently may recover through JSON parsing, but only dict-shaped parsed payloads satisfy structured control or final task output contracts. AgentTask internals may add short process fields only where the framework has a concrete consumer: intent or decision-basis fields before route/plan/control decisions, and compact self-check, summary, verification, repair, or progress-message fields after main result fields. These are bounded process_summary facts for next-step clarity and observation, not raw chain-of-thought, not EvidenceEnvelope evidence, not completion evidence, and not a public runtime mode.
• for long or prose-heavy deliverables whose main value is the natural-language body, do not force the body through .output() only to carry text. Let the body generate as natural text, then use a compact structured judge/readback contract for status, evidence, quality, and artifact refs. For trusted file deliverables, use Workspace artifact write/readback plus a compact manifest. Do not add .output() solely to trigger instant fields for the body stream; plain public delta remains a valid body source when the consumer handles replay boundaries. For AgentTask-backed AgentExecution, public delta may also project framework-owned progress, action observation, Flat plan/action summaries, TaskBoard status tables, phase, retry, and terminal-result facts as short paragraphs separated by blank lines, while instant remains the structured stream for UI state and diagnostics. Flat projections stay linear: plan completion can state the previous completed action and current action plan, and terminal output can summarize what was done and the result. TaskBoard status tables are display-only projections from structured AgentTask events; the first TaskBoard projection may render a table and later ticks may render card-state changes instead of reprinting the whole table. These projections do not own completion or quality judgment. Do not add a default parallel narrator request for process prose; use bounded process fields such as progress_message, short_summary, verification_summary, and final_response from the existing planner, verifier, card, or finalizer request, then consume them through instant / synthetic $delta when the UI needs richer structure. Internal artifact writers should consume AgentExecution stream facts: natural body text comes from raw delta items, and retry boundaries come from $status when the provider reports it. If the public "<$retry>...</$retry>" delta replay marker reaches the artifact consumer, treat that exact marker as a retry control event; never write, clean into, or transport it as artifact content. If a complete Markdown artifact body appears inside structured evidence, treat it as a deliverable body only when the evidence item is explicitly labeled as artifact/body/deliverable/Markdown or tied to the manifest path; ordinary source content and source excerpts remain evidence snippets. After trusted Workspace write/readback succeeds, let terminal verification judge any stale artifact-write remaining_work instead of planning another write-only step. For long trusted Workspace artifacts, artifact delivery should record workspace_artifact.acceptance_locator ledger items after real Workspace write/readback. Locators may use artifact-manifest sections, TaskBoard card criteria, and optional model-returned acceptance_points intent, but line ranges, offsets, headings, and fingerprints must come from the actual file. Verifier-visible evidence may include bounded workspace_artifact.targeted_readback ledger items read from those locators, with declared output-contract sections and generic anchors only as fallback; treat locators as readback pointers and targeted readbacks as scoped evidence snippets, not completion judgments. TaskBoard finalization should keep file-backed deliverable bodies in Workspace and return only a concise summary or path/ref pointer as final_result, not a second copy of the file body. AgentTask terminal results should carry a user-facing final_response for accepted, degraded, partial, and blocked outcomes: Flat terminal verification may return it in the existing verifier request, TaskBoard finalization may return it in the existing finalizer request, and framework fallback should be deterministic from structured status, artifact refs, final_result pointers, and unmet criteria without starting a separate narrator request. Accepted degraded deliveries use artifact_status="degraded" with disclosed evidence limits, while useful but unaccepted artifacts remain artifact_status="partial" and should explain unmet requirements instead of being reported as completed. get_text() / async_get_text() may prefer final_response for task-strategy result dicts; get_data() / async_get_data() return the business final_result view when present, while get_full_data() / async_get_full_data() return the complete route/task envelope. TaskBoard terminal payloads may include bounded taskboard.completion_notes for card summaries, known gaps, verifier notes, and acceptance progress; use them to disclose final-response limitations, but treat them as projection-only process context, not EvidenceEnvelope evidence or completion proof. For model-produced verifier/finalizer content, prose fields such as status, reason, progress_message, and final_response are display context only; semantic completion, repairability, and acceptance state must come from structured output fields such as is_complete, requires_block, and criterion_checks[].satisfied plus host guards, never from tokenization, keyword, substring, regex, or status-text matching over model prose. TaskBoard planning card ids are optional model hints. The framework owns canonical card ids, deduplication, and dependency remapping; ambiguous id hints should fail closed rather than being guessed. Intermediate downloads, webpage snapshots, generated code, search notes, memory-like task notes, and large extracted text may also be persisted as Workspace/Action refs and opened later through bounded readback; these refs are execution evidence, not proof that the final deliverable exists. A discovered URL, path, download, or snapshot ref is also not evidence that its content has been read; treat it as ref_only until a bounded readback/content preview is available. Explicit content, excerpt, or snippet fields are bounded previews only for the visible excerpt, not proof that the whole file was read. When a TaskBoard control card needs a new concrete URL, path, or ref materialized before continuing, return structured target_refs with next_board_action=readback; do not rely on URLs hidden inside gaps prose as executable targets. Intermediate TaskBoard artifacts should stay on working/evidence paths; framework-marked final repair or continuation cards may write the required final deliverable path when that path is part of the task output contract. If a TaskBoard control card returns next_board_action=patch with a Workspace text patch proposal, AgentTask should materialize the patch into the bound Workspace file and expose the resulting readback refs; the verifier still owns completion judgment. For Flat repository/file tasks, clone or list manifest paths are ref_only until a file read, artifact readback, or bounded content preview is visible; use them as retrieval targets, not source-content evidence. TaskBoard final verification receives board-level source refs with preserved content_state boundaries, so final synthesis must not upgrade discovered paths into source-content evidence without bounded preview/readback. TaskBoard checkpoints may include a bounded acceptance-index projection and handoff projection for long-running resume and inspection. Treat those projections as orientation only: they summarize criteria/card status, evidence refs, artifact refs, preflight facts, and explicit task-scoped dirty or unresolved state facts, but they are not EvidenceEnvelope evidence and do not accept the task. The acceptance index may also carry dirty/cache state, verdict fingerprints, scoped evidence refs, and progress counters so TaskBoard can avoid re-verifying unchanged green criteria; dirty items, required deliverable guards, and explicit blocking facts still go through verifier or host checks. Preflight requirements must come from mounted Actions, ExecutionResources, or existing Workspace refs; do not assume universal git, browser, shell, or startup-script checks. TaskBoard scheduling defaults to event-driven frontier mode: completed cards can immediately unlock ready successors, while fan-in cards still wait for all declared dependencies. Use taskboard_scheduler="batch" only for historical tick-batch diagnostics or regression comparison. AgentTask grounding uses the canonical EvidenceEnvelope.evidence_items ledger. Prefer visible cite_as handles or canonical ids in evidence_use; path, URL, record, artifact, and action/ref aliases are producer-declared structural compatibility affordances that host guards canonicalize only when unambiguous. Guards must not rely on business-specific action-name rules. Compatibility views such as scoped_retrieval_results and TaskBoard source_refs are projections. Treat status=failed|empty as unavailable/missing-data evidence only, never as support for positive facts. Treat body_state=ref_only as discovery/ref- pointer evidence only. When structured output supports it, return evidence_use bindings with claim, evidence_ids, and support_type. File-backed task outputs may also return optional acceptance_points with a criterion, expected heading or exact anchor, and supporting evidence ids so the framework can build locator evidence after Workspace readback; do not invent line numbers or byte offsets.
• AgentTask work units receive an internal task context contract with intermediate-resource ref/readback policy and prompt-safe runtime metadata. Runtime records may keep compact current_time diagnostics, but default model-hot prompts omit concrete runtime timestamps and only expose availability metadata. For current, latest, recent, or as-of tasks, require the caller or source evidence to provide the business date when it matters. This contract is model-decision context, not a model-call, tool-call, node-count, iteration, or wall-clock cap, and runtime time must not be used as a business/source fact by itself.
• AgentTask observation projects normalized agent_task.action.started, agent_task.action.completed, and agent_task.action.failed stream events from Action records. Treat them as factual observability for UI, DevTools, and experiment logs; recovered success or partial_success Action records project as completed observations, while failed observations are reserved for failed, blocked, timed-out, or unrecovered error records. Do not use them as a local quality, relevance, route, or completion judgment.
• treat execution.step_plan as compatibility guidance only. AgentTask no longer uses TaskDAG / DynamicTask as an internal bounded-step strategy; legacy dynamic_task / execution_dag step proposals and effort(..., execution={"step_plan": "dag"}) degrade to one direct bounded AgentExecution step with diagnostics. Use TaskDAG / DynamicTask separately when the application or visual automation surface owns the submitted graph.
• treat AgentExecution.strategy("auto"|"direct"|"flat"|"taskboard") as the top-level route/execution selector. direct forces the ordinary model_request route with ActionLoop and does not create AgentTask; do not use .effort("direct") for route control. auto keeps ordinary prompt/action runs direct unless structural task signals such as goals, success criteria, task options, or Skill selectors enter AgentTask. Once AgentTask is selected, task execution="auto" uses one AgentTask-owned task-shape model request that allows free natural language analysis and then returns a thin structured execution_hint; do not route with keywords, regex, or local scorecards, and do not treat the hint as completion evidence. Use execution="flat" / .strategy("flat") to force the linear loop and execution="taskboard" / .strategy("taskboard") only when the host explicitly wants TaskBoard. Nested AgentExecution instances inherit the parent strategy context unless the child explicitly overrides it.
• treat Blocks as the internal lowering bridge from AgentTask ExecutionPlan / PlanBlock instances and validated TaskDAG nodes to TriggerFlow-backed ExecutionBlockGraph, not as a public task lifecycle. PlanBlock selection is evidence of need, not permission; ExecutionBlocks cannot accept task completion. Blocks registries fail closed on unknown block kinds, invalid runtime bindings, invalid signal contracts, denied capabilities, or pending capabilities without an approval_wait. skill_activation loads selected Skill guidance/resources and records skill-context evidence, while side-effect evidence must come from downstream ActionRuntime, Workspace, approval, or other concrete execution blocks.
• 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 facade methods such as execution.get_prompt_text(), execution.get_data_object(), execution.get_key_result(...), execution.wait_keys(...), execution.get_async_generator(type="specific"), execution.streaming_print(), and await execution.async_get_meta() when the app needs prompt inspection, object/key readers, streams, or process facts. For task-strategy route internals such as status, artifact_status, taskboard, or diagnostics, use result.get_full_data() / await result.async_get_full_data(). Direct low-level ModelRequest calls return ModelRequestResult; do not use the retired ModelResponseResult name. Ordinary agent.input(...).start() expressions create a fresh one-run execution each time and remain valid in loops. Explicit completed AgentExecution objects are immutable run records; do not reconfigure or rerun the same object, and create a new execution for the next request.
• when the host owns a developer loop and needs one bounded Agent step, choose agent.create_execution(lineage=..., limits=...) plus explicit execution.async_record_workspace(...) observation/checkpoint writes before building the next ContextPackage; do not introduce task-step mode as a public category 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 or Skills; 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
• when an application needs to add optional operator context while a task-strategy AgentExecution is already running, use await execution.async_add_guidance(...) or execution.add_guidance(...). Treat guidance as non-blocking context: AgentTask records it to Workspace workspace_refs["guidance"], exposes guidance_items / guidance_refs, and applies it at the next Flat or TaskBoard safe boundary. It must not be used as completion evidence, must not be injected into non-task route prompts, and must not replace pause_for(...) / continue_with(...) when the workflow requires a blocking external answer.
• when AgentTask completion depends on a particular capability, express it as framework contract rather than prompt force: expose capabilities through planner metadata, use structured step_scope for bounded action steps, and use capability_evidence_requirements for completion evidence. For side effects such as workspace writes/readbacks, require action_succeeded evidence for the host Actions instead of accepting model claims. Preserve prior action evidence in Workspace context packs before bulky execution metadata so later Skills or Action steps can use the actual evidence, not only a summary saying evidence was collected.
• when a checkpointed AgentTask must resume after a crash, use agent.resume(task_id) or await agent.async_resume(task_id) and consume the returned task-strategy AgentExecution through .start()/.async_start(), result, stream, and meta surfaces. Treat resume_task(...) as a compatibility alias only; do not teach AgentTask.async_resume(...), task.async_run(), or a bare AgentTask handle as the recommended public lifecycle
• 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/ModelRequestResult, TriggerFlow, Dynamic Task/TaskDAG, ActionRuntime, ExecutionResource, Workspace/ContextBuilder, 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
• for public API or compatibility-line releases, run the public typing allowlist gate. compatibility/public-typing-allowlist.json is an exception ledger for documented Any boundaries with owner, reason, narrowing plan, and expiry; it is not a public-method allowlist, so new public methods must be fully typed unless they add a reviewed exception.
• 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, ExecutionResource, 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 put model-app quality gates, business scoring, or route choice into local helper functions that only count words, split tokens, search keywords, or compare snapshots when an Agently model request can own the judgment
• 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
• references/model-quality-validation.md