Agently Request

Use this Skill when the work can be owned by one ModelRequest family. Use agently-triggerflow when a later semantic step needs a tool result, system lookup, approval, artifact readback, or host computation produced after the first request, or when branching, concurrency, retry, or pause/resume must stay visible in the application lifecycle.

Read by Need

• Provider, endpoint, environment, settings namespace, or connectivity: model-setup.md.
• Prompt slots/config, mappings, reusable contracts, or Agent inheritance: prompt-management.md.
• Structured output, Pydantic, ensure/validation, streaming formats, or direct long-output delivery: output-control.md.
• Reusing one request result as text/data/meta or consuming its streams: model-request-result.md.
• Session continuity and durable memory: session-memory.md.
• Embeddings, RecordStore, ContextSource, retrieval, and grounded citations: knowledge-base.md.
• Cross-source progressive disclosure and real-world Skills: context-and-skills.md.

Request Contract

• When it is known that the user is developing with Agently and is doing solution design, process optimization, or Prompt review, use the collaborative method by default without waiting for the user to request a table. Confirm the scoped request inventory and responsibilities first. For user-selected or justified critical requests, default to one table-first Prompt design, then wait for confirmation/revision. Group long slots by topic and expose model-visible examples separately from reviewer-only notes. See references/prompt-management.md; routine unselected requests do not need a new approval ceremony.
• After measured schema/ensure/length failures, consider a shallower model-facing projection or coherent request splits with Host reconstruction and unchanged final validation. See references/output-control.md.
• Keep a one-off fluent request readable as one chain: .input(...), .info(...), .instruct(...), .output(...), then its result call. Split only for actual reuse, independently owned configuration, or dynamic composition.
• Do not promote literals or behavior from a single observed instance into normative prompt instructions. Derive a general invariant and test contrasting cases; use illustrative examples only to explain an already stated rule, and keep their total rendered content smaller than the non-example normative prompt.
• Put runtime facts in input, authoritative evidence/API/schema material in info, behavior and transformation rules in instruct, and the exact downstream-consumed shape in output.
• Define each consumed field's type, meaning, requiredness, enum/format/range, nullability, and cross-field constraints where applicable.
• Give the model every non-sensitive satisfiable validator rule before the first attempt. Deterministic validation remains authoritative; retry feedback repairs a declared contract and must not become blind rule discovery.
• Use ModelRequest structured output for prose-derived intent, routing, relevance, grading, and acceptance. Host code owns schema/type checks, authorization, arithmetic, offered-key membership, and side effects.
• Combine semantic fields in one ordered response only when they share the same request-time evidence snapshot and later fields need no post-dispatch fact. Streaming cannot inject a tool or host result into an in-flight request.
• Validate schema, offered keys, authorization, and deterministic constraints before a real call or side effect.

For VLM requests, prefer .image(question=..., file=...|url=...|files=[...]|urls=[...]). Use .attachment(...) only when the caller owns provider-style mixed content or exact content ordering.

Results, Context, and Memory

• Direct ModelRequest calls return ModelRequestResult; Agent quick chains return AgentExecutionResult. Reuse the same result facade for text, parsed data, metadata, and streams instead of issuing the request again.
• When no consumer needs progress, await the final getter directly. Treat instant fields as provisional UI or cancelable/idempotent preparation and reconcile them against the final validated result.
• Session memory is not workflow state. SessionMemory owns extraction and compression policy; RecordStore owns durable records and retrieval; TriggerFlow execution state owns workflow progression.
• Keep raw retrieval records cold. Give the model bounded task-relevant facts and one host-issued key per candidate, then validate and reconstruct canonical identities in host code.
• For retrieval-backed answers, offer trusted ref_id values, require [[ref:<ref_id>]], and resolve approved source cards/links host-side.

Avoid

• Handwritten provider HTTP, prompt templating, JSON repair, or retry loops before checking Agently settings and output contracts.
• Moving a one-use schema or prompt step away from its request chain only to shorten the visible code.
• Re-requesting the model separately for text, data, and metadata.
• Treating retrieval hits, memory records, provisional stream fields, or model prose as deterministic proof of authorization or side effects.
• Turning entity literals, one-time input or environment state, a historical incident, test fixture, or expected answer from one observed instance into a prompt branch, or letting illustrative examples create behavior that the normative contract never states.