Use to design, understand, tune, or audit a non-trivial model workflow, including framework-undecided designs, partial implementations, and production reviews: model-node responsibilities, input/output completeness, data-flow correctness or redundancy, model-versus-Host/workflow ownership, topology, evidence, lifecycle, concurrency, or observed execution effects. The user need not say Prompt review or name Agently. Use agently-request for one request family and agently-triggerflow for already-decided executable orchestration details.
Use this Skill for cross-owner design and audit. It produces ownership and handoff contracts; it does not become a scheduler, TriggerFlow definition, TaskDAG, retry engine, or RuntimeEvent protocol.
For iterative design or review, show current items and a timestamped, versioned change log at the response head; distinguish decisions from applied and verified changes. Read Multi-Round Collaboration.
Infer whether the task is a new design, a partial implementation, or a production review from the available assets and evidence. Explain the basis briefly and read Project Stages for the matching questions and deliverable. Ask only for missing facts that materially change the design. Start with the business goal, constraints, and simplest sufficient solution; do not introduce a model where an exact query or deterministic program suffices. This does not permit substituting keyword rules for semantic judgment.
Do not assume a model, SDK, or framework has already been selected. For an
unselected or explicitly different stack, express contracts in neutral roles
first and respect that stack; apply Agently owner/API mappings only when Agently
is in scope. This design review alone does not authorize a framework migration.
Unresolved product/project shape still routes through agently; cross-owner
design belongs here, while a resolved single-request or runtime question stays
with its existing mechanism owner.
For experiment reports and design documents, when the task instructions are not in English, prefer a complete document in that instruction language; a bilingual version is also acceptable. An explicit requested output language takes precedence. Apply this to the document itself, not only the chat summary. Preserve code, identifiers, raw prompts and evidence as needed; their language does not determine the surrounding report language.
Use collaborative review when it can answer the developer's question about
model-node definitions, execution effects, data completeness/redundancy, or
model/Host division of work; do not require the phrase "Prompt review".
First show the whole in-scope flow, highlighting model nodes, their duties and
input/output handoffs alongside Host work. Read references/model-request-topology.md
for the question-to-evidence checks. Use the existing topology, not another
ledger; a drawing alone does not prove runtime correctness or quality.
Group related node tables for comparison under
../agently-request/references/prompt-management.md: up to three logical nodes
may share a reply, and tightly coupled larger groups are allowed. Clear scope
can be shown with its details in the same reply. Preserve consequential-change
confirmation and revisit only affected responsibilities/handoffs after revision.
Every non-trivial linear, branching, concurrent, or looped application needs:
Use small project-defined reference data at a handoff only when it enables an independent consumer, parallel development, replay, local validation, or fault localization. Mark simulated data honestly and prove that real producer output can replace it. Do not invent a universal handoff packet.
Treat a non-terminal model result as a stage-scoped contribution: it must satisfy its local contract and create observable progress, but it does not need to claim whole-task completion. Unknown or deferred work remains explicit; terminal results and irreversible effects still require full acceptance.
references/system-boundaries.md.instant: references/model-request-topology.md.references/execution-topology-validation.md.references/information-and-evidence-design.md.references/lifecycle-and-pressure-design.md.references/observability-and-validation.md.After ownership is clear:
agently-request;agently-runtime;agently-triggerflow;agently advanced
reference rather than a standalone default Skill;agently-migration.instant output may
update UI or start cancelable/idempotent preparation, but cannot inject that
preparation's result back into the running request.Before implementation, confirm that every invariant has one owner, every request boundary has a reason, every output field has an authorized consumer, every provisional path has invalidation behavior, every loop has progress and terminal rules, every cross-boundary identity has freshness/correlation, and every validation rule has a declared producer or deliberately hidden host gate.
Do not replace semantic routing, relevance, planning, or quality judgment with keywords, regex, tokenization, or local score tables. Do not add a parallel execution topology beside TriggerFlow or TaskDAG.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.