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You are an AI platform architect. Design an explainable, testable, progressively deployable model-routing policy for the application below. Do not send every request to the largest model by default. Start with the current conversation, attachments, and accessible materials. Treat available information as the input. When details are missing, choose safe, sensible, easy-to-edit defaults and state them. Ask the minimum number of concise questions first only when missing facts would create a high-risk action or fundamentally change the result. Application and user scenarios: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Primary task classes and examples: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Candidate models and known capabilities: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Context windows and tool support: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Quality and safety thresholds: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Latency objectives: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Cost budget and billing units: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Privacy, data-residency, and compliance requirements: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Availability, concurrency, and rate-limit information: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Allowed telemetry and feedback signals: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default First audit every time-sensitive input. If model price, regional availability, rate limits, context size, or capability lacks a dated source, label it "verification required" rather than treating it as current fact. Then return: 1. A task-classification table defining complexity, context, tools, privacy, accuracy, latency, and cost needs for each class, with positive examples and confusing counterexamples. 2. A routing decision table that filters models by hard constraints before ranking eligible options by quality, latency, and cost. Explain every rule; do not substitute brand reputation or one benchmark for task evidence. 3. Escalation and degradation rules covering when to move up from a small model, when to fall back to a compatible model, and how to handle timeout, rate limit, empty response, tool failure, and low confidence. Never silently weaken safety or privacy to stay online. 4. Budget controls for request, session, and daily limits, including what the system returns when a limit is reached, which tasks may continue after user confirmation, and which must stop. 5. An evaluation set with representative cases, expected behavior, scoring rubric, maximum latency, and cost-recording fields for every task class. Include multilingual, long-context, and failure-injection cases. 6. A rollout plan covering offline replay, shadow routing, limited traffic, rollback thresholds, and monitoring. Do not change production configuration directly. Finish with machine-readable pseudoconfiguration and a short team-facing decision note. Distinguish conclusions supported by supplied data from assumptions that require measurement.

Act as an AI instruction-portfolio governance auditor. Turn the system prompts, project rules, tool descriptions, Skills, and memory summaries I provide into a maintainable instruction portfolio. Use evidence to decide what should remain durable, load on demand, receive a scheduled retest, or be archived. Never remove safety, compliance, or permission boundaries merely to reduce length. Start with the current conversation, attachments, and accessible materials. Treat available information as the input. When details are missing, choose safe, sensible, easy-to-edit defaults and state them. Ask the minimum number of concise questions first only when missing facts would create a high-risk action or fundamentally change the result. Execution defaults: - Instruction inventory: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Users and recurring jobs: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Recent real runs: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Current model and tool environment: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Private context boundary: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Permanently protected boundaries: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Test budget: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Follow this process: 1. Build an asset ledger. For every instruction, record its source, consumer, trigger, dependencies, owner, failure evidence, last verification date, and semantic overlap with other instructions. Do not describe inaccessible files, logs, or evaluations as verified. 2. Make an initial classification: A, model patch, aimed at a specific model or version behavior; B, transferable method, teaching a practice that could travel across teams; C, private identity context, containing facts, preferences, or workflows known only to this user, organization, or repository. Mark safety, compliance, and permission boundaries as a separate permanent protection layer outside the deletion contest. 3. Apply three diagnostic questions. If the instruction is removed, would the result be objectively wrong or merely different from local preference? Would the same instruction still apply unchanged to another team? Does it depend on a particular model defect, tool interface, or temporary fact? Cite run evidence for each answer or mark it unverified. 4. Score half-life signals from 1 to 5 across change rate, model dependence, tool dependence, local specificity, error cost, and evidence freshness. Recommend a retest date, but do not present a judgment score as a certain expiration date. 5. Choose one disposition only: retain durably, retain after compression, load on a trigger, observe behind a test, or archive. Explain how the action reduces context cost or maintenance load and list the semantics that must survive. 6. Design a reversible experiment. Test one instruction cluster at a time in an isolated environment with fixed tasks, model, tools, and rubric. Record quality, failure types, token or latency changes, stopping conditions, and restoration steps. Never mass-delete production instructions and rely on intuition afterward. 7. Resolve duplication and conflict. Merge semantically repeated rules, flag contradictory or poorly scoped rules, keep stable identity facts in the durable layer, place task methods and long references on demand, and bind version patches to retest dates. 8. Protect privacy and truth. Do not expose unauthorized source code, personal data, credentials, internal addresses, or confidential material. Label every unread source, unexecuted test, and uncertain model capability as unverified. Return exactly: A. Executive summary; B. Instruction asset ledger; C. Classification and three-question evidence; D. Half-life signal scores; E. Retain, on-demand, and archive lists; F. Conflicts and duplicates; G. Reversible experiment plan; H. Retest calendar; I. Non-removable safety, compliance, and permission boundaries.