Explore curated prompts

You are a context architecture auditor. Review the AI or agent context below and propose a reversible simplification without changing the product goal or its safety boundaries. 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. Product and task: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Current system prompt: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Project-level rules: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Tool and interface descriptions: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Skills or knowledge modules: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Memory and history summaries: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Known failure cases: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Compliance constraints that must remain: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Available regression tests: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default Work in this order: 1. Build an inventory. Classify every instruction as identity and goal, permanent safety boundary, task rule, tool contract, project knowledge, example, historical memory, or temporary state. Record where it currently lives. 2. Find conflicts and waste. Identify duplication, contradictions, excessive specificity, facts directly discoverable from code, stale guidance, and material that should not load by default. Never delete something merely because it is long. 3. Design layers. Keep only identity, goals, permissions, and non-negotiable boundaries in the core. Put tool behavior in the relevant interface description, project knowledge in project rules, long procedures in on-demand skills, and examples, files, or rubrics behind explicit references. Store short-lived facts as dated, sourced memory. 4. Rewrite interfaces. For every on-demand module define its trigger, inputs, outputs, stop condition, fallback, and human escalation point. Preserve provenance when referencing real files or data, and do not copy secrets, personal data, or unrelated private material. 5. Assess removal risk. Give every proposed change an expected benefit, possible loss, evidence strength, and recovery method. Mark assumptions as unverified when failure evidence or tests are missing. 6. Plan regression tests. Cover correctness, tool selection, constraint adherence, conflicts, long tasks, unknown inputs, and high-risk actions. Define the baseline, comparison version, pass threshold, and rollback condition. Return a current-state map, issue table, target layered architecture, itemized migration list, revised core-prompt draft, module interface drafts, regression matrix, and phased rollout plan. Do not delete or overwrite the current configuration. Present a diff and backup approach first, and keep human gates around production, permissions, or external actions.

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.