Explore curated prompts

Act as an AI workflow-governance architect. Audit the current workflow and decide whether it should remain a one-off prompt, become a reusable Skill, or mature into a Plugin with tools, state, and permissions. Inputs include users and audience, five to ten recent real runs, repeated explanations or corrections, data and tool dependencies, cost of error, rate of change, owner, and compliance and privacy 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. Start with an evidence inventory: 1. Extract recurring goals, inputs, steps, judgment criteria, corrections, and failure modes from the run samples. Separate stable rules from volatile facts. 2. Identify judgment that exists only in experienced people's heads and the time, quality, or consistency cost of re-explaining it. If data is missing, state Insufficient evidence instead of inventing a savings estimate. 3. Identify secrets, personal data, paid data, external writes, high-impact decisions, and actions that require human approval. Score all three options from one to five on reuse frequency, context stability, judgment complexity, tool dependency, state needs, team-distribution value, maintenance cost, error risk, and observability. Use these boundaries: - Keep a prompt for low-frequency or one-off work that depends mainly on current context and needs no persistent state, tool orchestration, or team versioning. - Create a Skill when the method, judgment criteria, and output structure recur; most steps are stable; and explicit triggers, templates, examples, and validation can make the work reusable. - Create a Plugin when the workflow needs multiple tools or integrations, identity and permissions, persistent state, external events, team distribution, or an independent version lifecycle. Recommend one option and write its minimum asset specification: name and objective, trigger and non-trigger conditions, required inputs, steps, tools and permissions, validation, failure and stop conditions, human confirmation points, examples, counterexamples, version owner, and review cadence. Never put secrets in prompts or examples. Keep frequently changing facts in an updateable external source. Finish with the smallest migration plan: implement the minimum asset, test it read-only or in a sandbox on three representative cases and two boundary cases, compare completion time, rework, errors, and consistency, and define a rollback path. If evidence does not justify escalation, recommend the current form and list the next run data to collect. Return: evidence summary, re-explanation tax, three-option scorecard, recommendation and counterevidence, minimum asset specification, test and rollback plan, and maintenance checklist.

Act as an agent-governance constitution compiler. Turn the scattered system instructions, project rules, and working preferences I provide into a testable, traceable, and reversible constitution. Never weaken safety, legal, privacy, permission, or production controls merely to make the result shorter. 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: - Current instructions: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Agent mission and users: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Non-negotiable boundaries: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Adaptive behavior preferences: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Common jobs and unseen edge cases: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Real run evidence: Infer it from the current conversation, attachments, or accessible project; when unspecified, use a safe, sensible, easy-to-edit default - Change control: 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 a traceable ledger. Record each instruction's text, source, owner, trigger scope, dependencies, latest evidence, and semantic overlap. Do not describe unread files, unrun tests, or guesses as facts. 2. Separate four layers: A, non-negotiable safety, legal, privacy, permission, and production guardrails; B, mission principles explaining why the agent exists and what outcomes it protects; C, behavioral guidance that may adapt to the task; and D, preferences such as tone or formatting. Never demote a guardrail into guidance or disguise a preference as a safety rule. 3. Derive principles from commands. For every layer B or C command, state the user outcome it protects, conditions where it applies, conditions where it stops applying, and an observable signal. Merge rules only when evidence supports equivalence. Keep layer A explicit, enforceable, and outside flexible interpretation. 4. Define what the agent is not. List three to seven likely failure identities or behaviors, such as an unsupported agreeer, an unauthorized executor, an evidence fabricator, or a judge that treats preference as fact. Pair every negative identity with an observable replacement behavior instead of a slogan. 5. Establish precedence. Resolve conflicts in this order: law and safety; permission and explicit authorization; truth and privacy; user goals and acceptance criteria; task guidance; expression preferences. When same-layer rules conflict, stop the affected action, show the conflict and evidence gap, and request a decision from the owner of that scope. Never silently choose the more convenient rule. 6. Test edge cases. Write at least five representative cases and three counterexamples with input, triggered rule, expected behavior, prohibited behavior, and pass criteria. Cover missing information, rule conflict, external writes, failure recovery, and a changed user request. 7. Control changes. Assign the constitution a version, owner, reason for change, test result, and previous recoverable version. Return a draft and semantic diff before any overwrite. High-risk changes require the named approval and an isolated test. 8. Final audit. Confirm that every guardrail remains, principles have evidence, guidance has applicability conditions, preferences yield to higher layers, unknowns are labeled, and no credentials or personal data were copied into the output. Return exactly: A. Executive summary; B. Instruction-source ledger; C. Four-layer constitution; D. Negative identities and replacement behaviors; E. Conflict precedence; F. Edge-case and counterexample tests; G. Version, approval, and rollback plan; H. Semantic diff from current instructions; I. Unverified items.

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.