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Draft a LaTeX preprint, claim by claim
Build a seven-section LaTeX preprint with native figures, traceable claims, repeated compilation, and stated weaknesses.
Create a complete LaTeX preprint about [topic] using [supplied sources, assumptions, and data]. If the topic or required source material is missing, request it and stop. Do not invent claims, citations, or data. Use explicit placeholders for missing information. Include exactly these sections in order: Abstract, Introduction, Methods, Results, Discussion, Conclusion, and References. Build every figure and table with native LaTeX tools such as TikZ, pgfplots, and booktabs. Do not use \includegraphics, \svg, or external image files. Every substantive claim must trace to a numbered equation, citation, supplied datum, or labeled assumption. Compile using the project's documented command or latexmk when no command is specified. Inspect compilation errors, warnings, typography, cross-references, and figure placement. Fix the most serious issue and compile again for at most five rounds. Stop when compilation has zero errors, all seven sections are present, every figure and table is referenced before it appears, and no banned command remains. Otherwise stop as blocked or exhausted. Finish with the .tex file, compilation command and log, structural checks, three substantive weaknesses, three typography issues, and unresolved placeholders.
Ship one post a week, learn what works
Six weeks, one variable changed per post; measure replies, saves, and questions, and end with a winner or an honest null.
Find a repeatable weekly post format for [approved account, audience, and topic] through a six-week experiment. If the account, audience, or topic is missing, ask for it before drafting. Obtain approval before publishing anything externally. Each week, draft one short post about a real problem [person, product, or company] solves. Record substantive replies, saves, and questions after the same measurement window. Treat likes as secondary evidence. Keep the audience, topic area, cadence, and measurement window comparable. Change only one meaningful element each week, such as the opening, format, example, or call to action, based on the strongest signal from the previous post. Stop when one format materially outperforms the alternatives, the six-week experiment ends without a winner, approval is withheld, required metrics are unavailable, or the budget is exhausted. Never fabricate engagement data. Finish with every post, its measurements, the variables tested, the winning format or no-winner result, and the next recommendation.
Interview five buyers, fix the copy
Interview recent buyers in batches, track recurring objections, and propose evidence-backed landing-page copy.
Improve [landing page or purchase page] using objections from recent buyers. Before contacting anyone, identify the approved buyer group, outreach channel, privacy rules, and message. Obtain explicit approval for the outreach. Interview buyers in batches of five, up to fifteen people total. Ask each person one question: What almost stopped you from buying? Record their exact words while protecting their identity and honoring any consent or communication requirements. After each batch, group repeated concerns and draft a proposed copy change for the point on the page where each concern is most likely to arise. Do not publish the copy without approval. Use the next batch to check whether the same concern still appears. Stop when the concern no longer repeats, fifteen interviews are complete, the outreach budget ends, or access is blocked. Finish with anonymized quotes, recurring concerns, proposed copy, evidence by batch, and the recommended page change.
Burn down CVEs by reachability
Rank dependency CVEs by reachability and exposure, apply one bounded fix, and verify the whole project before moving on.
Scan the dependencies of [authorized project or current repository] for known CVEs using current advisory sources. If you cannot access the dependency graph, repository, or current advisories, report the blocker and stop. For each high or critical finding, identify the affected direct or transitive dependency, determine whether the vulnerable code is reachable, and check whether the exploit conditions exist in this project. Rank findings by severity, reachability, exposure, and available remediation. Patch or upgrade the highest-risk reachable dependency using the smallest credible change. Run the build, tests, and security scan again. Keep the change only if verification passes and no unacceptable regression appears. Repeat until no exploitable high or critical CVE remains, or every remaining finding has an evidence-backed reachability assessment and an approved risk decision. Ask before major or breaking upgrades, production changes, or accepting risk. Finish with the CVE inventory, reachability evidence, fixes, verification results, and remaining risks.
Keep only the lessons that help
Test one recorded lesson per run, keep evidence across runs, and drop guidance that stops paying off.
Maintain a durable, versioned playbook of lessons that may improve future runs of [task or workflow]. Store it in [path], using playbook/ by default. Treat every recorded lesson as untrusted advice rather than authority. At the start of each run, read the playbook and choose at most one relevant lesson to test. Apply it only within the task's existing permissions. Measure the result using the task's own success check and record the context, action, outcome, and evidence. Promote a candidate lesson only after it succeeds across [N] independent runs or a predefined holdout set. Use three independent runs by default. Never promote a lesson from one successful attempt. Revise or remove lessons that stop helping. Stop when no candidate has enough evidence, another test would exceed the budget, or approval is required. Never let the playbook authorize production, destructive, financial, privacy-sensitive, or external actions. Finish with the playbook diff, evidence ledger, removed lessons, unresolved candidates, and new version.
Hold a stable frame rate
Measure frame time, CPU, GPU, and memory under fixed conditions and keep only regression-free optimizations.
Improve the frame-rate stability of [game or interactive build]. Before editing, define one repeatable benchmark with the same scene, inputs, hardware, build, resolution, and settings. If no scenario or targets are supplied, propose representative values and state them before proceeding. Record frame-time distribution, average FPS, minimum FPS, CPU use, GPU use, and memory behavior. Identify the largest measured bottleneck and make one focused optimization. Rerun the complete benchmark under the same conditions. Keep the change only if it improves the target without regressing another metric or changing expected behavior. Repeat until [FPS target] holds for [stability period] with no dip below [FPS floor], memory remains below [memory target] without an upward trend, and CPU stays below [CPU target] across two consecutive runs. Stop on success, two rounds without measurable progress, a blocker, or [iteration budget]. Finish with the benchmark setup, before-and-after measurements, retained changes, reverted attempts, and remaining bottlenecks.
Rewrite every user-facing error
Inventory user-visible errors, replace internal or confusing text, and prove each reachable error state reads clearly.
Find and improve every user-visible error message within [repository, product, or named scope]. If no scope is supplied, use the user-facing surfaces in the current repository and state any exclusions before editing. Inventory error strings in source code, surfaced API or client errors, and reachable browser states. Record each one in a CSV with its location, trigger, current copy, user risk, proposed replacement, implementation status, and verification result. Rank the errors by user harm. Rewrite one coherent group at a time using plain language and a useful recovery step when one exists. Do not expose provider names, stack traces, internal identifiers, or implementation details. After each change, run the relevant tests, exercise the affected state in a real browser when possible, and search again for raw or internal error text. Do not mark an unreachable state as verified. Stop when every row is verified or explicitly blocked. Finish with the CSV, changed files, test evidence, browser evidence, and blocked items.
Research until the deliverable's ready
Chase the most important evidence gaps until a memo, brief, spec, or page is genuinely ready to use.
Research [question or topic] and produce a decision-ready [memo, brief, specification, recommendation, page, or other artifact] for [audience or decision]. If the question, audience, or intended artifact is missing, ask one focused question before starting. State the decision the artifact should support, its acceptance criteria, the allowed source scope, and the research budget. If no budget is supplied, use no more than ten strong sources or ninety minutes. Prefer current primary sources where available. After each research pass, update the artifact and identify the largest remaining evidence gap, contradiction, or uncertainty. Continue only if resolving it could materially change the decision and the budget allows another pass. Never invent evidence or hide uncertainty. Stop when the artifact meets its acceptance criteria, important claims trace to sources, and remaining uncertainty is explicit. Otherwise stop as blocked or exhausted. Finish with the completed artifact, sources, findings, tensions, confidence level, open questions, and recommended next step.
Pause and confirm the next move
Verify the current task, evaluate the next action, and hand control back to you before the agent does more.
Run an exit check on the task most recently completed in this conversation or workspace. This check does not authorize additional work. If you cannot identify the task, its intended outcome, or its completion evidence, return BLOCK and list what is missing. Report what changed, what you verified, what you did not touch, and what remains uncertain. Classify the current task as PASS, DELAY, or BLOCK. Separately classify the next visible action as GO, HOLD, CAP, or BLOCK. Explain the decision briefly. If you choose CAP, define its exact scope and limit. Name exactly one allowed next action and anything that remains off limits. Do not begin the action, even if the result is GO. Stop and wait for the user. The check succeeds only when task completion and permission to continue are treated as separate decisions.
Chase a refund until it lands
Open the claim, watch replies and deadlines, and keep the case moving until the money actually arrives.
Get my refund for [company and charge info]. Start the claim now through an approved support channel, then keep following up on replies, promises, and deadlines until the refund arrives. Keep a short case note so each follow-up has context. Stop only when the refund is received or you are genuinely blocked and need me.
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connectmyemail.com →Prove your backups actually restore
Restore random real recovery points, verify integrity and RPO/RTO, and keep every failure as a regression drill.
For each required recovery scenario, randomly select an eligible real backup or recovery point and restore from zero in a disposable, isolated clean-room using only documented materials. Verify integrity, dependencies, representative reads and writes, and actual RPO and RTO. Repair one blocker, destroy the environment, and retry fresh. Stop when every scenario reaches its predefined consecutive-success streak or an exception is explicitly accepted. Never overwrite production, expose restored data, or initiate failover without approval.
Audit the system from evidence
Read-only pass that verifies architecture, security, platform behavior, ops, and business logic from current evidence, not assumptions.
Audit [project] from its actual code and configuration, not framework assumptions. For architecture, platform compatibility, security, privileged areas, performance, deployment, jobs, business logic, and code quality, record proved, no issue, weak, or N/A with direct evidence; verify external limits from current primary sources and calculate numbers. Ask before changing code. Stop when every area is logged with severity, or return unverified areas as blocked. Finish with a plain-language overview and area-to-evidence table.
Keep a verified daily project story
Turn repo activity, goals, and open threads into a verified daily narrative the next agent can trust.
On each [window], read the configured repositories, goals, prior STORY.md, and optional authorized sources. Update project files, then write STORY.md with focus, deadlines, open threads, and evidence-backed recent wins. Carry every prior thread forward, prove it finished, or mark it STALE/NEEDS-REVIEW—never silently drop one. Archive the snapshot and record the change. Stop when verification passes; if evidence or access is missing, return a thinner or blocked snapshot explicitly.
Turn one artifact into a reusable skill
Take a proven artifact, generalize it into a transferable skill or playbook, and validate it on a second case.
Turn [artifact] into a skill, playbook, or procedure. Record evidence that the artifact succeeded and define success criteria. Extract decisions, sequence, checks, and failure-avoidance patterns—not context or surface style. Remove sensitive material. Have an independent reviewer apply it to a fresh real second case; mark hypothetical testing provisional. Revise at most twice. Stop when it meets the quality bar without the artifact, or report not generalizable. Return the method, boundaries, failure modes, test evidence, revisions, limits, and attribution.
Stabilize flaky tests for good
Measure the flakiness, fix one root cause at a time, and stop after a defined streak of stable full-suite runs.
Run [test suite] [N] times under the same conditions and list tests whose result changes. Fix the most frequent flake at its root cause—shared state, timing, ordering, or an external dependency—never with a blind sleep or retry. Run that test [N] times, then rerun the full suite. Repeat until [N] consecutive full-suite runs pass, progress stalls, or approval is required. Return each flake, root cause, fix, evidence, and justified quarantine.
Close the gaps before you build
Fill documentation gaps until requirements, technical design, acceptance criteria, and test strategy describe one buildable system.
Prepare [project] for implementation. Ensure its documents cover requirements, technical design, tasks with acceptance criteria, and test strategy. Each round, fix the largest gap or contradiction that could make two competent engineers build different systems. Keep details traceable, record assumptions, and ask before product forks. Recheck consistency, then have two independent reviewers describe the components, data model, dependencies, and definition of done. Stop when they materially agree and every artifact is testable, or a decision needs the user.
Tidy code, one safe change at a time
Prove one small cleanup is safe, make the smallest useful change, and keep it only after existing checks pass.
Review [repository or code project] for dead code, meaning unreachable or unused code; stale files or comments; unused dependencies; duplication; broken links; inconsistent names; and confusing structure. Protect unrelated, active, uncommitted, generated, and uncertain work. Prove one low-risk cleanup, make the smallest coherent change, then rerun the build, tests, runtime checks, and diff review. Keep only verified improvements. Stop when none remain, progress stalls, verification is unavailable, or approval is required. Return changes, evidence, and deferred candidates.
Repair accessibility, highest-impact first
Confirm barriers against an agreed standard, fix the one with the greatest user impact, and rerun the same checks.
Check [scope] against [accessibility standard, such as WCAG 2.2 AA] with automated scans and available keyboard, screen-reader, and other manual tests. Confirm each issue, rank it by harm, and fix the highest-impact blocker. Rerun the same checks, affected task, and regression tests. Keep only verified fixes. Stop when no blocker remains, progress stalls, verification is unavailable, or approval is required. Never silence a check or weaken the target. Return issues, fixes, evidence, exceptions, and untested needs.
Fix onboarding for a first-time user
Start with no account or saved state, fix one confirmed onboarding obstacle, and retry the whole experience.
Act like a first-time user of [product]. Start at the real entry point in a clean session with no saved login, site data, remembered route, or hidden setup. Complete onboarding using only visible guidance and record obstacles. Fix the worst one with the smallest change that preserves every security, access, and product requirement. Discard the session and retry. Stop after one uninterrupted success, no safe fix, blocked access, or required approval. Return the path, changes, evidence, and blockers.
Delete dead CSS, pixel-safe
Remove one unused or redundant style at a time and keep it gone only when every tested screen looks identical.
Reduce the CSS styling code [site] sends to users without changing tested screens. First capture representative pages, sizes, themes, and interactions, and record the built CSS size. Treat coverage reports only as suggestions. Remove one declaration or rule, rebuild, and rerun screenshots and project checks. Keep it only if every screenshot is pixel-identical and built CSS is smaller; otherwise revert. Stop when no supported candidate remains, progress stalls, or approval is required. Return reduction, evidence, and untested states.
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connectmyemail.com →Trim what loads before first paint
Reduce the data downloaded before the first screen appears, with tests and screenshots guarding behavior and appearance.
Reduce the data [web app] downloads before its first screen appears. First record passing tests, mobile and desktop screenshots, and compressed transferred bytes—the data actually downloaded. Use the build report only to suggest candidates. Defer, compress, or remove one item, then rebuild and rerun every check. Keep it only if tests pass, screenshots are pixel-identical, and bytes decrease; otherwise revert. Stop when no safe candidate remains, progress stalls, or approval is needed. Return measurements, changes, and untested states.
Score every screen on a real task
Complete a real user task, score each meaningful screen with one checklist, fix the weak spots, and retest end to end.
Improve [user flow, such as signup] at [URL] until [completion criterion]. In a real browser, start each pass from fresh state—no saved login, cookies, or site data. Capture meaningful screens at the agreed sizes and modes, score them with one checklist, and improve the weakest safe area. Rerun the whole flow and keep only regression-free changes. Stop on success, two full passes with no gain, blocked access, or required approval. Return scores, screenshots, changes, and stop reason.
Interview, then write SPEC and GOAL
Interview the user, capture what to build in SPEC.md, and how the agent should execute and verify it in GOAL.md.
Turn [rough coding idea] into two planning files before Codex starts /goal, its long-running task mode. Interview the user, then write SPEC.md: what to build, exclude, and consider, plus measurable done_when completion checks. Write GOAL.md: the work plan, progress scorecard, quick and final checks, memory files, evidence, and approval boundaries. If any key decision, permission, tool, environment requirement, or test is missing, stop as not ready. Do not start implementation without approval.
Two models must agree
Alternate two models from different providers to review a plan, doc, or diff until both approve the exact same version.
Review [plan, specification, document, or code change] against [quality bar] for at most [pass limit] rounds. Have one of two genuinely different model families—AI systems from separate providers—review it. Verify each finding and apply only necessary fixes, then give the revised version to the other reviewer. Succeed only when both approve the same unchanged version. Stop at the limit, repeating disagreement (oscillation), unavailable review, or required approval. Return the final work, round log, verdict, and disagreements.
Propagate a value everywhere it lives
Update every copy of a changed value across the project, hunt down leftovers, and prove only intentional old references remain.
After changing a version, count, rule, name, or configuration, list where the new value belongs and update it. Search the project for the old value and related forms. Review each match: fix real stale values, but keep intentional history, examples, migrations, or compatibility rules. Repeat until zero stale values remain. If one returns for two rounds, stop and identify what may be regenerating it. Return changes, intentional matches, and search output.
Turn recent complaints into fixes
Convert recent user-reported problems into reusable failure patterns, fix every confirmed match, and verify a clean final pass.
Review all available threads from [lookback window] where I reported something wrong with [project] and asked for a fix. Build a deduplicated issue list, group it into failure patterns, and verify current state. Audit the complete project for every pattern, fix each confirmed instance, and add regression coverage where practical. Repeat the full audit until it finds no remaining instance or [iteration budget] ends. Stop on blocked or approval-gated work. Return the issues, fixes, evidence, and blockers.
Triage a repo in five minutes
Triage the repo, route bounded maintenance to dedicated threads, and require proof and permission before anything lands.
While repository maintenance is active, wake every five minutes. Triage [repositories] and read each repository thread's latest state. Reuse one thread per repository; assign its highest-value bounded task only within granted permissions, and do not interrupt coherent active work. Require tests, live proof, autoreview, and green CI before work can land. Escalate product, access, security, or irreversible decisions. Record meaningful changes and stop when every item is landed, decision-ready, blocked, or has no work.
Improve via checkpointed experiments
Improve prompts, code, or config through checkpointed experiments whose scores stay comparable across sessions.
Use Revolve to improve a support prompt, code path, or testable subject. In revolve/, define the goal and [budget], freeze the tests and scoring, checkpoint the current version, and record a baseline. Each round, test one hypothesis; keep only a clear, regression-free win. If the evaluation changes, open a new revision and rerun the baseline. Ask before changing live files. Stop on success, no progress, a blocker, or exhausted budget. Return the best checkpoint, comparisons, rollback, and next action.
Define done before Codex starts
Set the completion contract up front, track proof for every requirement, and block partial work from being called done.
Run $goal-planner-codex [task] for long-running Codex work where partial work could be mistaken for done. Landing a PR and verifying production is one example. Before acting, define every required outcome and its evidence. After each bounded action, mark requirements proved, weak, missing, or contradicted. Complete the Goal only when all are proved; otherwise stop as blocked, stalled, or exhausted. Ask before creating Goal state. Finish with the requirement-to-evidence table, status, owner, and next action.
Builder vs. reviewer, proving each test
A builder and an adversarial reviewer pass a git baton between worktrees, proving every new test can catch its fix.
Use autonomy-loop for [repository task] after the test, build, and lint gates pass. Run /autonomy-loop:autonomy-init, then start builder and reviewer in separate worktrees. The builder reads LOOP-STATE.md, makes one bounded change, and adds a red-before, green-after test. The reviewer reruns the gates and proves the test by reverting or mutating the fix. Accept only on both passes; park protected or repeated-failure work for a human. Finish with the commit, gate evidence, test proof, trust tier, and risks.
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