Let AKA autonomously add, run, inspect, and revise intra-kernel timeline probes for standalone CUDA/inline PTX or CuTe DSL when ordinary benchmark, NSYS, or NCU evidence cannot answer a specific kernel-internal timing question.
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Generate a structured implementation plan from an evidence draft. Validate paths, obtain configured independent Codex and Qoder reviews, synthesize available advice against repository evidence, preserve the draft, and produce testable acceptance criteria and validation steps.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
This repository-native skill is adapted from the gen-plan flow in PolyArch Humanize. It turns an
episode draft into a complete implementation plan without modifying source code or starting the
implementation.
--input <path>: required draft document.--output <path>: required new plan document.--direct: generate a one-shot plan without asking questions.--discussion: ask for decisions that materially change the plan. This is the default when no
mode is supplied.--direct and --discussion are mutually exclusive.
Python supplies ATREX_EPISODE_MODE (default: full). Fast/full plans must contain exactly
one coherent optimization category. Goal plans may cover multiple evidence-backed directions,
including interacting algorithm, layout, and kernel changes; validate the combined result.
The campaign selects the mode and reviewer configuration. Both reviewers default off
for V1, fast episodes, and full episodes. Enable the desired reviewer with the
stage-specific --v1-ask-*, --fast-episode-ask-*, or --full-episode-ask-* flag.
When invoking the shell helpers outside a campaign, explicitly set
ATREX_PLAN_REVIEW_CODEX_ENABLED=1 or ATREX_PLAN_REVIEW_QODER_ENABLED=1 to enable
that reviewer; otherwise it stays disabled.
ask_codex and ask_qoder consultations are read-only. They are non-persistent by
default; an explicitly configured long Codex or Qoder reviewer session remains read-only and
campaign-private. Give every enabled reviewer the same candidate proposal and bounded evidence
packet, isolate both from the project, and disable Qoder tools.Execute these phases sequentially.
From the campaign workspace, run:
bash skills/gen-plan/scripts/validate-gen-plan-io.sh \
--input <input> --output <output> <mode>
Stop on a nonzero exit. The script reports the resolved input, output, template, and mode. It never creates the output file.
Read the draft and quickly inspect the workspace README, goal, current kernel, prior memory, and any paths named by the draft. Reject only a draft that is clearly unrelated to this repository. Be lenient with informal drafts and mixed languages.
Build an evidence-to-action chain:
Before consulting either reviewer, populate
skills/gen-plan/templates/candidate-proposal-template.md in automatically removed process scratch.
This frozen candidate proposal is not the final plan. It must state the selected evidence-to-action
chain, the episode mode and permitted optimization categories, target paths and symbols, the expected mechanism, scope
constraints, rejected directions, validation and falsification conditions, and unresolved
assumptions. Do not persist the candidate proposal in the campaign workspace.
Check the draft for unclear scope, contradictions, missing dependencies, infeasible changes, and quantitative targets. Treat numeric performance targets as trends unless the draft explicitly marks them as hard acceptance thresholds.
In direct mode, make conservative assumptions and record unresolved material choices under
Pending Decisions; do not pause for questions. In discussion mode, ask only questions whose
answers materially change scope, correctness, or acceptance.
The campaign independently configures Codex and Qoder for fast and full episodes. It probes a
reviewer only when that reviewer is first enabled for the current episode mode, then caches the
availability decision in private runtime state. A reviewer disabled by configuration or by its
availability probe must not be retried; retain the helper's disabled status and recorded reason.
Campaign restarts reuse cached availability decisions.
After completing the initial analysis, freeze one evidence packet and use the bundled review helper
before finalizing the plan direction. Give every enabled reviewer the candidate proposal, original
draft, and the same small set of directly relevant text files, normally README.md, kernel.py,
the latest canonical memory entry, and source or profile summaries cited by the draft. The candidate
is the primary review target; the draft and context are evidence for testing its claims. Never
include credentials, raw secrets, unrelated files, or large binary profile artifacts.
bash skills/gen-plan/scripts/ask-reviewers.sh \
--input <input> \
--proposal <temporary-candidate-proposal> \
--context README.md \
--context kernel.py
--proposal is required and must name the non-empty frozen candidate proposal in process scratch.
Add other --context arguments only when they materially affect the plan. The helper starts the
enabled external reviewers concurrently so neither can see or anchor on the other's response.
When proposal, draft, and context would exceed Qoder's five-attachment limit, the helper folds all
context files into one labeled temporary bundle and gives that identical bundle to every enabled
reviewer. Do not manually remove evidence or issue a second full review call. For an eligible
transient failure, the helper retries only the failed reviewer once; it does not rerun a successful
reviewer and does not retry quota, authentication, timeout, disabled, or missing-CLI failures.
Enabled external reviewer processes always use maximum reasoning effort; episode/session settings,
reviewer effort environment variables, and legacy --reasoning-effort arguments cannot lower it.
By default each external review is ephemeral. --long-reviewer-session codex or
--long-reviewer-session qoder resumes one campaign-private, read-only reviewer thread across
episodes while continuing to send the complete current candidate proposal, draft, and bounded
context on every call. Long Claude reviewer sessions are not implemented and fail explicitly.
Session state lives under .atrex_long_horizon/ and must never enter a candidate commit. Because
qodercli only resolves resumable sessions within the current working directory's project, a
persistent Qoder reviewer runs from a dedicated directory alongside its state file, which also keeps
it isolated from the candidate project.
Each review returns its backend-specific summary marker followed by the same five assessment
sections:
CODEX_SUMMARY or QODER_SUMMARYRISKSMISSING_REQUIREMENTSDIRECTION_RECOMMENDATIONSVALIDATION_RECOMMENDATIONSQUESTIONS_OR_ASSUMPTIONSIf the current episode backend is Codex or Qoder and its matching
ATREX_PLAN_REVIEW_*_ENABLED value is not 0, first review the frozen candidate proposal and retain
that backend's review in the current session using the same sections. Only then run the helper; it
skips the matching nested process and obtains any other enabled backend's independent review. Mark
the retained review status current_codex_session or current_qoder_session. Do not revise it after
seeing the external review; resolve new information only during synthesis. If the matching reviewer
is disabled, do not create an in-session substitute review; let the helper record disabled.
After the helper's selective retry, if an enabled reviewer is unavailable, times out, or fails, do
not fabricate its advice or rerun the successful reviewer. In direct mode, continue with the
available review and conservative analysis, recording each status and failure reason. If every
enabled reviewer fails, continue using only the primary analysis and label the result as unreviewed.
In discussion mode, ask whether to retry only the failed reviewer or continue with partial or no
independent review. A reviewer explicitly marked disabled is not a failure and must not trigger a
retry question. If every reviewer is disabled, continue with primary analysis and label the plan as
intentionally unreviewed.
Compare all available reviews only after the helper has completed. When both are enabled, treat agreement as a useful confidence signal, not proof, and resolve disagreement from the original draft and repository evidence rather than by majority vote. Evaluate every recommendation as follows:
Record how the frozen candidate changed after review. Every material correction must identify the reviewer and supporting evidence; if the candidate remains unchanged, state why the reviews did not justify a change.
Available reviewers are advisory, not authoritative. The final plan must remain a superset of the human draft and must still contain exactly one optimization category in fast/full mode.
Use skills/gen-plan/templates/gen-plan-template.md as the output schema. Replace every placeholder
with concrete content. The plan must include:
AC-N form, each with positive and negative tests;Use milestones, phases, and steps rather than time estimates. Refer to code by path and symbol, not
by line range. Plan terminology such as AC-N, Milestone, and Phase belongs in the plan only and
must not be prescribed as implementation naming.
Before writing, verify that the plan:
Write the complete plan to the validated output path, then read it back and fix any remaining placeholder, inconsistency, or missing draft content. Report the output path, optimization category, both consultation statuses, adopted-suggestion count, acceptance-criteria count, and pending-decision count.
| Exit code | Meaning |
|---|---|
| 0 | Validation passed |
| 1 | Input file not found |
| 2 | Input file is empty |
| 3 | Output directory does not exist |
| 4 | Output path already exists |
| 5 | Output directory is not writable |
| 6 | Invalid arguments |
| 7 | Plan template is missing |
| Exit code | Meaning |
|---|---|
| 0 | Consultation completed, or nested invocation intentionally skipped for the matching backend |
| 1 | Draft, candidate proposal, or context input is invalid |
| 2 | Helper arguments or environment configuration are invalid |
| 3 | Reviewer response is missing one or more required review sections |
| 124 | Reviewer consultation timed out |
| 127 | Reviewer CLI could not be found or started |
ask-reviewers.sh reports the exit status of each child consultation in its structured output and
returns successfully once both attempts finish, allowing direct mode to retain a surviving review.
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Let AKA autonomously add, run, inspect, and revise intra-kernel timeline probes for standalone CUDA/inline PTX or CuTe DSL when ordinary benchmark, NSYS, or NCU evidence cannot answer a specific kernel-internal timing question.
中文简介正在准备中。当前显示原文说明。
Learn the target framework from enabled knowledge tools and implement a baseline GPU kernel. Use this skill to understand compute semantics, determine the target platform and framework, search reference implementations, and produce a correct V0 baseline with performance records for later profile-driven optimization.
中文简介正在准备中。当前显示原文说明。
Run the evidence loop of one long-horizon GPU kernel optimization episode. Use this skill to reconstruct the incumbent, profile and localize a bottleneck, research progressively, plan one coherent direction, implement and repair, validate development correctness and performance, and record every decisive experiment in the episode journal.
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Mine a per-kernel optimization trace — a git repository capturing successive versions of one kernel being optimized — into structured, gate-validated optimization-experience records for the GPU kernel wiki. Use when asked to distill an optimization run, a kernel_opt trace directory or a version ladder into wiki records; to report what such a run actually achieved; to build, extend, re-run or validate the staging store behind those records; or to explain how a trace-derived record's number, snippet or provenance was established.
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Choose and run ACU-only, adaptive PPU in-kernel timeline, or optional bounded joint analysis for a PPU kernel. Use for device-level bottleneck diagnosis, kernel-internal critical-path questions, or evidence that genuinely needs both; do not require all three modes.
中文简介正在准备中。当前显示原文说明。
Quickly extract a typed GPU-Wiki query intent from prose. Never inspect or query a knowledge store.
中文简介正在准备中。当前显示原文说明。