跳到正文
cccskills
免费在 GitHub 公开

measure-before-you-fix

Investigate slow jobs, stale data, and timeout alerts by timing the specific operation and comparing monitoring thresholds. Produce an evidence-based verdict that distinguishes insufficient time, stalled work, and false alerts before proposing changes.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Before fixing a slow/stale/timeout alert, measure the step yourself. Kill the theory with a stopwatch, not a code change. Measure-first ops triage for temporal alerts (stale, timeout, freshness, wedged, N hours behind) from gbrain doctor, autopilot, sync, and cron monitors — runs BEFORE any timeout raise, threshold change, or pipeline rewrite.

查看安装方法

适用场景

  • Investigate repeated timeout alerts
  • Check whether stale-data warnings reflect actual lag
  • Compare monitor thresholds with gbrain doctor
  • Distinguish slow work from stalled processing

包含的文件(2)

  • SKILL.md9.8 KB
  • routing-eval.jsonl1.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Measure Before You Fix

Convention: see conventions/brain-first.md — before re-deriving a diagnosis, search the brain for prior incidents of the same alert. A recurring alert usually has a recorded verdict already.

Route here on any alert whose claim is temporal — "X is stale", "step timed out", "pipeline wedged", "job is slow", "N hours behind". These alerts invite an immediate structural fix (raise the timeout, split the step, reorder the pipeline). Do the measurement first. It is almost always cheaper than the fix, and it frequently invalidates it. (Routing is a harness convention, not a mechanical guarantee — the contract below is the discipline that makes it stick.)

On gbrain surfaces this covers: gbrain doctor staleness checks (e.g. sync freshness, cycle freshness), autopilot cycle alerts, the sync stall watchdog (reason: 'stall_timeout'), and any cron monitor built on top of them.

The rule

One stopwatch measurement of the suspect step, before any code change.

If you cannot state the measured duration of the thing you claim is slow, you do not yet know the root cause, and any fix you write is a guess wearing a diff.

Contract

This skill guarantees:

  • No structural fix (timeout raise, step split, pipeline reorder, wrapper rewrite) is proposed before a measured duration of the blamed step exists.
  • The measurement targets the specific entity the alert named, not the aggregate (--all hides which member is slow).
  • The alert's threshold is compared against the authoritative one (gbrain doctor's warn/fail lines) before the system is declared unhealthy.
  • The verdict explicitly distinguishes "needs more time" from "is wedged" — they have opposite fixes.
  • Read-only: this skill changes no timeouts, thresholds, or code. It produces a measurement verdict that sizes the fix; the fix itself is a separate, now-informed change.

Procedure

  1. Read the alert's own numbers. They often contradict the theory already. (Locks: none means it isn't lock contention. Note it and drop that branch.)

  2. Time the suspect step directly. Isolate the smallest unit that the alert blames, and run it with a clock:

    time gbrain sync --source source-a --no-embed
    

    Run it on the specific named entity, not the aggregate. A whole-brain run hides which member is slow; --source source-a answers the question. Cross-check state with gbrain sources status (per-source sync lag).

  3. Compare measured vs. budgeted. Grep every timeout in the wrapper, not just the default in the helper signature:

    grep -n "timeoutMs\|timeout:" <the wrapper or cron script>
    

    A generous per-call override makes the helper's default irrelevant. Check the call site before blaming the default.

  4. Check the alert threshold against the authoritative one. Before concluding the system is broken, confirm the alerter and the audit agree on what "bad" means. gbrain doctor's sync-freshness check defaults to 24h warn / 72h fail (env-overridable via GBRAIN_SYNC_FRESHNESS_WARN_HOURS / GBRAIN_SYNC_FRESHNESS_FAIL_HOURS); a cron monitor paging at 12h is speaking below the authoritative warn line. A monitor that acts early is correct; a monitor that speaks at its act-line is a false-positive generator.

  5. Only now design the fix — against the number you measured.

The threshold-mismatch failure mode

A cron monitor legitimately acts earlier than the doctor fails, to keep drift out of FAIL territory. That is good design. The bug is reusing the act-threshold as the alert-threshold: everything between "act" and "warn" becomes a recurring page about a healthy system.

Separate the two constants. Act at the aggressive line, speak at the authoritative one:

const ACT_HOURS = Number(env.MONITOR_ACT_HOURS || 12);      // act early — fine
const ALERT_HOURS = Math.max(ACT_HOURS, DOCTOR_WARN_HOURS); // speak at the audit's line

Symptom to recognize instantly: a repeating alert whose numbers sit below the doctor's own warn line, while the underlying resource looks fine when queried directly.

Red flags that you are theorizing, not diagnosing

  • You have a root cause but no measured duration.
  • Your fix is a rewrite and you have not run the step once.
  • You revise the theory twice without taking a new measurement between revisions.
  • The alert says "no recovery in flight" — verify whether recovery is in fact running before believing it (ps for the worker; check the launch flag; gbrain jobs list for queued work).
  • "Already up to date" in the step output. That step is not your bottleneck.

When it fails

Follow the agent operator protocol for any gbrain error code, exit code, [AGENT] block or notice block. Specific to this skill:

  • gbrain doctor warns or fails: record the exact check and threshold before declaring the system unhealthy; the doctor's 24h/72h sync-freshness thresholds may differ from a cron monitor's.
  • gbrain sources status shows held items or lock_busy: that is the measured cause; fix it and re-measure instead of raising timeouts.

Anti-Patterns

  • Raising a timeout to fix a stall. If the step is genuinely hung, a bigger budget just hangs longer. Measure, then decide between "needs more time" and "is wedged" — they have opposite fixes. (gbrain's sync stall watchdog makes the same distinction natively: it keys on forward progress, not elapsed time.)
  • Rewriting a pipeline on an unmeasured starvation theory. Splitting steps to fix starvation that does not exist adds surface area and fixes nothing.
  • Trusting the alert's causal claim. Alerts report symptoms accurately and causes badly. The staleness number is real; the reason attached to it is a guess.
  • Skillifying or persisting a root cause you have not measured. A confident wrong diagnosis baked into a playbook is worse than the original bug.

Known failure modes handled

  • Triple-wrong diagnosis on a freshness alert. A cron monitor paged repeatedly about two sources (source-a, source-b) reported hours-stale. Three successive root causes were asserted and a wrapper rewrite approved — before any measurement. The measurement: time gbrain sync --source source-a --no-embed finished in single-digit seconds with "Already up to date" (same for source-b), and gbrain sources status showed every source synced that morning. Every theory died at once. Actual cause: the monitor alerted at its act-threshold, hours below gbrain doctor's authoritative warn line. The fix was two lines (ALERT_HOURS = max(ACT_HOURS, WARN_HOURS)), not a rewrite. Lesson: when a page repeats about a system that measures healthy, suspect the thresholds before the system.
  • Contention-theory corollary caught in the same pass: a CPU-contention worry about a deprioritized (nice'd) step was equally unfounded — the step finished in seconds while the host was under sustained concurrent load. Contention theories need the same stopwatch as staleness theories.

Output Format

The output is a measurement verdict (conversation-level; this skill writes no brain pages). Only after the verdict is a fix proposed, sized against the measured number:

## Measurement verdict
- Alert:      <the alert text and which monitor emitted it>
- Claim:      <the temporal claim, e.g. "source-a 14h stale">
- Measured:   <exact command> → <duration> (<key output, e.g. "Already up to date">)
- Budgeted:   <timeout constant + any call-site override, file:line>
- Thresholds: monitor act-line <X>h vs doctor warn-line <Y>h → <match | MISMATCH>
- Verdict:    false page on healthy system | needs more time | wedged | genuine regression
- Fix:        <the change, justified by the measured number — or "none; adjust the alert line">

Dedup (sharp boundaries)

  • GStack investigate — systematic debugging of code bugs ("why is this broken", 500 errors, wrong output). Boundary: investigate root-causes code behavior; this skill is the measure-first gate for temporal ops alerts (stale/timeout/freshness/wedged) that runs before any timeout or threshold is touched. If the stopwatch confirms a genuine slowness or regression, hand off to investigate with the measured number.
  • skills/maintain/SKILL.md — runs brain health checks and repairs (doctor, extraction, dream cycle). Boundary: maintain emits and acts on health output; this skill governs how to respond when one of those checks pages, before budgets or wrappers change.
  • smoke-test (host-side) — binary post-restart health checks with auto-fix. Boundary: smoke-test answers "is it up after a restart"; this skill answers "is this slow/stale claim even true".
  • skills/cron-scheduler/SKILL.md — schedules monitors and jobs. Boundary: cron-scheduler decides when monitors run; this skill supplies the act-line vs alert-line rule their thresholds must encode.
  • skills/conventions/test-before-bulk.md — trial-before-bulk for mutations. Same spirit (evidence before action), different object: that convention gates bulk writes; this skill gates timeout/threshold/pipeline changes.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Check research claims cited in books, articles, or conversations by tracing the original paper, methods, underlying data, and independent replication. Save a sourced assessment explaining whether the claim holds up and what remains uncertain.

中文简介正在准备中。当前显示英语简介。

  • Check a study cited in a book
  • Trace a numerical claim in an article to its source data
  • Verify research evidence before using it in writing
garrytan/gbrain3.1万2026年10月22日 更新

Explore old files and email archives to rediscover personal writing, ideas, and meaningful conversations. Review selected items one at a time, save worthwhile material as organized notes, and track progress within explicitly permitted folders.

中文简介正在准备中。当前显示英语简介。

  • Rediscover old journals and creative writing
  • Find meaningful letters and email conversations
  • Recover past product and business ideas
garrytan/gbrain3.1万2026年10月22日 更新

Restructure raw article text stored in gbrain into summaries, exact quotes, key insights, and links to related pages. Preserve the original text and connect the article's significance to projects and interests already recorded in gbrain.

中文简介正在准备中。当前显示英语简介。

  • Organizing saved articles into readable notes
  • Collecting exact quotes for essays
  • Connecting articles to current projects
garrytan/gbrain3.1万2026年10月22日 更新

ask-user

免费

Present clear choices when a task needs a human decision, then pause execution until the user replies. This reusable interaction pattern supports filing decisions, priority selection, and confirmation before destructive actions, with an option to decline.

中文简介正在准备中。当前显示英语简介。

  • Clarifying requests with several possible meanings
  • Confirming before bulk deletion or overwriting
  • Choosing where to file meeting notes
garrytan/gbrain3.1万2026年10月22日 更新

Collect public posts across a blog, newsletter, or feed archive and save clean Markdown pages for each article. The workflow avoids duplicates, supports resumed runs, repairs empty results, and hands articles to a separate enrichment skill.

中文简介正在准备中。当前显示英语简介。

  • Importing a blog's public article archive
  • Organizing public newsletter posts as knowledge pages
  • Rerunning a feed import without duplicates
garrytan/gbrain3.1万2026年10月22日 更新

Create a detailed chapter-by-chapter reading companion from an EPUB or PDF, connecting the book’s ideas to experiences and words saved in your personal knowledge store. The result highlights parallels without prescribing actions.

中文简介正在准备中。当前显示英语简介。

  • Connect a book’s ideas to personal experiences
  • Review detailed chapter summaries alongside personal reflections
  • Create a parent’s guide for reading a children’s book aloud
garrytan/gbrain3.1万2026年10月22日 更新

查看 garrytan 的全部技能

报告此技能的问题