القائمة
Estimate software performance, capacity, latency, throughput, and cloud cost from first-principles rough numbers. Use when the user says something is too slow, asks whether a design will scale, gives stack numbers, or wants early tradeoffs before benchmarking.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Create a researched, durable, approval-gated goal contract whose verification tools, repository-vault authentication, exact proof steps, rollout safety, and evidence lifecycle are ready before execution. Use when the user asks to set, create, refine, save, or verify a goal; asks whether an agent has everything needed to prove completion; wants a proof-driven alternative to a raw `/goal`; or explicitly invokes `$verify-goal`.
Relentlessly interview the user in dependency-aware batches to sharpen a plan, decision, design, idea, codebase change, or bug fix. Use when the user wants to get grilled, investigate a bug from a ticket or screenshots, pass unfinished grilling to a teammate, or pick it up from a PR. Begin each session by choosing native dialogs or one full text batch, then Sideshow or experimental Grill Visuals for live diagrams. In repositories, inspect current behavior, run a mandatory gap and blast-radius pass, and maintain durable decision docs as answers settle.
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked via Task after a parent gathers diff and file contents. Loads the rubric from the `thermo-nuclear-code-quality-review` skill in the cursor-team-kit plugin.
Validate your code changes through the no-mistakes pipeline - automated code review, tests, lint, docs, push, PR, and CI - before they reach upstream. Use when the user asks to run no-mistakes, gate or ship or validate their changes, push safely, asks you to do a task and then validate it, or invokes /no-mistakes.
Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
| name | napkin-math |
| description | Estimate software performance, capacity, latency, throughput, and cloud cost from first-principles rough numbers. Use when the user says something is too slow, asks whether a design will scale, gives stack numbers, or wants early tradeoffs before benchmarking. |
Use rough orders of magnitude to find the limiting resource before coding. Bias to useful bounds, not faux precision.
Name the path - turn the request into one concrete operation: request, job, sync, import, query, deploy, render, or checkout. Done when the hot path is a short ordered list.
Attach units - write input assumptions in visible units: RPS, rows, bytes, objects, users, fanout, regions, retries, retention, concurrency. Done when every variable has a unit and a rough value.
Load current numbers - when any estimate depends on hardware, network, storage, serialization, compression, or cloud cost, read sirupsen/napkin-math, especially its README tables, unless repo-local production measurements are more relevant.
Done when the numbers used are either copied from the repo-local system or traced to the napkin-math reference.
Budget each step - estimate latency, throughput, and cost for each step using the numbers below, napkin-math, or repo-local measurements. Done when each step has an optimistic and pessimistic bound.
Find the wall - compare required work against the slowest step or tightest quota. Done when one bottleneck is named with math, not vibes.
Trade one constraint - propose 2-4 changes that move the wall: batch, cache, parallelize, colocate, compress, denormalize, drop durability, precompute, or reduce fanout. Done when each tradeoff says what improves, what worsens, and what must be measured next.
Round these hard. Prefer powers of ten when unsure.
| Operation | Rough budget |
|---|---|
| CPU cache / sequential memory | ns to tens of ns |
| System call | hundreds of ns |
| SSD sequential read | ~GB/s |
| SSD random read | ~100 us |
| Context switch | ~10 us |
| Same-zone network | ~100 us, many GB/s |
| Same-region network | few hundred us |
| Cross-region network | tens to hundreds ms |
| Redis/MySQL/cache query | ~0.5 ms before app logic |
| Blob GET/PUT first byte | tens to hundreds ms |
| JSON-ish serialization | ~100 MB/s |
| Efficient binary serialization | ~1 GB/s |
| Compression/decompression | hundreds MB/s to ~1 GB/s |
| Internet egress | expensive enough to check |
| Logs/traces | often costlier than expected |
These are fallback memory anchors. For real estimates, use provider docs, production traces, or sirupsen/napkin-math when a row matters.
Return:
## Assumptions
- ...
## Back-of-envelope
| Step | Work | Budget | Result |
| --- | --- | --- | --- |
## Bottleneck
...
## Tradeoffs
- ...
## Measure Next
- ...