技能 measure-dont-guess
📦
measure-dont-guess
內容修訂 r1 中風險 ⚙️ 外部命令
Measure Evidence Before Changing Code
Assumptions can lead to ineffective fixes and misleading performance claims. This skill guides assistants through hypotheses, predefined criteria, repeatable measurements, and evidence-based decisions.
支援: Claude Codex Code(CC)
使用我的智慧代理安裝
將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。
智能體請求
Review the Skillstore skill "measure-dont-guess" from https://skillstore.io/skills/masseater-measure-dont-guess.md and its manifest at https://skillstore.io/api/skills/masseater-measure-dont-guess/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。
Agent 可讀資源
當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。
測試它
正在使用「measure-dont-guess」。 Illustrative request: Investigate intermittent checkout failures without assuming the database is responsible.
預期結果:
- Question: At which boundary does the checkout request first become invalid?
- Hypotheses: request validation, service transformation, or database handling.
- Observation plan: Record redacted boundary values and correlation identifiers in an approved environment.
- Decision criterion: Identify the earliest boundary where valid input produces invalid output.
- Status: Root cause remains unconfirmed until reproduction and measurements are available.
正在使用「measure-dont-guess」。 Illustrative request: Determine whether caching improves a slow endpoint.
預期結果:
- Hypothesis: Repeated reads account for most endpoint latency.
- Baseline plan: Measure endpoint and read spans under the same representative workload across repeated runs.
- Decision criterion: Agree on the required latency reduction and acceptable error rate before testing.
- Experiment: Change only caching behavior, repeat measurements, and inspect other operations for regressions.
- Status: No performance improvement is claimed before measurements.
安全審計
中風險v1 • 2026/10/24 開啟版本化報告
All seven static alerts are false positives: Markdown link labels are not shell execution, and the entropy alerts concern readable Japanese documentation. No evidence found of prompt injection, obfuscated payloads, or intentional data exfiltration. A separate confidentiality risk arises from recommending production diagnostics and logging boundary values without redaction guidance.
5
已掃描檔案
133
分析行數
0
複核項目
0
已忽略的誤報
已確認的安全問題 (1)
中
Production Diagnostics Lack Sensitive Data Safeguards
The workflow prioritizes production measurements and instructs logging incoming and outgoing values at layer boundaries. Without redaction, credentials or personal data could enter logs. Recording measurement results in commit messages could further persist sensitive diagnostic content.
Production measurement and boundary-value logging are explicit instructions, and the reviewed workflow provides no redaction guidance. Actual disclosure depends on diagnostic data and execution.
風險因素
⚙️ 外部命令 (4)
審計單位: codex
分享與引用此報告
分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。
複製報告連結
https://skillstore.io/skills/masseater-measure-dont-guess/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown 徽章
[](https://skillstore.io/skills/masseater-measure-dont-guess?utm_source=security_passport_badge)HTML 徽章
<a href="https://skillstore.io/skills/masseater-measure-dont-guess?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/masseater-measure-dont-guess/security.svg" alt="Skillstore security assessment" loading="lazy"></a>嵌入卡片
<iframe src="https://skillstore.io/embed/skills/masseater-measure-dont-guess.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>學術引用 (APA · BibTeX · CFF)
APA 引用
masseater. (2026). measure-dont-guess security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/masseater-measure-dont-guess/audits/1BibTeX 引用
@techreport{masseater-masseater-measure-dont-guess-2026,
author = {masseater},
title = {measure-dont-guess security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/masseater-measure-dont-guess/audits/1},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "measure-dont-guess security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
- name: "masseater"
date-released: "2026-10-24"
url: "https://skillstore.io/skills/masseater-measure-dont-guess/audits/1"
identifiers:
- type: other
value: "skillstore:masseater-measure-dont-guess:audit:1"
description: "Skillstore immutable audit report identifier"
Skillstore 評分
評分依據 證據置信度: 中 41
架構
85
可維護性
87
內容
65
社群
91
規範符合度
你可以打造什麼
Investigate a Reproducible Bug
Trace a failing request across application layers, isolate the source of invalid values, and verify one targeted fix.
Verify a Performance Improvement
Collect a representative baseline, change one measured bottleneck, and compare repeated results while checking for regressions.
Define Feature Acceptance Evidence
Turn feature requirements into failing acceptance checks before implementation, then verify the completed behavior with the same checks.
試試這些提示
Beginner: Define a Measurement
Use measure-dont-guess for [question]. List hypotheses, the measurement needed for each, and decision criteria before proposing code changes.
Intermediate: Find the Root Cause
Investigate [bug] using [reproduction and logs]. Review recent changes, propose redacted boundary observations, and define a failing test before one targeted fix.
Advanced: Compare Performance
Evaluate [proposed optimization] in [approved representative environment]. Define baseline spans, repeat measurements, report variation, and check regressions after one change.
Advanced: Validate Across Layers
Plan validation for [feature] across [layers]. Define failing acceptance tests, redacted observations, and decision criteria. Identify missing access before any production action.
最佳實務
- Write hypotheses and acceptance criteria before collecting results, then keep experiment conditions consistent.
- Use approved representative environments, redact sensitive diagnostics, and sanitize evidence before adding it to commits.
- Change one factor at a time, repeat measurements, and report uncertainty or missing access explicitly.
避免
- Treating an unmeasured explanation as a confirmed root cause.
- Changing acceptance criteria after seeing results or combining unrelated fixes.
- Logging raw production inputs or committing diagnostic results without checking for sensitive data.
常見問題
What does this skill provide?
It provides measurement-first instructions for debugging, performance comparisons, and feature validation. It does not include an executable measurement engine.
Can I use it with Claude, Codex, and Claude Code?
The workflows can guide these assistants. Actual measurements depend on the tools, permissions, and environment available to each assistant.
Does it require the referenced TypeScript template?
No. The general workflow applies elsewhere, but Effect and Cloudflare instructions must be mapped to equivalent tools in your repository.
Must measurements run in production?
The source prioritizes production and otherwise calls for matching staging conditions. Obtain authorization and define diagnostic safeguards before accessing either environment.
What happens when measurements are unavailable?
The workflow directs the assistant to explain what is missing and label unmeasured claims as speculation instead of proceeding on assumptions.
How should logs and measurement records be handled?
Redact secrets and personal data, restrict access, and sanitize results before committing them. These safeguards must supplement the source instructions.
開發者詳細資訊
作者
masseater授權
MIT
Skillstore 修訂
r1
版本提示
作者未聲明版本。
引用
443c05ac1206b41f40676914eae29431c8e9605b
維護新鮮度
2026/10/24
使用量
0 次下載 · 0 次瀏覽
檔案結構