measure-dont-guess
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.
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Copia esta solicitud en tu Agente. Incluye la página canónica del Skill y el manifiesto.
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.Tu Agente debe seguir mostrando su plan y solicitar cualquier confirmación exigida por la política de seguridad.
Recursos legibles por agentes
Usa estos enlaces cuando un agente de IA, crawler o script necesite contexto limpio en vez de leer la página completa.
Pruébalo
Usando "measure-dont-guess". Illustrative request: Investigate intermittent checkout failures without assuming the database is responsible.
Resultado esperado:
- 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.
Usando "measure-dont-guess". Illustrative request: Determine whether caching improves a slow endpoint.
Resultado esperado:
- 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.
Auditoría de seguridad
Riesgo medioAll 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.
Preocupaciones de seguridad confirmadas (1)
Factores de riesgo
⚙️ Comandos externos (4)
Compartir y citar este informe
Comparte el informe de evaluación versionado, la insignia neutral, la tarjeta insertable y las citas. Skillstore presenta evidencias sin decidir si este Skill es seguro.
Copiar enlace del informe
https://skillstore.io/skills/masseater-measure-dont-guess/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportInsignia Markdown
[](https://skillstore.io/skills/masseater-measure-dont-guess?utm_source=security_passport_badge)Insignia HTML
<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>Tarjeta para insertar
<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>Citas académicas (APA · BibTeX · CFF)
Cita APA
masseater. (2026). measure-dont-guess security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/masseater-measure-dont-guess/audits/1Cita BibTeX
@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"
Puntuación de Skillstore
Por qué esta puntuación Confianza de la evidencia: MedioLo que puedes crear
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.
Prueba estos prompts
Use measure-dont-guess for [question]. List hypotheses, the measurement needed for each, and decision criteria before proposing code changes.
Investigate [bug] using [reproduction and logs]. Review recent changes, propose redacted boundary observations, and define a failing test before one targeted fix.
Evaluate [proposed optimization] in [approved representative environment]. Define baseline spans, repeat measurements, report variation, and check regressions after one change.
Plan validation for [feature] across [layers]. Define failing acceptance tests, redacted observations, and decision criteria. Identify missing access before any production action.
Mejores prácticas
- 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.
Evitar
- 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.
Preguntas frecuentes
What does this skill provide?
Can I use it with Claude, Codex, and Claude Code?
Does it require the referenced TypeScript template?
Must measurements run in production?
What happens when measurements are unavailable?
How should logs and measurement records be handled?
Detalles del desarrollador
Autor
masseaterLicencia
MIT
Revisión de Skillstore
r1
Aviso de versión
El autor no declaró una versión.
Repositorio
https://github.com/masseater/skills/tree/4810c8f1186a229ca57d2f278c0a94f0fc862fec/skills/measure-dont-guessRef.
443c05ac1206b41f40676914eae29431c8e9605b
Actualidad del mantenimiento
24/10/2026
Uso
0 descargas · 0 vistas
Estructura de archivos