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skill-authoring
77Create and Revise Agent Skills
AI-written skills can introduce assumptions that users never approved. This skill guides creation and revision from user decisions, explicit goals, and organized rules.
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Skills measure-dont-guess
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measure-dont-guess
Content revision r1 Medium Risk ⚙️ External commands
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.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
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.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "measure-dont-guess". Illustrative request: Investigate intermittent checkout failures without assuming the database is responsible.
Expected outcome:
- 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.
Using "measure-dont-guess". Illustrative request: Determine whether caching improves a slow endpoint.
Expected outcome:
- 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.
Security Audit
Medium Riskv1 • 10/24/2026 Open versioned report
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
Files scanned
133
Lines analyzed
0
Review items
0
False positives ignored
Confirmed security concerns (1)
Medium
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.
Risk Factors
⚙️ External commands (4)
Audited by: codex
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APA citation
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 citation
@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 Score
Why this score Evidence Confidence: Medium 41
Architecture
85
Maintainability
87
Content
65
Community
91
Spec Compliance
What You Can Build
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.
Try These Prompts
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.
Best Practices
- 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.
Avoid
- 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.
Frequently Asked Questions
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.
Developer Details
Author
masseaterLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
443c05ac1206b41f40676914eae29431c8e9605b
Maintenance freshness
10/24/2026
Usage
0 downloads · 0 views
File structure
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