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Descriptive Evidence

Category: Security  ·  Sub-category: forensics  ·  Last updated:
Use when the deliverable is a DESCRIPTION of what's in the data rather than an effect, a counterfactual, or a prediction — stylized facts, raw and indexed trends ("what's the trend in X", "how has Y changed over time", "is X rising", "plot the growth in …"), summary-statistics / Table-1 tables, distributions, descriptive maps / choropleths (where something is concentrated, how a rate varies across places), and the descriptive-evidence section that motivates an empirical paper. The descriptive layer BENEATH the modeling fork: it is often the deliverable itself, and when it isn't, a stylized fact is what motivates the causal/structural/predictive question (then route to the fork). Forces the comparability choices that silently decide what a fact says — denominator, deflator + base year, per-capita scaling, weighting, unit, window, aggregation level — to be fixed before plotting; makes the signature silent failure LOUD — composition / aggregation artifacts (Simpson's paradox, a shifting denominator or sample, nominal-not-real growth, a mix shift) that make a trend look like a within-group change it isn't; demands a stylized fact be ROBUST to alternative reasonable cuts/definitions/windows or it isn't stylized; and holds the causal firewall — describe in descriptive verbs ("rose alongside", "is higher among"), never causal ones ("raised", "drove", "caused"). Use in R, Julia, or Python whenever someone says "what's the trend", "show me the growth", "summary stats", "Table 1", "describe this data", "stylized facts", "what does the distribution look like", "map where X is concentrated", or "give me some motivating facts" — even when it feels like just a quick plot or map, because a mis-deflated, mis-weighted, composition-driven, or count-not-rate "fact" looks exactly as clean as a real one.
Security AStatic scan found no risk patternsHow grading works ›

What this skill does

Descriptive Evidence is a community-contributed Claude Code skill in the forensics sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/descriptive-evidence/ and loads when your prompt matches the skill's trigger.

When to invoke it: Use when the deliverable is a DESCRIPTION of what's in the data rather than an effect, a counterfactual, or a prediction — stylized facts, raw and indexed trends ("what's the trend in X", "how has Y changed over time", "is X rising", "plot the growth in …"), summary-statistics / Table-1 tables, distributions, descriptive maps / choropleths (where something is concentrated, how a rate varies across places), and the descriptive-evidence section that motivates an empirical paper. The descriptive layer BENEATH the modeling fork: it is often the deliverable itself, and when it isn't, a stylized fact is what motivates the causal/structural/predictive question (then route to the fork).

Who uses this skill

The Descriptive Evidence Claude Code skill is built for security engineers, penetration testers, DevSecOps practitioners, and development teams hardening codebases and infrastructure. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 153,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).

How to install

Free

Manual install (2 steps)

mkdir -p ~/.claude/skills/descriptive-evidence
curl -L https://claudskills.com/skills/descriptive-evidence/SKILL.md \
  -o ~/.claude/skills/descriptive-evidence/SKILL.md

Or just download SKILL.md directly and drop it into ~/.claude/skills/descriptive-evidence/. Claude Code auto-discovers it on next session.

Skills live at ~/.claude/skills/descriptive-evidence/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\descriptive-evidence\SKILL.md on Windows. See the full install guide for step-by-step instructions.

Telegram

📱 Install from your phone or desktop Telegram

Open @claudskills_bot on Telegram, tap Open Desktop App, and the desktop app installs this skill for you. Or share the bot link with a colleague — they get the same one-tap install. Learn more →

Pro

One-click install via the desktop app

The ClaudSkills desktop app installs any skill directly into ~/.claude/skills/ with one click — no terminal required. Pro starts at $9/mo or $149 lifetime.

Pro

For the full experience including quality scoring and one-click install features for each skill — upgrade to Pro.

Frequently asked questions

How do I install the Descriptive Evidence Claude Code skill?
Install via the ClaudSkills desktop app (one click) or copy SKILL.md from the source repository to ~/.claude/skills/descriptive-evidence/SKILL.md and restart Claude Code. Both flows are detailed at claudskills.com/install/.
What does the Descriptive Evidence skill do?
Use when the deliverable is a DESCRIPTION of what's in the data rather than an effect, a counterfactual, or a prediction — stylized facts, raw and indexed trends ("what's the trend in X", "how has Y changed over time", "is X rising", "plot the growth in …"), summary-statistics / Table-1 tables, distributions, descriptive maps / choropleths (where something is concentrated, how a rate varies across places), and the descriptive-evidence section that motivates an empirical paper. The descriptive layer BENEATH the modeling fork: it is often the deliverable itself, and when it isn't, a stylized fact is what motivates the causal/structural/predictive question (then route to the fork). Forces the comparability choices that silently decide what a fact says — denominator, deflator + base year, per-capita scaling, weighting, unit, window, aggregation level — to be fixed before plotting; makes the signature silent failure LOUD — composition / aggregation artifacts (Simpson's paradox, a shifting denominator or sample, nominal-not-real growth, a mix shift) that make a trend look like a within-group change it isn't; demands a stylized fact be ROBUST to alternative reasonable cuts/definitions/windows or it isn't stylized; and holds the causal firewall — describe in descriptive verbs ("rose alongside", "is higher among"), never causal ones ("raised", "drove", "caused"). Use in R, Julia, or Python whenever someone says "what's the trend", "show me the growth", "summary stats", "Table 1", "describe this data", "stylized facts", "what does the distribution look like", "map where X is concentrated", or "give me some motivating facts" — even when it feels like just a quick plot or map, because a mis-deflated, mis-weighted, composition-driven, or count-not-rate "fact" looks exactly as clean as a real one.
Is this skill free to install?
Yes. ClaudSkills is an open registry — every skill keeps its source repository's license, and manual install via copy is free. ClaudSkills Pro ($9/mo, $79/yr, or $149 one-time) adds one-click install via the desktop app and a multi-signal Quality Score.
When should I use the Descriptive Evidence skill?
Use Descriptive Evidence when your Claude Code task falls under the Security category — specifically in the forensics area. Claude Code auto-discovers installed skills and invokes the right one based on the task description, so you can also ask Claude directly (e.g. "use Descriptive Evidence" or describe the task and let Claude pick). Browse related skills at /category/security/.
What is a Claude Code skill and how does the Descriptive Evidence skill fit in?
A Claude Code skill is a SKILL.md file that lives under ~/.claude/skills/<name>/ and tells the Claude Code CLI agent how to perform a specific task (instructions, prompts, allowed tools). Skills are auto-discovered at session start. Descriptive Evidence is one of 67,000+ skills indexed in the open ClaudSkills catalog, classified under the Security category. Learn more at /learn/what-is-a-claude-skill/.

Attribution & license

Cite this skill

If you reference this skill in a blog post, paper, or documentation, you can cite it as:

APA
lancegui. (2026). Descriptive Evidence [Claude Code skill]. ClaudSkills. https://claudskills.com/skills/descriptive-evidence/
BibTeX
@misc{descriptive-evidence-2026,
  author    = {lancegui},
  title     = {Descriptive Evidence [Claude Code skill]},
  year      = {2026},
  publisher = {ClaudSkills},
  url       = {https://claudskills.com/skills/descriptive-evidence/}
}

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Security scan

Grade A · scanned 2026-07-18 — free static scan against the OWASP Agentic Skills Top 10.

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Browse all Security skills in the ClaudSkills registry, or explore these other picks from the same category:

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