Agent skill · Security

sharp-edges

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes. Use when reviewing API designs, configuration schemas, cryptographic library ergonomics, or evaluating whether code follows 'secure by default' and 'pit of success' principles. Triggers: footgun, misuse-resistant, secure defaults, API usability, dangerous configuration.

trailofbitsgithub.com/trailofbitsGitHub ↗
claude-coderead-onlyCC-BY-SA-4.0
Install
npx skills add trailofbits/skills --skill sharp-edges --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 19
SKILL.md size: 12 KB
Bundled scripts: none
Allowed tools: ReadGrepGlob
Path: plugins/sharp-edges/skills/sharp-edges/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 6,426
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Sharp Edges Analysis Evaluates whether APIs, configurations, and interfaces are resistant to developer misuse. Identifies designs where the "easy path" leads to insecurity. ## When to Use - Reviewing API or library design decisions - Auditing configuration schemas for dangerous options - Evaluating cryptographic API ergonomics - Assessing authentication/authorization interfaces - Reviewing any code that exposes security-relevant choices to developers ## When NOT to Use - Implementation bugs (use standard code review) - Business logic flaws (use domain-specific analysis) - Performance optimization (different concern) ## Agent The `sharp-edges-analyzer` agent runs the full sharp edges analysis workflow autonomously. Use it when you want a dedicated analysis of APIs, configurations, or interfaces for misuse resistance and footgun potential. The agent follows the four-phase workflow (Surface Identification, Edge Case Probing, Threat Modeling, Validate Findings) and reads language-specific references on demand. ## Core Principle **The pit of success**: Secure usage should be the path of least resistance. If developers must understand cryptography, read documentation carefully, or reme

What's inside
Steps it walks through
  1. When to Use
  2. When NOT to Use
  3. Agent
  4. Core Principle
  5. Rationalizations to Reject
  6. Sharp Edge Categories
  7. 1. Algorithm/Mode Selection Footguns
  8. 2. Dangerous Defaults
  9. 3. Primitive vs. Semantic APIs
  10. 4. Configuration Cliffs
  11. 5. Silent Failures
  12. 6. Stringly-Typed Security
  13. Analysis Workflow
  14. Phase 1: Surface Identification
Ships with 18 files
  • agents/openai.yaml
  • assets/trail-of-bits-mark.svg
  • references/auth-patterns.md
  • references/case-studies.md
  • references/config-patterns.md
  • references/crypto-apis.md
  • references/lang-c.md
  • references/lang-csharp.md
  • references/lang-go.md
  • references/lang-java.md
  • references/lang-javascript.md
  • references/lang-kotlin.md
  • references/lang-php.md
  • references/lang-python.md
  • references/lang-ruby.md
  • references/lang-rust.md
  • references/lang-swift.md
  • references/language-specific.md
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About this skill
What does the sharp-edges skill do?

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes. Use when reviewing API designs, configuration schemas, cryptographic library ergonomics, or evaluating whether code follows 'secure by default' and 'pit of success' principles. Triggers: footgun, misuse-resistant, secure defaults, API usability, dangerous configuration.

How do I install it?

Run `npx skills add trailofbits/skills --skill sharp-edges --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From trailofbits/skills, a repository with 6,426 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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