Agent skill

Sharp Edges

by sickn33 in sickn33/agentic-awesome-skills

“sharp-edges”

— description from SKILL.md by sickn33
MITAuto-check passedSecurity

Install Sharp Edges

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill sharp-edges -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills sharp-edges --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sharp-edges .claude/skills/sharp-edges && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sharp-edges
GitHub stars
47k
Used in
1 other repo
Token cost
~2.8k tokens
SKILL.md length
1,034 words
Files
1
Skills in repo
1,354
Repo updated
First seen
Licence
MIT

At a glance

  • Works in 10 steps: Algorithm/Mode Selection Footguns → Dangerous Defaults → Primitive vs. Semantic APIs → …
  • SKILL.md covers When to Use, When NOT to Use, Core Principle and Rationalizations to Reject, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

About this skill

Sharp Edges is a skill in sickn33/agentic-awesome-skills (47k stars). Its SKILL.md is about 2.8k tokens, and copies of it appear in 1 other owners' repositories. Licence: MIT.

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Algorithm/Mode Selection Footguns
  2. Dangerous Defaults
  3. Primitive vs. Semantic APIs
  4. Configuration Cliffs
  5. Silent Failures
  6. Stringly-Typed Security
  7. Surface Identification
  8. Edge Case Probing
  9. Threat Modeling
  10. Validate Findings

What it can do on your machine

Read from SKILL.md and the folder at commit ec02547. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are php, python, go and yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sharp Edges loads about 2.8k tokens when it runs. Until then it costs about 6 tokens; SKILL.md has 1,034 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~6
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit ec02547, republished under its MIT licence (© sickn33). 1,034 words, ~2,770 tokens.

Download SKILL.mdSave it as .claude/skills/sharp-edges/SKILL.md (or your agent's skills folder).
name
sharp-edges
description
sharp-edges
risk
critical
source
community
date_added
2026-09-04

name: sharp-edges description: "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...

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)

Core Principle

The pit of success: Secure usage should be the path of least resistance. If developers must understand cryptography, read documentation carefully, or remember special rules to avoid vulnerabilities, the API has failed.

Rationalizations to Reject

RationalizationWhy It's WrongRequired Action
"It's documented"Developers don't read docs under deadline pressureMake the secure choice the default or only option
"Advanced users need flexibility"Flexibility creates footguns; most "advanced" usage is copy-pasteProvide safe high-level APIs; hide primitives
"It's the developer's responsibility"Blame-shifting; you designed the footgunRemove the footgun or make it impossible to misuse
"Nobody would actually do that"Developers do everything imaginable under pressureAssume maximum developer confusion
"It's just a configuration option"Config is code; wrong configs ship to productionValidate configs; reject dangerous combinations
"We need backwards compatibility"Insecure defaults can't be grandfather-clausedDeprecate loudly; force migration

Sharp Edge Categories

1. Algorithm/Mode Selection Footguns

APIs that let developers choose algorithms invite choosing wrong ones.

The JWT Pattern (canonical example):

  • Header specifies algorithm: attacker can set "alg": "none" to bypass signatures
  • Algorithm confusion: RSA public key used as HMAC secret when switching RS256→HS256
  • Root cause: Letting untrusted input control security-critical decisions

Detection patterns:

  • Function parameters like algorithm, mode, cipher, hash_type
  • Enums/strings selecting cryptographic primitives
  • Configuration options for security mechanisms

Example - PHP password_hash allowing weak algorithms:

php
// DANGEROUS: allows crc32, md5, sha1
password_hash($password, PASSWORD_DEFAULT); // Good - no choice
hash($algorithm, $password); // BAD: accepts "crc32"
2. Dangerous Defaults

Defaults that are insecure, or zero/empty values that disable security.

The OTP Lifetime Pattern:

python
# What happens when lifetime=0?
def verify_otp(code, lifetime=300):  # 300 seconds default
    if lifetime == 0:
        return True  # OOPS: 0 means "accept all"?
        # Or does it mean "expired immediately"?

Detection patterns:

  • Timeouts/lifetimes that accept 0 (infinite? immediate expiry?)
  • Empty strings that bypass checks
  • Null values that skip validation
  • Boolean defaults that disable security features
  • Negative values with undefined semantics

Questions to ask:

  • What happens with timeout=0? max_attempts=0? key=""?
  • Is the default the most secure option?
  • Can any default value disable security entirely?
3. Primitive vs. Semantic APIs

APIs that expose raw bytes instead of meaningful types invite type confusion.

The Libsodium vs. Halite Pattern:

php
// Libsodium (primitives): bytes are bytes
sodium_crypto_box($message, $nonce, $keypair);
// Easy to: swap nonce/keypair, reuse nonces, use wrong key type

// Halite (semantic): types enforce correct usage
Crypto::seal($message, new EncryptionPublicKey($key));
// Wrong key type = type error, not silent failure

Detection patterns:

  • Functions taking bytes, string, []byte for distinct security concepts
  • Parameters that could be swapped without type errors
  • Same type used for keys, nonces, ciphertexts, signatures

The comparison footgun:

go
// Timing-safe comparison looks identical to unsafe
if hmac == expected { }           // BAD: timing attack
if hmac.Equal(mac, expected) { }  // Good: constant-time
// Same types, different security properties
4. Configuration Cliffs

One wrong setting creates catastrophic failure, with no warning.

Detection patterns:

  • Boolean flags that disable security entirely
  • String configs that aren't validated
  • Combinations of settings that interact dangerously
  • Environment variables that override security settings
  • Constructor parameters with sensible defaults but no validation (callers can override with insecure values)

Examples:

yaml
# One typo = disaster
verify_ssl: fasle  # Typo silently accepted as truthy?

# Magic values
session_timeout: -1  # Does this mean "never expire"?

# Dangerous combinations accepted silently
auth_required: true
bypass_auth_for_health_checks: true
health_check_path: "/"  # Oops
php
// Sensible default doesn't protect against bad callers
public function __construct(
    public string $hashAlgo = 'sha256',  // Good default...
    public int $otpLifetime = 120,       // ...but accepts md5, 0, etc.
) {}

See config-patterns.md for detailed patterns.

5. Silent Failures

Errors that don't surface, or success that masks failure.

Detection patterns:

  • Functions returning booleans instead of throwing on security failures
  • Empty catch blocks around security operations
  • Default values substituted on parse errors
  • Verification functions that "succeed" on malformed input

Examples:

python
# Silent bypass
def verify_signature(sig, data, key):
    if not key:
        return True  # No key = skip verification?!

# Return value ignored
signature.verify(data, sig)  # Throws on failure
crypto.verify(data, sig)     # Returns False on failure
# Developer forgets to check return value
6. Stringly-Typed Security

Security-critical values as plain strings enable injection and confusion.

Detection patterns:

  • SQL/commands built from string concatenation
  • Permissions as comma-separated strings
  • Roles/scopes as arbitrary strings instead of enums
  • URLs constructed by joining strings

The permission accumulation footgun:

python
permissions = "read,write"
permissions += ",admin"  # Too easy to escalate

# vs. type-safe
permissions = {Permission.READ, Permission.WRITE}
permissions.add(Permission.ADMIN)  # At least it's explicit

Analysis Workflow

Phase 1: Surface Identification
  1. Map security-relevant APIs: authentication, authorization, cryptography, session management, input validation
  2. Identify developer choice points: Where can developers select algorithms, configure timeouts, choose modes?
  3. Find configuration schemas: Environment variables, config files, constructor parameters
Show full SKILL.md (406 more words)Show less
Phase 2: Edge Case Probing

For each choice point, ask:

  • Zero/empty/null: What happens with 0, "", null, []?
  • Negative values: What does -1 mean? Infinite? Error?
  • Type confusion: Can different security concepts be swapped?
  • Default values: Is the default secure? Is it documented?
  • Error paths: What happens on invalid input? Silent acceptance?
Phase 3: Threat Modeling

Consider three adversaries:

  1. The Scoundrel: Actively malicious developer or attacker controlling config

    • Can they disable security via configuration?
    • Can they downgrade algorithms?
    • Can they inject malicious values?
  2. The Lazy Developer: Copy-pastes examples, skips documentation

    • Will the first example they find be secure?
    • Is the path of least resistance secure?
    • Do error messages guide toward secure usage?
  3. The Confused Developer: Misunderstands the API

    • Can they swap parameters without type errors?
    • Can they use the wrong key/algorithm/mode by accident?
    • Are failure modes obvious or silent?
Phase 4: Validate Findings

For each identified sharp edge:

  1. Reproduce the misuse: Write minimal code demonstrating the footgun
  2. Verify exploitability: Does the misuse create a real vulnerability?
  3. Check documentation: Is the danger documented? (Documentation doesn't excuse bad design, but affects severity)
  4. Test mitigations: Can the API be used safely with reasonable effort?

If a finding seems questionable, return to Phase 2 and probe more edge cases.

Severity Classification

SeverityCriteriaExamples
CriticalDefault or obvious usage is insecureverify: false default; empty password allowed
HighEasy misconfiguration breaks securityAlgorithm parameter accepts "none"
MediumUnusual but possible misconfigurationNegative timeout has unexpected meaning
LowRequires deliberate misuseObscure parameter combination

References

By category:

  • Cryptographic APIs: See references/crypto-apis.md
  • Configuration Patterns: See references/config-patterns.md
  • Authentication/Session: See references/auth-patterns.md
  • Real-World Case Studies: See references/case-studies.md (OpenSSL, GMP, etc.)

By language (general footguns, not crypto-specific):

LanguageGuide
C/C++references/lang-c.md
Goreferences/lang-go.md
Rustreferences/lang-rust.md
Swiftreferences/lang-swift.md
Javareferences/lang-java.md
Kotlinreferences/lang-kotlin.md
C#references/lang-csharp.md
PHPreferences/lang-php.md
JavaScript/TypeScriptreferences/lang-javascript.md
Pythonreferences/lang-python.md
Rubyreferences/lang-ruby.md

See also references/language-specific.md for a combined quick reference.

Quality Checklist

Before concluding analysis:

  • Probed all zero/empty/null edge cases
  • Verified defaults are secure
  • Checked for algorithm/mode selection footguns
  • Tested type confusion between security concepts
  • Considered all three adversary types
  • Verified error paths don't bypass security
  • Checked configuration validation
  • Constructor params validated (not just defaulted) - see config-patterns.md

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/sharp-edges of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit ec02547

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Sharp Edges compared with similar skills
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Sharp Edges this skillsickn33/agentic-awesome-skills47k1 repos~2.8kAutomated safety check: PassMIT
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Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit4801 repos~3.3kAutomated safety check: PassNone
Security Alert Triageelastic/agent-skills5921 repos~3.5kAutomated safety check: NotesApache-2.0

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Categories

Questions about Sharp Edges

How do I install Sharp Edges in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill sharp-edges -a claude-code`. Or copy the skill folder (skills/sharp-edges in sickn33/agentic-awesome-skills) into .claude/skills/sharp-edges in your project. Claude Code loads it when a task matches its description.

How do I install Sharp Edges in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill sharp-edges -a codex`. Or copy the skill folder (skills/sharp-edges in sickn33/agentic-awesome-skills) into .agents/skills/sharp-edges in your project. Codex loads it when a task matches its description.

Can I use Sharp Edges in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill sharp-edges -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sharp-edges, .gemini/skills/sharp-edges, .github/skills/sharp-edges and .opencode/skills/sharp-edges in your project.

What does Sharp Edges need to run?

SKILL.md names no scripts, command-line tools or credentials: Sharp Edges is instructions for the agent only.

Does Sharp Edges access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Sharp Edges safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sharp Edges use?

Sharp Edges is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sharp Edges use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Sharp Edges?

Skills that share tags, products or a category with Sharp Edges: Fla Ascend Performance (fla-org/flash-linear-attention, 5.8k stars), Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars) and Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 480 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sharp Edges?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,343 GitHub stars. The repository holds 1,354 skills in this directory. The repository was last updated on October 7, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.