Agent skill

Detecting Shadow API Endpoints

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration…

Apache-2.0Auto-check passedBackend & APIs

Install Detecting Shadow API Endpoints

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-shadow-api-endpoints -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-shadow-api-endpoints --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/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-shadow-api-endpoints .claude/skills/detecting-shadow-api-endpoints && 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
detecting-shadow-api-endpoints
GitHub stars
34k
Token cost
~3.6k tokens
SKILL.md length
242 words
Files
4 (incl. scripts, references)
Skills in repo
639
Repo updated
First seen
Licence
Apache-2.0

At a glance

Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration…

  • Works in 3 steps: Traffic Analysis and Comparison → Cloud Configuration Scanning → Source Code Repository Mining
  • Assessing API attack surface
  • SKILL.md covers Overview, When to Use, Prerequisites and Detection Methods, plus 2 more sections
  • Runs Python scripts from its folder; calls aws

What it does

Detecting Shadow API Endpoints is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration scanning, and source code repository mining for undocumented routes. Use when assessing API attack surface, auditing for forgotten test environments or deprecated API versions still running, or building an API registration governance policy.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Backend & APIs, covering REST APIs, OpenAPI specifications and Microservices. It works with OpenAPI and Amazon Web Services. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Assessing API attack surface
  • Auditing for forgotten test environments
  • Deprecated API versions still running
  • Building an API registration governance policy

Example prompts

  • “/detecting-shadow-api-endpoints”

Requirements

  • Python 3

Workflow steps

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

  1. Traffic Analysis and Comparison
  2. Cloud Configuration Scanning
  3. Source Code Repository Mining

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • aws

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

  • Network

    Links to these hosts (documentation or services it may open):

    • apisec.ai
    • wiz.io
    • checkmarx.com
    • treblle.com
    • blog.securelayer7.net

    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

Detecting Shadow API Endpoints loads about 3.6k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 242 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~117
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 242 words, ~3,623 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-shadow-api-endpoints/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-shadow-api-endpoints
description
Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration scanning, and source code repository mining for undocumented routes. Use when assessing API attack surface, auditing for forgotten test environments or deprecated API versions still running, or building an API registration governance policy.
domain
cybersecurity
subdomain
api-security
tags
api-security, shadow-apis, api-discovery, undocumented-apis, zombie-apis, api-inventory, attack-surface-management, api-governance
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
PR.PS-01, ID.RA-01, PR.DS-10, DE.CM-01
mitre_attack
T1190, T1133, T1526, T1213

Detecting Shadow API Endpoints

Overview

Shadow APIs are API endpoints operating within an organization's environment that are not tracked, documented, or secured. They emerge from rapid development cycles, forgotten test environments, deprecated API versions left running, third-party integrations, or developer side projects deployed without governance. Shadow APIs bypass authentication and monitoring controls, creating hidden entry points for attackers. Studies show that up to 30% of API endpoints in large organizations are undocumented, making shadow API detection a critical component of API security posture management.

When to Use

  • When investigating security incidents that require detecting shadow api endpoints
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • API gateway or reverse proxy with traffic logging (Kong, AWS API Gateway, Envoy)
  • Network traffic capture capability (packet broker, port mirroring)
  • Access to source code repositories and CI/CD pipeline configurations
  • Cloud provider access for configuration scanning (AWS, GCP, Azure)
  • API documentation inventory (OpenAPI specs, Swagger docs)
  • Python 3.8+ for custom discovery tooling

Detection Methods

1. Traffic Analysis and Comparison

Compare live API traffic against documented OpenAPI specifications to identify undocumented endpoints:

python
#!/usr/bin/env python3
"""Shadow API Endpoint Detector

Compares observed API traffic patterns against documented
OpenAPI specifications to identify undocumented (shadow) endpoints.
"""

import json
import re
import yaml
import sys
from collections import defaultdict
from datetime import datetime
from typing import Dict, List, Set, Tuple, Optional
from dataclasses import dataclass, field

@dataclass
class DiscoveredEndpoint:
    method: str
    path_pattern: str
    first_seen: str
    last_seen: str
    request_count: int
    source_ips: Set[str] = field(default_factory=set)
    status_codes: Set[int] = field(default_factory=set)
    has_auth_header: bool = False
    documented: bool = False

class ShadowAPIDetector:
    # Common patterns for parameterized path segments
    PARAM_PATTERNS = [
        (re.compile(r'/\d+'), '/{id}'),
        (re.compile(r'/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}'), '/{uuid}'),
        (re.compile(r'/[a-zA-Z0-9]{20,40}'), '/{token}'),
    ]

    def __init__(self):
        self.documented_endpoints: Set[Tuple[str, str]] = set()
        self.discovered_endpoints: Dict[Tuple[str, str], DiscoveredEndpoint] = {}

    def load_openapi_spec(self, spec_path: str):
        """Load documented endpoints from OpenAPI specification."""
        with open(spec_path, 'r') as f:
            if spec_path.endswith('.json'):
                spec = json.load(f)
            else:
                spec = yaml.safe_load(f)

        paths = spec.get('paths', {})
        for path, methods in paths.items():
            # Normalize OpenAPI path parameters
            normalized_path = re.sub(r'\{[^}]+\}', '{id}', path)
            for method in methods:
                if method.upper() in ('GET', 'POST', 'PUT', 'DELETE', 'PATCH', 'HEAD', 'OPTIONS'):
                    self.documented_endpoints.add((method.upper(), normalized_path))

        print(f"Loaded {len(self.documented_endpoints)} documented endpoints from {spec_path}")

    def normalize_path(self, path: str) -> str:
        """Normalize an observed path by replacing dynamic segments with placeholders."""
        # Remove query string
        path = path.split('?')[0]

        for pattern, replacement in self.PARAM_PATTERNS:
            path = pattern.sub(replacement, path)

        return path

    def process_access_log(self, log_file: str, log_format: str = "common"):
        """Process API access logs to discover endpoints."""
        patterns = {
            "common": re.compile(
                r'(?P<ip>[\d.]+)\s+\S+\s+\S+\s+\[(?P<time>[^\]]+)\]\s+'
                r'"(?P<method>\w+)\s+(?P<path>\S+)\s+\S+"\s+(?P<status>\d+)'
            ),
            "json": None  # Handle JSON logs separately
        }

        with open(log_file, 'r') as f:
            for line in f:
                if log_format == "json":
                    try:
                        entry = json.loads(line)
                        method = entry.get('method', entry.get('http_method', ''))
                        path = entry.get('path', entry.get('uri', ''))
                        status = int(entry.get('status', entry.get('status_code', 0)))
                        ip = entry.get('remote_addr', entry.get('client_ip', ''))
                        timestamp = entry.get('timestamp', entry.get('@timestamp', ''))
                        has_auth = bool(entry.get('authorization', entry.get('auth_header', '')))
                    except json.JSONDecodeError:
                        continue
                else:
                    match = patterns[log_format].match(line)
                    if not match:
                        continue
                    method = match.group('method')
                    path = match.group('path')
                    status = int(match.group('status'))
                    ip = match.group('ip')
                    timestamp = match.group('time')
                    has_auth = 'Authorization' in line

                # Only process API paths
                if not path.startswith('/api') and not path.startswith('/v'):
                    continue

                normalized = self.normalize_path(path)
                key = (method.upper(), normalized)

                if key not in self.discovered_endpoints:
                    self.discovered_endpoints[key] = DiscoveredEndpoint(
                        method=method.upper(),
                        path_pattern=normalized,
                        first_seen=timestamp,
                        last_seen=timestamp,
                        request_count=0,
                        documented=(key in self.documented_endpoints)
                    )

                endpoint = self.discovered_endpoints[key]
                endpoint.request_count += 1
                endpoint.last_seen = timestamp
                endpoint.source_ips.add(ip)
                endpoint.status_codes.add(status)
                if has_auth:
                    endpoint.has_auth_header = True

    def identify_shadow_apis(self) -> List[DiscoveredEndpoint]:
        """Identify endpoints that are not in the documented specification."""
        shadows = []
        for key, endpoint in self.discovered_endpoints.items():
            if not endpoint.documented:
                shadows.append(endpoint)

        # Sort by request count descending (most active shadows first)
        shadows.sort(key=lambda e: e.request_count, reverse=True)
        return shadows

    def classify_risk(self, endpoint: DiscoveredEndpoint) -> str:
        """Classify the risk level of a shadow endpoint."""
        risk_score = 0

        # No authentication observed
        if not endpoint.has_auth_header:
            risk_score += 3

        # High traffic volume
        if endpoint.request_count > 1000:
            risk_score += 2
        elif endpoint.request_count > 100:
            risk_score += 1

        # Multiple source IPs (wider exposure)
        if len(endpoint.source_ips) > 10:
            risk_score += 2

        # Successful responses (endpoint is functional)
        if 200 in endpoint.status_codes or 201 in endpoint.status_codes:
            risk_score += 1

        # Write operations are higher risk
        if endpoint.method in ('POST', 'PUT', 'DELETE', 'PATCH'):
            risk_score += 2

        # Sensitive path patterns
        sensitive_patterns = ['admin', 'internal', 'debug', 'test', 'backup',
                            'config', 'health', 'metrics', 'graphql', 'console']
        for pattern in sensitive_patterns:
            if pattern in endpoint.path_pattern.lower():
                risk_score += 3
                break

        if risk_score >= 8:
            return "CRITICAL"
        elif risk_score >= 5:
            return "HIGH"
        elif risk_score >= 3:
            return "MEDIUM"
        return "LOW"

    def generate_report(self) -> dict:
        """Generate a comprehensive shadow API discovery report."""
        shadows = self.identify_shadow_apis()
        total_documented = len(self.documented_endpoints)
        total_discovered = len(self.discovered_endpoints)

        report = {
            "scan_date": datetime.now().isoformat(),
            "summary": {
                "documented_endpoints": total_documented,
                "total_discovered_endpoints": total_discovered,
                "shadow_endpoints": len(shadows),
                "shadow_ratio": f"{len(shadows)/max(total_discovered,1)*100:.1f}%",
            },
            "shadow_endpoints": []
        }

        for endpoint in shadows:
            risk = self.classify_risk(endpoint)
            report["shadow_endpoints"].append({
                "method": endpoint.method,
                "path": endpoint.path_pattern,
                "risk_level": risk,
                "request_count": endpoint.request_count,
                "unique_sources": len(endpoint.source_ips),
                "authenticated": endpoint.has_auth_header,
                "status_codes": sorted(endpoint.status_codes),
                "first_seen": endpoint.first_seen,
                "last_seen": endpoint.last_seen,
            })

        return report


def main():
    detector = ShadowAPIDetector()

    # Load documented API specifications
    spec_files = sys.argv[1:] if len(sys.argv) > 1 else ["openapi.yaml"]
    for spec in spec_files:
        if spec.endswith(('.yaml', '.yml', '.json')):
            detector.load_openapi_spec(spec)

    # Process access logs
    detector.process_access_log("/var/log/api/access.log")

    report = detector.generate_report()

    print(f"\n{'='*60}")
    print(f"SHADOW API DISCOVERY REPORT")
    print(f"{'='*60}")
    print(f"Documented: {report['summary']['documented_endpoints']}")
    print(f"Discovered: {report['summary']['total_discovered_endpoints']}")
    print(f"Shadow: {report['summary']['shadow_endpoints']} ({report['summary']['shadow_ratio']})")
    print()

    for ep in report["shadow_endpoints"]:
        risk_marker = {"CRITICAL": "[!!!]", "HIGH": "[!!]", "MEDIUM": "[!]", "LOW": "[.]"}
        print(f"  {risk_marker.get(ep['risk_level'], '[?]')} {ep['method']} {ep['path']}")
        print(f"      Risk: {ep['risk_level']} | Requests: {ep['request_count']} | Auth: {ep['authenticated']}")

    # Save full report
    with open("shadow_api_report.json", "w") as f:
        json.dump(report, f, indent=2, default=str)
    print(f"\nFull report saved to shadow_api_report.json")


if __name__ == "__main__":
    main()
2. Cloud Configuration Scanning
bash
# AWS: Discover API Gateway endpoints not in documentation
aws apigateway get-rest-apis --query 'items[*].[name,id]' --output table

# List all routes for each API
aws apigatewayv2 get-apis --query 'Items[*].[Name,ApiId,ProtocolType]' --output table

# AWS Lambda function URLs (potential shadow APIs)
aws lambda list-function-url-configs --function-name "*" 2>/dev/null

# Find ALB listener rules routing to undocumented backends
aws elbv2 describe-rules --listener-arn $LISTENER_ARN \
  --query 'Rules[*].[Priority,Conditions[0].Values[0],Actions[0].TargetGroupArn]'
3. Source Code Repository Mining
bash
# Search for undocumented route definitions in source code
# Express.js routes
grep -rn "app\.\(get\|post\|put\|delete\|patch\)" --include="*.js" --include="*.ts" src/

# Flask/Django routes
grep -rn "@app\.route\|@api\.route\|path(" --include="*.py" src/

# Spring Boot endpoints
grep -rn "@\(Get\|Post\|Put\|Delete\|Patch\)Mapping\|@RequestMapping" --include="*.java" src/

# Compare found routes against OpenAPI specification
diff <(grep -roh "'/api/[^']*'" src/ | sort -u) \
     <(yq '.paths | keys[]' openapi.yaml | sort -u)

Prevention and Governance

API Registration Gateway Policy
yaml
# Kong plugin configuration - reject unregistered routes
plugins:
  - name: request-validator
    config:
      allowed_content_types:
        - application/json
      body_schema: null
  - name: pre-function
    config:
      access:
        - |
          -- Block requests to unregistered endpoints
          local registered = kong.cache:get("registered_endpoints")
          local path = kong.request.get_path()
          local method = kong.request.get_method()
          local key = method .. ":" .. path
          if not registered[key] then
            kong.log.warn("Shadow API access attempt: ", key)
            return kong.response.exit(404, {error = "Endpoint not registered"})
          end

References

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/detecting-shadow-api-endpoints of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Detecting Shadow API Endpoints next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Detecting Shadow API Endpoints compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detecting Shadow API Endpoints this skillmukul975/Anthropic-Cybersecurity-Skills34k—~3.6kAutomated safety check: PassApache-2.0
API DesignerJeffallan/claude-skills12k2 repos~2kAutomated safety check: PassMIT
OpenAPI to MCP Servermcp-use/mcp-use11k—~5.2kAutomated safety check: PassApache-2.0
Use Yaakmountain-loop/yaak19k—~1.9kAutomated safety check: PassMIT
Old Coder API DesignAmazingAng/old-coder7491 repos~3.4kAutomated safety check: PassMIT
API CallerNVIDIA/SkillEvaluator5441 repos~1.1kAutomated safety check: PassApache-2.0

Similar skills

  • API Designer

    Jeffallan/claude-skills

    Designs REST and GraphQL APIs from resource modeling to an OpenAPI 3.1 contract, with versioning, pagination and RFC 7807 error handling.

    12k GitHub starsUsed in 2 repos~2k tokens
    Backend & APIsAuto-check passed
  • OpenAPI to MCP Server

    mcp-use/mcp-use

    Turns an OpenAPI or Swagger spec into an MCP server with the mcp-use TypeScript SDK, mapping each operation to a tool, wiring auth, testing and deploying.

    11k GitHub stars~5.2k tokensUpdated today
    Backend & APIsAuto-check passed
  • Use Yaak

    mountain-loop/yaak

    A skill your agent uses when the user mentions Yaak, a Yaak workspace, or the yaak command, or asks to call, hit, or smoke test HTTP/REST endpoints, save or organize API requests for reuse or manual…

    19k GitHub stars~1.9k tokensUpdated today
    Backend & APIsAuto-check passed
  • Old Coder API Design

    AmazingAng/old-coder

    Reviews or designs an HTTP/JSON API's endpoints, auth, pagination, versioning and deprecations, guarding against inventing a bespoke interface or silently breaking consumers.

    749 GitHub starsUsed in 1 repo~3.4k tokens
    Backend & APIsAuto-check passed
  • API Caller

    NVIDIA/SkillEvaluator

    Official

    Call any REST API dynamically. An agent skill from NVIDIA/SkillEvaluator.

    544 GitHub starsUsed in 1 repo~1.1k tokens
    Backend & APIsAuto-check passed
  • API Gateway

    itsmostafa/aws-agent-skills

    AWS API Gateway for REST and HTTP API management. An agent skill from itsmostafa/aws-agent-skills.

    1.2k GitHub starsUsed in 1 repo~2.2k tokens
    Backend & APIsAuto-check passed

More from mukul975/Anthropic-Cybersecurity-Skills

All 639 skills in this repo
  • Campaign Attribution Evidence Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.

    34k GitHub stars~2.3k tokensUpdated 1 mo ago
    Auto-check passed
  • Go Malware Analysis in Ghidra

    mukul975/Anthropic-Cybersecurity-Skills

    Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • LNK and Jump List Forensics

    mukul975/Anthropic-Cybersecurity-Skills

    Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • Malware Persistence Analysis with Autoruns

    mukul975/Anthropic-Cybersecurity-Skills

    Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.

    34k GitHub stars~1.2k tokensUpdated 1 mo ago
    Auto-check passed
  • NTFS MFT Deleted File Recovery

    mukul975/Anthropic-Cybersecurity-Skills

    Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.

    34k GitHub stars~2.7k tokensUpdated 1 mo ago
    Auto-check passed
  • Network Covert Channel Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.

    34k GitHub stars~2k tokensUpdated 1 mo ago
    Auto-check passed

Categories

Questions about Detecting Shadow API Endpoints

What does Detecting Shadow API Endpoints do?

Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration…. Detecting Shadow API Endpoints is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Discover and inventory shadow API endpoints that operate outside documented OpenAPI/Swagger specs, using traffic analysis against API gateways (Kong, AWS API Gateway, Envoy), cloud configuration scanning, and source code repository mining for undocumented routes.

When should I use Detecting Shadow API Endpoints?

Detecting Shadow API Endpoints fits situations like: assessing API attack surface; auditing for forgotten test environments; deprecated API versions still running; building an API registration governance policy.

How do I install Detecting Shadow API Endpoints in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-shadow-api-endpoints -a claude-code`. Or copy the skill folder (skills/detecting-shadow-api-endpoints in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-shadow-api-endpoints in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Shadow API Endpoints in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-shadow-api-endpoints -a codex`. Or copy the skill folder (skills/detecting-shadow-api-endpoints in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-shadow-api-endpoints in your project. Codex loads it when a task matches its description.

Can I use Detecting Shadow API Endpoints 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 mukul975/Anthropic-Cybersecurity-Skills --skill detecting-shadow-api-endpoints -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-shadow-api-endpoints, .gemini/skills/detecting-shadow-api-endpoints, .github/skills/detecting-shadow-api-endpoints and .opencode/skills/detecting-shadow-api-endpoints in your project.

What does Detecting Shadow API Endpoints need to run?

Going by SKILL.md and its folder, Detecting Shadow API Endpoints needs Python for the scripts in its folder and the command-line tools its instructions call (aws). Our summary lists: Python 3.

Does Detecting Shadow API Endpoints access the network?

SKILL.md names 5 domains. As links in the text: apisec.ai, wiz.io, checkmarx.com, treblle.com and blog.securelayer7.net. This is read from the text; nothing was executed.

Is Detecting Shadow API Endpoints 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Shadow API Endpoints use?

Detecting Shadow API Endpoints is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Shadow API Endpoints use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 553 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Shadow API Endpoints?

Skills that share tags, products or a category with Detecting Shadow API Endpoints: API Designer (Jeffallan/claude-skills, 12k stars), OpenAPI to MCP Server (mcp-use/mcp-use, 11k stars), Use Yaak (mountain-loop/yaak, 19k stars) and Old Coder API Design (AmazingAng/old-coder, 749 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Shadow API Endpoints?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,870 GitHub stars. The repository holds 639 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.