Deepsec Documentation Guide
vercel-labs/deepsec
Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.
Agent skill
by datadrivenconstruction in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Security review checklist for construction software systems.
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-construction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .claude/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .claude/skills/security-review-construction && rm -rf skills-srcUse ~/.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/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .claude/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-constructionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-construction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .agents/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .agents/skills/security-review-construction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .agents/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-construction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .cursor/skills/security-review-construction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .cursor/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git --path 4_DDC_Curated/Quality-Assurance/security-review-construction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-construction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .gemini/skills/security-review-construction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .gemini/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-constructionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .github/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .github/skills/security-review-construction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .github/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction security-review-construction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/4_DDC_Curated/Quality-Assurance/security-review-construction .opencode/skills/security-review-construction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "security-review-construction" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/4_DDC_Curated/Quality-Assurance/security-review-construction into .opencode/skills/security-review-construction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "security-review-construction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
security-review-constructionSecurity review checklist for construction software systems.
Security Review Construction is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Security review checklist for construction software systems. Use when building integrations, APIs, data pipelines, or dashboards for construction projects.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `claw.json` and `instructions.md`).
It sits in Security, covering Security review and Data pipelines and ETL. The repository describes itself as: 221 AI skills for construction: BIM analysis, cost estimation, scheduling, document control, and automation with Claude Code. The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit ce45bbf. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
owasp.orgnist.govcisecurity.orgiso.orgFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
ENCRYPTION_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Security Review Construction loads about 3.3k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 431 words of instructions outside code blocks.
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.
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.
The full file from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction at commit ce45bbf, republished under its MIT licence (© datadrivenconstruction). 431 words, ~3,294 tokens.
.claude/skills/security-review-construction/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.This skill ensures all construction software systems follow security best practices, protecting sensitive project data, financial information, and business intelligence.
# CRITICAL: Construction financial data security
# ❌ NEVER Do This
project_budget = 15000000 # Hardcoded in source
margin_percentage = 0.18 # Business-sensitive info in code
# ✅ ALWAYS Do This
import os
from cryptography.fernet import Fernet
# Load from secure configuration
project_config = load_secure_config(os.environ['PROJECT_CONFIG_PATH'])
# Encrypt sensitive data at rest
def encrypt_financial_data(data: dict) -> bytes:
key = os.environ.get('ENCRYPTION_KEY')
f = Fernet(key)
return f.encrypt(json.dumps(data).encode())# BIM data often contains proprietary design information
# ❌ NEVER store BIM directly in public cloud without encryption
s3.upload_file('model.ifc', bucket='public-bucket')
# ✅ ALWAYS encrypt and control access
def upload_bim_secure(file_path: str, project_id: str):
# Encrypt file
encrypted_path = encrypt_file(file_path)
# Generate pre-signed URL with expiration
presigned_url = s3.generate_presigned_url(
'get_object',
Params={
'Bucket': 'secure-bim-bucket',
'Key': f'{project_id}/{os.path.basename(file_path)}'
},
ExpiresIn=3600 # 1 hour expiration
)
# Log access
audit_log.info(f"BIM access granted: {project_id}")
return presigned_url# Subcontractor data includes business-sensitive information
class SubcontractorDataHandler:
"""Secure handling of subcontractor data"""
# Fields that require encryption
SENSITIVE_FIELDS = [
'insurance_policy_number',
'bank_account',
'tax_id',
'bonding_capacity',
'historical_pricing'
]
def store_subcontractor(self, data: dict) -> str:
# Encrypt sensitive fields
for field in self.SENSITIVE_FIELDS:
if field in data:
data[field] = self.encrypt(data[field])
# Store with audit trail
sub_id = self.db.insert(data)
self.audit.log(f"Subcontractor created: {sub_id}")
return sub_id
def get_subcontractor(self, sub_id: str, requester_id: str) -> dict:
# Check authorization
if not self.can_access(requester_id, sub_id):
raise PermissionError("Unauthorized access to subcontractor data")
# Log access
self.audit.log(f"Subcontractor accessed: {sub_id} by {requester_id}")
# Return with decrypted sensitive fields (only to authorized users)
return self.decrypt_sensitive_fields(self.db.get(sub_id))# Mobile/field data collection must be secure
from datetime import datetime, timedelta
import hashlib
class FieldDataCollector:
"""Secure field data collection"""
def validate_photo_submission(self, photo_data: dict) -> bool:
# Verify GPS timestamp is recent (within 24 hours)
photo_time = datetime.fromisoformat(photo_data['timestamp'])
if datetime.now() - photo_time > timedelta(hours=24):
raise ValueError("Photo timestamp too old - possible replay attack")
# Verify file hash matches
file_hash = hashlib.sha256(photo_data['content']).hexdigest()
if file_hash != photo_data['declared_hash']:
raise ValueError("File integrity check failed")
# Validate GPS coordinates are within project boundary
if not self.is_within_project_bounds(
photo_data['lat'],
photo_data['lon'],
photo_data['project_id']
):
self.audit.warn(f"Photo from outside project bounds: {photo_data}")
return True
def submit_daily_report(self, report: dict, user_id: str) -> str:
# Verify user is assigned to project
if not self.is_assigned_to_project(user_id, report['project_id']):
raise PermissionError("User not assigned to this project")
# Sign report with user credentials
report['signature'] = self.sign_report(report, user_id)
report['submitted_at'] = datetime.now().isoformat()
return self.db.insert(report)# CWICR contains proprietary cost data
class CWICRAccessControl:
"""Access control for CWICR database"""
TIERS = {
'basic': ['public_rates', 'standard_descriptions'],
'professional': ['regional_rates', 'productivity_factors'],
'enterprise': ['custom_rates', 'historical_data', 'analytics']
}
def search(self, query: str, user_id: str) -> list:
# Get user tier
tier = self.get_user_tier(user_id)
# Limit results based on tier
allowed_fields = self.TIERS[tier]
# Execute search with field restrictions
results = self.vector_search(
query=query,
fields=allowed_fields,
limit=self.get_tier_limit(tier)
)
# Log search for analytics
self.audit.log(f"CWICR search: {user_id}, query='{query[:50]}...'")
return results
def export_data(self, user_id: str, format: str) -> bytes:
# Enterprise only
if self.get_user_tier(user_id) != 'enterprise':
raise PermissionError("Export requires enterprise tier")
# Watermark exported data
data = self.get_exportable_data(user_id)
watermarked = self.add_watermark(data, user_id)
return watermarked# Secure OAuth integration with construction platforms
class ConstructionPlatformIntegration:
"""Secure integration with external platforms"""
def __init__(self, platform: str):
self.platform = platform
# Load credentials from secure vault
self.credentials = self.vault.get(f'{platform}_oauth')
def authenticate(self) -> str:
# Use OAuth 2.0 with PKCE
code_verifier = secrets.token_urlsafe(32)
code_challenge = base64.urlsafe_b64encode(
hashlib.sha256(code_verifier.encode()).digest()
).decode().rstrip('=')
# Never store tokens in code or logs
token = self.oauth_flow(code_verifier, code_challenge)
# Store token securely
self.secure_token_store.set(
key=f'{self.platform}_token',
value=token,
ttl=token['expires_in']
)
return token
def sync_data(self, project_id: str) -> dict:
# Validate project access before sync
if not self.has_project_access(project_id):
raise PermissionError(f"No access to project {project_id}")
# Rate limit syncs
self.rate_limiter.check(f'sync_{self.platform}')
# Sync with retry and error handling
try:
data = self.api_client.get_project_data(project_id)
self.validate_incoming_data(data)
return data
except APIError as e:
# Log error without sensitive details
self.logger.error(f"Sync failed for {project_id}: {type(e).__name__}")
raise# Construction documents often contain confidential information
class SecureDocumentManager:
"""Secure document handling for construction"""
# Document classification levels
CLASSIFICATIONS = {
'public': [],
'internal': ['daily_reports', 'schedules'],
'confidential': ['contracts', 'bids', 'financials'],
'restricted': ['legal', 'hr', 'insurance']
}
def upload_document(self, file: bytes, metadata: dict, user_id: str) -> str:
# Scan for malware
if not self.malware_scan(file):
raise SecurityError("Malware detected in uploaded file")
# Classify document
classification = self.classify_document(metadata)
# Check user can upload to this classification
if not self.can_upload(user_id, classification):
raise PermissionError(f"Cannot upload {classification} documents")
# Encrypt based on classification
if classification in ['confidential', 'restricted']:
file = self.encrypt(file)
# Store with audit trail
doc_id = self.storage.put(file, metadata)
self.audit.log(f"Document uploaded: {doc_id} by {user_id}")
return doc_id
def download_document(self, doc_id: str, user_id: str) -> bytes:
# Check access
doc = self.storage.get_metadata(doc_id)
if not self.can_access(user_id, doc['classification']):
raise PermissionError("Access denied")
# Log download
self.audit.log(f"Document downloaded: {doc_id} by {user_id}")
# Return decrypted content
return self.decrypt(self.storage.get(doc_id))Remember: Construction data includes financial, legal, and competitive information. A breach can result in lost bids, legal liability, and reputational damage. Security is not optional.
© datadrivenconstruction, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in 4_DDC_Curated/Quality-Assurance/security-review-construction of datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.
Open the folder on GitHubat commit ce45bbf
Security Review Construction 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Security Review Construction this skilldatadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction | 345 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Deepsec Documentation Guidevercel-labs/deepsec | 8.1k | — | ~956 | Automated safety check: Pass | Apache-2.0 | |
| Kubernetes Network Security Auditkubeshark/kubeshark | 12k | — | ~7.3k | Automated safety check: Notes | Apache-2.0 | |
| Native Dependency Updatemono/SkiaSharp | 5.6k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Semgrep Security Scantrailofbits/skills | 7.4k | — | ~3.7k | Automated safety check: Notes | CC-BY-SA-4.0 | |
| Skillward AuditFangcun-AI/SkillWard | 143 | — | ~2.9k | Automated safety check: Pass | Custom licence |
vercel-labs/deepsec
Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.
kubeshark/kubeshark
Hunts for compromised workloads and malicious traffic in a Kubernetes cluster by sweeping network data through Kubeshark MCP, mapped to MITRE ATT&CK.
mono/SkiaSharp
Update native dependencies (libpng, libexpat, zlib, libwebp, harfbuzz, freetype, libjpeg-turbo, etc.) in SkiaSharp's Skia fork.
trailofbits/skills
Detects languages, proposes rulesets for approval, then runs the approved Semgrep scan across a codebase and merges the output into one SARIF file.
Fangcun-AI/SkillWard
Security-audit a third-party skill bundle (folder with SKILL.md, or .zip / .tar.gz archive) before installing it, using the SkillWard cloud scanner.
TheDecipherist/claude-code-mastery
Checks a codebase for hardcoded secrets, vulnerable dependencies, weak input handling, weak authentication and unsafe transport settings before deployment or merge.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Estimate embodied carbon and produce ESG/climate reporting for construction: LCA per work item, material-based carbon factors, EU taxonomy and CSRD alignment.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Generative design for construction: text-to-BIM concepts, option generation, and AI-assisted design iteration with cost and carbon feedback.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Material passports and circular construction: generate per-element material inventories from BOQ/BIM, mark reuse potential and recycled content, and prepare deconstruction data.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Automated pipeline for retraining ML models with new construction data.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Browse and search the OpenConstructionERP cost database: classification tree, SQL and semantic search, autocomplete, certainty badges, and the resource catalog.
Categories
Security review checklist for construction software systems. Security Review Construction is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Security review checklist for construction software systems.
Security Review Construction fits situations like: building integrations; dashboards for construction projects.
Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a claude-code`. Or copy the skill folder (4_DDC_Curated/Quality-Assurance/security-review-construction in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .claude/skills/security-review-construction in your project. Claude Code loads it when a task matches its description.
Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a codex`. Or copy the skill folder (4_DDC_Curated/Quality-Assurance/security-review-construction in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .agents/skills/security-review-construction in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill security-review-construction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/security-review-construction, .gemini/skills/security-review-construction, .github/skills/security-review-construction and .opencode/skills/security-review-construction in your project.
Going by SKILL.md and its folder, Security Review Construction needs credentials named ENCRYPTION_KEY. Our summary lists: Python 3; A credential in ENCRYPTION_KEY.
SKILL.md names 4 domains. As links in the text: owasp.org, nist.gov, cisecurity.org and iso.org. This is read from the text; nothing was executed.
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.
Security Review Construction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Security Review Construction: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), Native Dependency Update (mono/SkiaSharp, 5.6k stars) and Semgrep Security Scan (trailofbits/skills, 7.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
datadrivenconstruction (a GitHub user) maintains it in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction, which has 345 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on August 22, 2026.
Source: datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.