Data source: GitHub Trending bot issue happydog-intj/ai-xiaohongshu-daily #581 (GitHub Trending AI morning edition, 2026-10-21, created 2026-10-20 UTC), compared with #578 from the previous day. Ranked by stars gained today. Nine of the top ten were excluded for exceeding 40,000 total stars (ai-job-search, orca, ponytail, ComfyUI, ruflo, graphify, opencode, agency-agents, headroom). Yesterday’s pick, akitaonrails/ai-memory, dropped off the list and was not repeated.
cloudflare/security-audit-skill — +1,680 ⭐ today
URL: https://github.com/cloudflare/security-audit-skill
Total stars: 33,628 · Change vs. previous day: absent from yesterday’s top 8 (below +666) → +1,680 today (at least 2.5x) · Language: JavaScript (validators) / Markdown · License: MIT
A coding-agent skill from Cloudflare that turns agents such as Claude Code or Codex into a multi-phase security auditor. The audit runs in six phases:
- Reconnaissance: maps architecture, trust boundaries and input surfaces into architecture.md and coverage-ledger.json
- Coverage-led hunting: isolated hunter agents work through ledger units while coverage critics flag gaps
- Candidate validation: a fresh verifier that took no part in the hunt tries to disprove each candidate
- Structured output: confirmed, needs_validation and rejected records written to findings.json and validated against a JSON schema
- Independent record verification: new agents re-check the final source claims
- Reporting: auto-generates REPORT.md, FINDINGS-DETAIL.md and NEEDS-VALIDATION.md
The repo ships attack-class playbooks (memory safety, AI/LLM, web auth, supply chain, cloud, resource exhaustion, data isolation and more) plus zero-dependency Node.js validators, and supports multi-run audits that reuse earlier ledgers to revalidate only changed code. Its key differentiator is that the agent that finds an issue never confirms it, which cuts the false positives typical of LLM security reviews. Released in mid-September, it is surging back up the trending list a month later as demand grows for agent-driven review of the rising volume of agent-written code.
Practical use: plug it into a recurring audit pipeline for internal or client-delivered code, and feed the machine-readable findings.json into issue trackers or CI gates.
Tags: #SecurityAudit #AgentSkills #CodingAgents #VulnerabilityScanning #AppSec
Note: Trendshift badge history could not be checked because its cache is stale. Since the repo is only about a month old, it was not treated as a long-running top repo.
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