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SkillFortify, a formal verification for AI agent skills

Hacker News

SkillFortify, a formal verification for AI agent skills

Hi HN, In January 2026, 1,200 malicious skills infiltrated the OpenClaw agent marketplace (ClawHavoc campaign). A month later, researchers catalogued 6,487 malicious agent tools that VirusTotal cannot detect. The first agent-software RCE was assigned CVE-2026-25253. The response: a dozen heuristic scanning tools (pattern matching, LLM-as-judge, YARA rules). They all carry the same caveat: "no findings does not mean no risk." SkillFortify takes a different approach. Instead of checking for known bad patterns, it formally verifies what a skill CAN do against what it CLAIMS to do. Five mathematical theorems guarantee soundness -- if SkillFortify says a skill is safe, it provably cannot exceed its declared capabilities. What it does: - skillfortify scan . -- discover and analyze all skills in a project - skillfortify verify skill.md -- formally verify against capability declaration - skillfortify lock -- generate skill-lock.json for reproducible configs - skillfortify trust skill.md -- compute trust score (provenance + behavior) - skillfortify sbom -- CycloneDX 1.6 Agent Skill Bill of Materials Supports Claude Code skills, MCP servers, and OpenClaw manifests. Evaluated on 540 skills (270 malicious, 270 benign): F1=96.95%, zero false positives. Paper: [ZENODO_DOI_URL] Install: pip install skillfortify Code: https://github.com/varun369/skillfortify Built as part of the AgentAssert research suite. Happy to answer questions about the formal model, threat landscape, or benchmark methodology.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
12%12% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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