agent-runtime-validati…
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
Assesses network protocol exploitation candidates while separating observed service behavior, advisory applicability, reproduced faults, demonstrated impact, authorization, disruption risk, telemetry, and coverage. Use when a banner, scanner result, packet trace, reset, legacy
Claude Code
$ npx -y skills add zero-lab-ai/skills --skill protocol-exploitation-assessment --agent claude-code
Codex
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a codex -y
Cursor
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a cursor -y
Gemini CLI
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a gemini-cli -y
GitHub Copilot
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a github-copilot -y
OpenCode
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a opencode -y
Windsurf
$ npx skills add zero-lab-ai/skills --skill protocol-exploitation-assessment -a windsurf -y
name: protocol-exploitation-assessment description: "Assesses network protocol exploitation candidates while separating observed service behavior, advisory applicability, reproduced faults, demonstrated impact, authorization, disruption risk, telemetry, and coverage. Use when a banner, scanner result, packet trace, reset, legacy protocol, or suspected handshake weakness needs a defensible validation plan." allowed-tools: Read Grep Glob
A banner can identify a candidate. It cannot prove the running implementation, patch state, vulnerable behavior, or security impact. Preserve each proof layer.
This skill plans and records assessment. It does not provide exploit commands, payloads, credentials, poisoning, relay, coercion, replay, fuzz cases, or target contact.
1. Record exact targets, assessment window, disruption ceiling, signal ceiling or null, and written authorization by action class. Preserve approval gates and prohibitions separately. 2. Inventory each service from evidence. Keep transport reachability, protocol identity, implementation identity, version, configuration, and patch state as separate claims. Unknown stays null. 3. Store evidence provenance, trust, bounded or unknown time, subject, summary, and limitations. Historical notes and scanner labels remain low-trust context. 4. Classify candidates by protocol weakness family. Link advisories without equating a version-range match with applicability. 5. Separate candidate proof into: service observation, weakness evidence, reproduced behavior, and demonstrated impact. A socket reset needs independent service-health or crash evidence before it can prove a fault. 6. Define possible next actions by class, target, prerequisites, disruption, request count, expected telemetry, stop conditions, authorization, and cleanup. 7. Derive action state and candidate state. Recommend the lowest cost authorized action whose prerequisites are met and that adds evidence. 8. Mark a candidate finding-ready only after the weakness is demonstrated. Keep advisory matches and leads in the open-work ledger. 9. Cover service identity, implementation state, authentication, cryptography, state machine, input handling, disclosure, and telemetry/recovery for every service. Blocked and not-applicable work remains visible.
active validation.
reproducible protocol fault exists.
prerequisites for exploit development.
finding protocol weaknesses.
lie. This proves a match, not the running code's vulnerability.
parser rejection can reset one socket while the service stays healthy.
inspect what evidence the check actually collected.
real exposure without proving a particular attack path.
and outage risk that require explicit authorization and recovery controls.
query window cannot support a detection conclusion.
provenance, or configuration record may answer the question with less risk.
Portable, evidence-bound security skills for authorized defensive assessment, penetration testing, red teaming, detection engineering, purple teaming, and engagement operations. The collection contains 56 skills in 10 installable plugins from zero-lab.ai.
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
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