agent-runtime-validati…
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
Triages supplied Windows, Linux, or other volatile-memory images while preserving acquisition provenance, image integrity, process and region evidence, network and object correlations, extracted-artifact custody, timeline limits, and claim confidence. Use for offline
Claude Code
$ npx -y skills add zero-lab-ai/skills --skill memory-forensics-triage --agent claude-code
Codex
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a codex -y
Cursor
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a cursor -y
Gemini CLI
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a gemini-cli -y
GitHub Copilot
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a github-copilot -y
OpenCode
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a opencode -y
Windsurf
$ npx skills add zero-lab-ai/skills --skill memory-forensics-triage -a windsurf -y
name: memory-forensics-triage description: "Triages supplied Windows, Linux, or other volatile-memory images while preserving acquisition provenance, image integrity, process and region evidence, network and object correlations, extracted-artifact custody, timeline limits, and claim confidence. Use for offline memory-forensics review and handoff, not live rootkit trust assessment or full malware reverse engineering." allowed-tools: Read Grep Glob
Memory is a captured vantage, not an infallible narrator. Acquisition changes the system; the acquisition path can be subverted; symbols can be wrong; one image can produce many correlated outputs without becoming multiple independent sources.
This skill analyzes supplied image facts and normalized outputs. It does not acquire memory, provide live collection commands, recover credential material, execute extracted code, query reputation services, or authorize containment.
1. Record written authorization, case and asset identity, platform, image name, size, format, acquisition method and window, tool identity, hash, transfer, custodian, storage, access-log state, acquisition impact, and clock basis. 2. Record the analysis engine, version, reported platform or build, selected symbols or profile, match state, validation evidence, and limitations. A successful plugin run does not prove the profile is correct. 3. Inventory each supplied observation with its image, source group, artifact class, locator, role, honest time precision, and limitations. Different plugins over one image share one source group. 4. Correlate process inventory and lineage, executable regions, modules or mappings, network objects, handles, registry objects, files, credentials, kernel or driver structures, and supplied baselines. Preserve benign alternatives and missing cross-vantage evidence. 5. Keep claim types narrow. A connection is a network object, not C2 attribution. An LSASS handle is credential access capability, not credential extraction. A Run value is a persistence mechanism, not proof it executed. 6. Use `suspicious` for mutually reinforcing indicators within one source group. Use `corroborated` only with at least two independent source groups. Use `confirmed` only when the claim-specific proof requirements in 7. Track every candidate extraction. `extracted` needs a preserved digest and custody. Malware classification needs analysis of the extracted artifact; bytes that were never recovered cannot be classified. 8. Build timeline events only from cited times. Keep host-clock time separate from trusted time; unknown skew remains a limitation. Do not assign an exact timestamp to an object merely because the image has an acquisition window. 9. Cover all required artifact and case categories. Mark missing work `blocked` with a blocker rather than calling the surface clean. 10. Record owned, authorization-aware next steps and handoffs. Contact with a live host or external service needs explicit authority beyond offline review.
compare kernel, boot, firmware, memory, disk, and external vantages.
preserves extraction provenance and the questions to answer.
Plugins reading one image share acquisition and parsing dependencies.
security components can create similar regions; preserve and analyze the bytes.
connection, not destination ownership, protocol content, or intent.
not proof that credential material was read or used.
represented in memory; execution and durable disk state need more evidence.
file after hashing; it does not validate the acquisition binary or compromised source path.
until clock basis and skew are established.
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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Tracks embedded-device and firmware assessment coverage across exact units, acquisition methods, debug interfaces, stored…
Use for authorized security assessment of REST, GraphQL, WebSocket, or SOAP APIs, including discovery, authentication,…
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys)…
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting…