agent-runtime-validati…
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
Plans authorized offline password-strength audits across supplied hash datasets while enforcing compute budgets, progressive pass order, cohort coverage, aggregate-only reporting, and restricted handling. Use when preparing or documenting password cracking without online
Claude Code
$ npx -y skills add zero-lab-ai/skills --skill password-audit-plan --agent claude-code
Codex
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a codex -y
Cursor
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a cursor -y
Gemini CLI
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a gemini-cli -y
GitHub Copilot
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a github-copilot -y
OpenCode
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a opencode -y
Windsurf
$ npx skills add zero-lab-ai/skills --skill password-audit-plan -a windsurf -y
name: password-audit-plan description: "Plans authorized offline password-strength audits across supplied hash datasets while enforcing compute budgets, progressive pass order, cohort coverage, aggregate-only reporting, and restricted handling. Use when preparing or documenting password cracking without online authentication, spraying, stuffing, replay, or credential reuse testing." allowed-tools: Read Grep Glob
Offline password auditing is a measured experiment over supplied material. Algorithm, hardware, candidate space, runtime, and cohort denominators determine what the result means. Preserve unknowns until measurement exists.
This skill plans and records offline analysis. It does not collect hashes, expose raw material, generate organization-derived candidates, authenticate, spray, stuff, replay, test reuse, contact accounts, or access target systems.
1. Record authorization, offline-only boundary, compute and storage ceilings, prohibited sources and actions, stop conditions, workspace, retention, owner, and disposition evidence. 2. Inventory each dataset using opaque IDs. Record source class, record and unique digest counts, acquisition-time precision, algorithm claim, validation state, cost parameters, integrity, and limitations. Never place raw hashes in the plan. 3. Preserve record counts and digest counts separately. Deduplicate work while keeping the mapping needed to expand aggregate recoveries back to record and cohort counts. 4. Define cohorts with explicit denominators and overlap rules. Disabled, privileged, service, administrator, and standard populations remain separate where supplied. 5. Plan format validation and a measured benchmark before recovery passes. Do not invent throughput, runtime, keyspace completion, or recoveries. 6. Order bounded passes from generic baseline through generic rules, bounded masks, and optional adaptive work. Each pass names dependencies, candidate-source class, dataset, GPU budget, expected measurement, and stop conditions. 7. Let the review reject budget over-allocation, recovery before validation and benchmark, prohibited candidate sources, online actions, unproved results, and inconsistent cumulative counts. 8. Record pass results as counts and rates only. Attribute each unique recovery to its first successful pass; expand record recovery and cohort recovery from the preserved mapping without exposing plaintext. 9. Analyze aggregate length, character-class, duplicate, pattern-label, cohort, policy shape, and marginal-yield metrics. A policy document is a requirement, not proof a recovered password complied when created. 10. Track raw datasets, recovered material, potfiles, restore state, generated candidates, logs, and exports through disposition. Report only approved aggregate artifacts and opaque IDs. 11. Cover format, deduplication, cohorts, benchmark, baseline, rules, masks, adaptive work, handling, and reporting for every dataset. Blocked work remains visible.
cracking or decrypting.
a separate capability and authorization; they are outside this skill.
spending compute or interpreting results.
the reporting population. Expand results back to records and cohorts.
are not measurements of this dataset and configuration.
expected evidence value, and explicit priorities; record the rationale.
state, usability, privilege, or successful authentication.
candidate strings do not belong in the assessment record or report.
same storage, access, retention, and deletion rules.
authorization, even when the hardware is idle.
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.
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