Principal Agent Audit

Audit a principal AI agent or coordinator bot: review memory, learnings, recent errors, installed skills, operational risks, delegation posture, and propose...

Use this skill to review a main AI assistant, coordinator bot, or "chief" agent that has access to user context, tools, memory, and other agents.

Default frame: the reviewed agent is the trusted principal agent. Improvements should make it more reliable, private, auditable, useful, and safe as a coordinator.

Boundaries

• Read local memory, daily notes, learnings, skill files, and relevant workspace context.
• Do not use network access unless the user explicitly asks for external lookup.
• Do not publish skills, install packages, alter schedulers, change authentication, or edit critical config unless explicitly requested.
• Do not auto-modify personality, memory policy, routing policy, delegation rules, or coordination behavior. Propose changes first.
• Prefer reversible edits and written rationale.
• Treat private user data as sensitive. Summarize patterns; do not quote secrets or full private logs.

Review Inputs

Inspect only what is relevant:

• Durable memory files: operating preferences, identity, durable user instructions.
• Daily notes: recent raw events, decisions, and repeated themes.
• Learning/error logs: recurring failures, corrections, known tool issues, missing capabilities.
• Tool notes: local assumptions, integration gotchas, device or host specifics.
• Installed skills: overlap, risk, permissions posture, maintenance state, and suitability for a principal agent.
• Agent/team structure: whether delegation boundaries and handoff rules are clear.

Workflow

1. Establish the review question: general health, a specific failure, a proposed skill, a new capability, or multi-agent coordination.
2. Gather the smallest useful local context.
3. Classify findings:
   • Reliability: repeated failures, brittle commands, missing validation.
   • Privacy/security: excess permissions, external calls, token exposure risk.
   • Coordination: unclear agent roles, missing delegation rules, handoff gaps.
   • Memory hygiene: stale, missing, duplicated, or over-specific memories.
   • Tooling: missing binaries, broken assumptions, unsafe defaults.
   • User fit: whether the agent's behavior matches the user's durable preferences.
4. Decide whether action is needed:
   • No action: say so.
   • Documentation update: edit memory or local notes when the preference is durable.
   • Skill update: propose or make scoped edits if requested.
   • External action: ask first.
5. Report as a short operator briefing: verdict, evidence, risk, recommendation, and next action.

Proactive Reliability Patterns

Use these patterns selectively. They are guardrails for a trusted principal agent, not permission to self-modify.

Write-Ahead Logging

Before responding, preserve details that would be expensive to lose:

• User corrections.
• Durable preferences or operating rules.
• Decisions, names, IDs, URLs, dates, or published artifacts.
• Trial windows, scheduled reports, or future obligations.

Prefer raw daily notes for event capture and curated long-term memory only for distilled rules.

Working Buffer And Recovery

When context is near compaction or a session resumes after truncation:

• Record the current task, key decisions, file paths, IDs, and next action before continuing.
• Recover from local memory and workspace artifacts before asking the user to restate context.
• Summarize private context instead of copying full logs.

Verify Implementation, Not Intent

Before reporting completion:

• Verify the mechanism, not just the wording.
• For skill edits: read the updated SKILL.md, validate frontmatter, and confirm metadata still matches behavior.
• For scheduler edits: inspect the actual job, trigger time, delivery target, and job ID.
• For publication: inspect registry metadata after publishing when possible.

Autonomous Vs Prompted Scheduled Work

When evaluating scheduled work:

• Use autonomous isolated jobs when the work must execute without main-session attention.
• Use main-session prompts only when live context or user interaction is required.
• Record expected output and how success will be verified.

Proactivity Gate

Recommend proactive action only when it is local, reversible, low risk, and likely useful. External actions, public actions, broad deletes, publishing, authentication changes, and behavior-policy changes require explicit user approval.

Skill Evaluation Rule

When evaluating a skill for the principal agent, ask:

• Does it improve reliability, privacy, judgment, coordination, or recoverability?
• Does it introduce broad shell access, network dependency, hidden state, self-modification, or unclear external effects?
• Can it operate in read-only or proposal-first mode?
• Is its output auditable and reversible?
• Does it duplicate simpler existing memory, learning, or review workflows?

Classify the skill:

• Use now: low risk, clear benefit, good fit for the principal agent.
• Adapt locally: useful idea, but needs pruning, sandboxing, or stricter boundaries.
• Avoid: risk exceeds benefit for a trusted coordinator.

Output Style

• Be concise and direct.
• Lead with the verdict.
• Separate "use now", "adapt locally", and "avoid" when evaluating skills.
• Prefer conservative changes that make coordination clearer and safer.
• If changes were made, list exact files touched.

Decision Rule

A capability belongs in a principal agent only if it helps the agent become more reliable, private, auditable, and useful as a coordinator. Capabilities that add autonomy, network dependency, hidden state, broad shell access, or self-modification require exceptional justification and explicit approval.