services-auditor

name	services-auditor
description	Use when the user runs `empirica scan --explain` or asks you to audit running AI services. You read the deterministic scanner snapshot, judge each AI-touching process against the bundled security corpus, and emit findings/assumptions/unknowns with confidence + cited corpus sections. Two-tier judgment (cheap AI-touching pre-filter, then full taxonomy with citation). Read-only by design — never kill processes or modify configuration; emit `recommended_action` strings only. Tracks citation coverage explicitly (which of the 5 corpus files were referenced before each finding) so trust grounding is auditable.
version	1.0.0

Services Auditor — AI judgment over a deterministic scanner snapshot

This skill fires when an AI agent is asked to reason about the running state of an AI-touching machine. The deterministic scan (empirica scan) gives you the snapshot — what's actually executing right now. Your job is to judge each AI-touching entry against the bundled security corpus and emit empirica artifacts with confidence and citation.

You are not a separate process. You are the AI session that the user asked to audit. Run inside a normal empirica transaction.

---

When to use

• The user typed empirica scan --explain and a system-reminder pointed you here.
• The user said "audit running services," "what AI agents are dangerous here," "review the scan output," or similar.
• A scheduled services-audit loop fired (Phase 3, future) and woke you with this skill referenced.

If you only need the inventory itself (no judgment), point the user at empirica scan instead.

---

Phase 0 — PREFLIGHT

Open a transaction with the audit work_type so the Sentinel weights your evidence sources correctly:

empirica preflight-submit - <<'EOF'
{
  "task_context": "Services audit — read the deterministic scanner snapshot at ~/.empirica/last_scan_<project_id>.json, judge each AI-touching entry against the security corpus, emit findings/assumptions/unknowns.",
  "work_type": "audit",
  "domain": "default",
  "criticality": "medium",
  "vectors": {
    "know": 0.55, "uncertainty": 0.45,
    "context": 0.70, "clarity": 0.65,
    "engagement": 0.85
  },
  "reasoning": "Audit transaction. Will read snapshot + corpus, judge per-process against the taxonomy, cite sections."
}
EOF

---

Phase 1 — Read inputs

Two files are load-bearing:

1. The scanner snapshot

Always read the most recent saved snapshot (the user gets this via empirica scan --explain which auto-saves):

cat ~/.empirica/last_scan_<project_id>.json

If absent, run empirica scan --save yourself first, then read it.

2. The bundled security corpus

Stable, citable canon at empirica/data/security-corpus/ (or the user-customizable copy at ~/.empirica/security-corpus/ if present):

File	Source	Section IDs you cite
owasp-llm-top10.md	OWASP 2025	LLM-A01 … LLM-A10
owasp-agentic-top10.md	OWASP Dec 2025	Agentic-A01 … Agentic-A10
nist-ai-rmf.md	NIST AI RMF 1.0	GOVERN-1.5, MEASURE-2.7, …
mitre-atlas.md	MITRE ATLAS	T1499, T1078, T1588, T1059, …
google-saif.md	Google SAIF	SAIF-1 … SAIF-6

Section IDs are stable across revisions even when the body content is currently a stub. Cite the IDs.

---

Phase 2 — Two-tier judgment

Tier 1 — Cheap AI-touching pre-filter

Walk the snapshot's process list. For each row, classify in one short pass: AI-touching (true / false).

A process is AI-touching if any of:

• cmdline contains claude, cursor, codex, aider, gh copilot, gemini, ollama, vllm, llama-cpp, lmstudio, openai, anthropic, cohere, huggingface, replicate, qdrant, chromadb, weaviate, pinecone, langchain, crewai, autogen
• holds an env var name matching *_API_KEY for an AI vendor (cross- reference process_env.var_names_only if available)
• listens on a port commonly used by local AI tooling (11434 ollama, 8000/8080 generic LLM servers, 6333 qdrant default, 6379 redis if flagged in registered MCP servers)
• is registered as an MCP server in ~/.claude/mcp.json

Filter the ~hundreds of processes down to a working set of ~10–30. Most processes (browsers, terminals, system daemons) are not AI-touching and don't need full taxonomy judgment.

Tier 2 — Full taxonomy per AI-touching process

For each survivor, judge against the corpus and emit one artifact.

Confidence ladder (per the proposal):

Confidence	Citation present?	Artifact type	Behavior
≥ 0.95	yes	finding-log	high-trust
0.6 – 0.95	yes	assumption-log	medium-trust, logged
< 0.6	any	unknown-log	needs human review
any	no	unknown-log	uncited downgrades

Emission examples (use the batch form when emitting many at once via log-artifacts; the single-verb form is fine for one-off artifacts):

# High-trust finding with citation
empirica finding-log --finding "PID 12345 (curl -N https://ntfy.sh/...) is an orphaned credentialed listener — parent PID 1, cmdline references ntfy auth env vars, age 14 days. Recommended: kill 12345 + investigate parent recovery." \
  --impact 0.85 --visibility shared --output json
empirica source-add --title "OWASP Agentic Top 10 — A06: Vulnerable & Outdated Components" \
  --url "https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/" \
  --noetic --confidence 0.95 --output json
# Then link via log-artifacts evidence edge if you want the graph

# Medium-trust assumption (not enough signal for finding)
empirica assumption-log --assumption "PID 87654 (ollama serve, age 2 days) is benign because it's localhost-only on port 11434 with no external peers in the snapshot." \
  --confidence 0.75 --domain security --visibility shared

# Unknown — uncertain or uncited
empirica unknown-log --unknown "PID 98765 (/opt/foo/binary, no recognizable cmdline) — purpose unclear, no AI vendor signature, no listening port. Manual investigation required."

Citation discipline (load-bearing)

Every finding and assumption you emit MUST cite at least one corpus section ID. The citation goes in the artifact text itself (human-readable) AND optionally as a source-add + sourced_from edge for graph traversal.

Uncited findings are downgraded to unknown regardless of model confidence. This is not negotiable — it is the trust-grounding contract that makes the auditor's output auditable.

---

Phase 3 — Coverage tracking

The paper (COVERAGE_VECTORS_PAPER_OUTLINE.md) defines coverage as inspected / relevant. Track yours explicitly so the user can see what fraction of the relevant material you actually inspected:

Dimension	Numerator	Denominator
Process coverage	processes you full-judged in tier 2	AI-touching processes after tier 1 filter
Citation coverage	unique corpus section IDs you cited	corpus sections that exist (sum across the 5 files)
Listener coverage	listeners you judged	total listeners in network.connections

Surface the numbers in your final summary, e.g.:

Coverage: 18/24 AI-touching processes judged (75%),
          7/52 corpus sections cited (13%),
          4/4 listeners judged (100%).

A 95%-confidence finding with 13% citation coverage is honest. A 95%-confidence finding without a coverage report is not.

---

Phase 4 — POSTFLIGHT

Close the transaction with grounded vectors that reflect what you actually did:

empirica postflight-submit - <<'EOF'
{
  "vectors": {
    "know": 0.85, "uncertainty": 0.15,
    "completion": 1.0, "do": 0.85,
    "impact": 0.65, "engagement": 0.85
  },
  "reasoning": "Audit complete. Judged N AI-touching processes against corpus. Emitted X findings + Y assumptions + Z unknowns. Citation coverage K/52. Recommended actions surfaced as text — no destructive operations performed."
}
EOF

Phase 3 (future) wires a biweekly cron loop that fires this skill automatically. Today, the user runs it on demand.

---

Out of scope (V1)

• Process killing or config mutation. Read-only. recommended_action strings only. Empirica does not execute them; the user does.
• Network packet inspection. Metadata only — connection 5-tuple + listening ports. Same posture as Phase 1.
• Multi-host fleet view. Separate product (empirica fleet).
• Hosted-agent inventory (cloud operators on user's account) — Phase 4+ — needs API token introspection.
• RAG over the corpus. Phase 4 — needs Qdrant collection.
• Fine-grained semantic confidence calibration over many runs — comes from coverage paper validation work, not the auditor itself.

---

Anti-patterns

• Emitting findings without citation. Downgrade to unknown.
• Listing every process in the system as "interesting." Tier 1 must filter aggressively. Most processes are not AI-touching.
• Skipping the snapshot read. The deterministic snapshot is the ground truth — reasoning from memory or guess is uncited and breaks the contract.
• Inflating confidence to clear the citation requirement. The ladder gates by both — confidence ≥ 0.95 + cited is the only "finding" path. Inflating confidence to dodge an honest "unknown" is exactly the failure mode the auditor exists to flag in others.
• Killing processes or pushing config changes. Read-only by design. If you observe a vulnerability that warrants action, the user takes the action.

---

Sister skill: /services-audit-cron

For unattended scheduled scans (Phase 3), invoke /services-audit-cron to register the canonical biweekly cron loop. Body is one command (empirica services-audit) that does scan + diff + notify-on-novelty; loop registry + heartbeat handle the schedule. Complementary, not redundant: services-auditor is on-demand AI judgment; the cron loop is automated novelty detection.