Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/datacore-one/datacore/audit-agentsgit clone --depth 1 https://github.com/datacore-one/datacoreWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/datacore-one/datacore/audit-agents)<a href="https://agentmods.dev/commands/datacore-one/datacore/audit-agents"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/audit-agents.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00008 | $0.01222 |
| Opus 5 | $0.00004 | $0.00611 |
| Sonnet 5 | $0.00002 | $0.00244 |
| Haiku 4.5 | $0.00001 | $0.00122 |
Grade A, and why
audit-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/audit-agents
Command Context
When to Reference DIP-0016
Always reference when:
- Auditing agent registry entries
- Checking spawn relationships
- Validating reads/writes paths
- Injecting Agent Context sections
Key decisions this DIP informs:
- Registry entry requirements
- Agent Context section format
- Spawn cycle detection
- Compliance scoring
Quick Reference
| Question | Answer |
|---|---|
| Registry file? | .datacore/registry/agents.yaml |
| Commands registry? | .datacore/registry/commands.yaml |
| Agent files? | .datacore/agents/*.md |
| What DIPs govern this? | DIP-0016 (Agent Registry) |
Agents This Command Invokes
| Agent | Purpose |
|---|---|
agent-registry-auditor |
Compliance audit |
Integration Points
- DIP-0016 - Agent registry specification
- /diagnostic - System health complement
Audit agents for DIP-0016 compliance and registry alignment.
Workflow
Step 1: Understand Intent
If user invoked /audit-agents with no arguments, ask:
"What would you like to audit?"
- Full audit - Scan all agents, check registry, detect issues (Recommended)
- Specific agent - Audit a single agent by name
- Generate missing - Only generate entries for unregistered agents
- Fix issues - Run audit and auto-fix with confirmation
If intent is clear from context (e.g., /audit-agents ai-task-executor), proceed directly.
Step 2: Run Audit
Invoke the agent-registry-auditor agent with the selected scope:
Launching agent-registry-auditor...
The auditor will:
- Scan all agent files in
.datacore/agents/and module agent directories - Compare against
.datacore/registry/agents.yaml - Validate spawn relationships and detect cycles
- Check that read paths exist
- Generate compliance report
Step 3: Present Results
Show the compliance report:
AGENT REGISTRY AUDIT REPORT
═══════════════════════════════════════════════════════════════
Summary:
Total agents: 25
Fully compliant: 22
Needs attention: 3
Issues:
[!] agent-name - Missing registry entry
[!] other-agent - Spawns non-existent target
...
═══════════════════════════════════════════════════════════════
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 212 lines · 8 tokens per session scan A 2a5405a779bc
audit-agents is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 1,222 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
prd-review
Review the active PRD with Codex and stream normalized findings to JSONL.
prd-archive
Archive the active PRD (blocked until every accepted finding has a receipt).
prd-map
Build a codebase map so PRDs are written with repo context, not blind.
prd-split
Split the approved PRD into one issue spec per manifest entry.
rca-check
Lint an RCA or premortem document against the canonical template.
prd-os-init
Initialize prd-os in this repo (writes .prd-os/config.json).