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.
git clone --depth 1 https://github.com/RayforceDB/raysenseWrote 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/rayforcedb/raysense/audit)<a href="https://agentmods.dev/commands/rayforcedb/raysense/audit"><img src="https://agentmods.dev/badge/commands/rayforcedb/raysense/audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/rayforcedb/raysense/audit"><img src="https://agentmods.dev/badge/commands/rayforcedb/raysense/audit.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00025 | $0.00197 |
| Opus 5.5 | $0.00010 | $0.00079 |
| Sonnet 5.5 | $0.00005 | $0.00039 |
| Haiku 4.5 | $0.00003 | $0.00020 |
Grade A, and why
audit 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 1mo ago.
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.
What it actually says
Run a heavy structural audit of the project at $1.
If $1 is empty, treat the project root as the current working directory.
- Call
raysense_architecturewithpath: $1for top-level metrics, root-cause scores, cycles, levels, and unstable modules. - Call
raysense_evolutionwithpath: $1for churn × coupling hotspots. - Call
raysense_test_gapswithpath: $1for under-tested high-traffic files. - Call
raysense_dsmwithpath: $1for the dependency structure matrix.
Synthesize a written architecture review: structural strengths, the three biggest risks, and three concrete remediations to consider.
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.
- 1mo ago First seen · 16 lines · 25 tokens per session scan A 310234d80b08
audit is a command published in the GitHub repository RayforceDB/raysense (13 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 197 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other commands, from other repositories
debug
Systematic debugging: root cause before any fix, one hypothesis at a time, and a three-strikes rule that questions the architecture instead of stacking patches.
lint
The canonical linter per ecosystem over your changes: findings are diagnoses you judge, fix, or explain.
layer-check
You are the Three-Layer Enforcement Agent, the strict validator that ensures every feature is REAL across all tiers: Database, Backend, and Frontend. You do not accept incomplete implementations.
logic-fix-all
Autonomous audit-and-fix — after consent, scan the target, fix every logic issue found (all severities), verify each fix, and report anything unresolved.
logic-explain
Trace execution step by step — use when code behavior is surprising or confusing.
logic-health
Sweep a whole codebase or directory for logic correctness — use before a release or to identify risk hotspots.