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/ContoriumLabs/contoriumWrote 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/contoriumlabs/contorium/learn-workspace-intent)<a href="https://agentmods.dev/commands/contoriumlabs/contorium/learn-workspace-intent"><img src="https://agentmods.dev/badge/commands/contoriumlabs/contorium/learn-workspace-intent/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/contoriumlabs/contorium/learn-workspace-intent"><img src="https://agentmods.dev/badge/commands/contoriumlabs/contorium/learn-workspace-intent.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 | $0.00020 | $0.00129 |
| Opus 5.5 | $0.00008 | $0.00052 |
| Sonnet 5.5 | $0.00004 | $0.00026 |
| Haiku 4.5 | $0.00002 | $0.00013 |
Grade A, and why
learn-workspace-intent 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
Learn workspace intent
Use when structured operational intent would help the user or export quality.
- Check
contora.aiProvideris notoffand the vendor API key is configured via Contorium: Configure API key…. - Run Contorium: Learn workspace intent (AI) (
contora.analyzeWorkspaceIntent). - Intent is stored in
.contora/last-intent.jsonwith lifecycle metadata; stale intent is ignored automatically on export and in the sidebar.
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 · 13 lines · 20 tokens per session scan A 686a00ca30a1
learn-workspace-intent is a command published in the GitHub repository ContoriumLabs/contorium (7 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 129 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 2026-08-31.
Other commands, from other repositories
rb-refresh
A command that rebuilds RepoBrain's knowledge base for the current workspace. RepoBrain is a tool that keeps searchable project information for an agent.
prime
Load SageOx team context for this AI coworker session.
dmb-resume
Resume DMB context for this session.
dmb-status
Show DMB status for this project.
handoff
Saves session context to the active thread so the next agent can pick up where you left off.
mem-last
Prints latest memory cards for the current project.