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/mrboups/xbrainWrote 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/mrboups/xbrain/profile-user)<a href="https://agentmods.dev/commands/mrboups/xbrain/profile-user"><img src="https://agentmods.dev/badge/commands/mrboups/xbrain/profile-user/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/mrboups/xbrain/profile-user"><img src="https://agentmods.dev/badge/commands/mrboups/xbrain/profile-user.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 | $0.00017 | $0.00371 |
| Opus 5.5 | $0.00007 | $0.00148 |
| Sonnet 5.5 | $0.00003 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
gsd:profile-user 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.
This is a copy
89% identical to gsd:profile-user — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Routes to the profile-user workflow which orchestrates the full flow: consent gate, session analysis or questionnaire fallback, profile generation, result display, and artifact selection.
<execution_context> @D:/VSC/xbrain/.claude/get-shit-done/workflows/profile-user.md @D:/VSC/xbrain/.claude/get-shit-done/references/ui-brand.md </execution_context>
The workflow handles all logic including:
- Initialization and existing profile detection
- Consent gate before session analysis
- Session scanning and data sufficiency checks
- Session analysis (profiler agent) or questionnaire fallback
- Cross-project split resolution
- Profile writing to USER-PROFILE.md
- Result display with report card and highlights
- Artifact selection (dev-preferences, CLAUDE.md sections)
- Sequential artifact generation
- Summary with refresh diff (if applicable)
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 · 47 lines · 17 tokens per session scan A a93b27c120e9
gsd:profile-user is a command published in the GitHub repository mrboups/xbrain (2 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 371 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to gsd:profile-user, differing in 4 lines, and is treated as a copy.
Other commands, from other repositories
setup
Write a starter orchestrator config and check every agent login.
slm-cache
KV cache for repeated reads — call slmcacheget(key) first; on a miss do the expensive operation then slmcacheset(key, value, ttlseconds) to store it; on a hit use the returned value directly; always fail-open (hit:false on any error, never raises); saves tokens when the same file, query result, or tool output is read…
end
End a session — log what happened and propose durable memory updates.
pensyve
Route explicit Pensyve memory requests through the bundled MCP server; supports recall, remember, observe, inspect, status, review, forget, and mention-style guidance.
start
Start a session — load context + memory and brief yourself on current state.
recall
Search Pensyve memory by semantic similarity and text matching.