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/eliransu/digital-brainWrote 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/eliransu/digital-brain/task)<a href="https://agentmods.dev/commands/eliransu/digital-brain/task"><img src="https://agentmods.dev/badge/commands/eliransu/digital-brain/task/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/eliransu/digital-brain/task"><img src="https://agentmods.dev/badge/commands/eliransu/digital-brain/task.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 | $0.00075 | $0.00266 |
| Opus 5.5 | $0.00030 | $0.00106 |
| Sonnet 5.5 | $0.00015 | $0.00053 |
| Haiku 4.5 | $0.00007 | $0.00027 |
Grade A, and why
task 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
Invoke the task skill from the digital-brain plugin. Follow its SKILL.md exactly: parse $ARGUMENTS for title, @<Person> assignee (default you), sprint: cycle (default current), team: key (default from wiki/daily/_config.json → INV), priority:, due:. Resolve team UUID via list_teams, assignee via list_users, current cycle via list_cycles. Call mcp__claude_ai_Linear__save_issue. Then mirror as a vault note (note_kind: task, linear: <ID>) under wiki/notes/, register in wiki/notes/_index.md, touch wiki/hot.md only if due ≤ 24h, append to wiki/log.md. Confirm with the Linear URL and the mirror note path.
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 · 6 lines · 75 tokens per session scan A cdadd9fe1d3d
task is a command published in the GitHub repository eliransu/digital-brain (1 stars, last pushed 5mo ago), licensed MIT. It adds 75 tokens to every session and 266 once invoked, about $0.0003 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.
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checkpoint
Write a project checkpoint to this project's AgentDB store with ruflo memory store — the goal, the acceptance contract, the decisions, what is done, what is blocked, and the exact next action — and prove it by exact-key ruflo memory retrieve. The next session (Claude Code or Codex) restores the newest checkpoint by…
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Update a Jira task with a summary of the fix implementation.