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/rumotion/ai-agent-project-templateWrote 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/rumotion/ai-agent-project-template/save-context)<a href="https://agentmods.dev/commands/rumotion/ai-agent-project-template/save-context"><img src="https://agentmods.dev/badge/commands/rumotion/ai-agent-project-template/save-context/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/rumotion/ai-agent-project-template/save-context"><img src="https://agentmods.dev/badge/commands/rumotion/ai-agent-project-template/save-context.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 | $0.00011 | $0.00117 |
| Opus 5.5 | $0.00004 | $0.00047 |
| Sonnet 5.5 | $0.00002 | $0.00023 |
| Haiku 4.5 | $0.00001 | $0.00012 |
Grade A, and why
save-context 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 2mo 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
The current unit of work is finished. Following docs/agent-loop.md §5:
- Append a phase-completion summary (outcome, key decisions, files modified, open items, next) to
memory-bank/activeContext.md. - Update
memory-bank/progress.md(Done / Next) andmemory-bank/handoff.md. - Clear any acted-on entries from
memory-bank/reminders.md.
Change only files whose facts changed. Keep every entry concise.
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.
- 2mo ago First seen · 12 lines · 11 tokens per session scan A 3800bdee27e6
save-context is a command published in the GitHub repository rumotion/ai-agent-project-template (1 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 117 once invoked, about $0.0000 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
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.
compact-prep
Ask the agent to prepare for conversation compaction by updating any relevant state and providing guidance for the compaction agent and to kick off the session there after.
perf-difficult-conversation
Three-conversations framework rehearsal (Stone/Patton/Heen — Harvard Negotiation Project) for high-stakes conversations — termination, demotion, hard feedback, conflict surfacing. Produces three-conversations breakdown (what-happened / feelings / identity), conversation script with multiple opening choices, SCARF…
culture-onboarding-90
Design a 90-day onboarding architecture for a role. First day (psychological safety + belonging activation), first week (relationship + role context + early wins), first month (skill ramp + integration + baseline), first quarter (autonomous performance + cultural integration check). Written-first elements for…
reclaim-knowledge
Organize scattered professional material into a domain-based second-brain architecture. Maps existing sources (Canva, Drive, local folders, Notion, screenshots) to functional domains. Non-technical-friendly — produces folder structure + drag-and-drop instructions, not CLI commands.
discover-genius
Excavate genius from a person's scattered corpus. Produces Genius Profile (what only they uniquely see) + Freedom Path (four-bucket sort of every activity). Gateway to all downstream intelligence systems. For humans, not agents.