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/revaya-ai/revaya-aios-workspace-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/revaya-ai/revaya-aios-workspace-template/reflect)<a href="https://agentmods.dev/commands/revaya-ai/revaya-aios-workspace-template/reflect"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/reflect/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/revaya-ai/revaya-aios-workspace-template/reflect"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/reflect.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 | $0.00000 | $0.01260 |
| Opus 5 | $0.00000 | $0.00630 |
| Sonnet 5 | $0.00000 | $0.00252 |
| Haiku 4.5 | $0.00000 | $0.00126 |
Grade A, and why
reflect 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Daily Assessment — Learning Loops cadence 1 of 5. End-of-session or end-of-day capture. Target: 5–10 minutes. Output is filed to
knowledge/learnings/and feeds the Ops Center's knowledge loop.
Purpose
This is not journaling. It is structured data capture for the AIOS. Every session produces execution data. This command extracts it before it's lost. The Auto-Capture data layer feeds Knowledge Management, which feeds Learning Loops, which refines the Strategic Layer.
Run this at the end of any significant work session — especially before closing Claude Code.
Step 1: Load the OOBG
Read strategic-layer/oobg.md.
Ask: Was today's work aligned with the One Objective? Did it address the current Bottleneck? Score it:
- 🔴 Directly addressed the Bottleneck
- 🟡 Served the Objective but didn't move the needle on the Bottleneck
- 🟢 Maintenance/required work, not bottleneck-breaking
- ⚪ Unclear — flag for review
Step 2: Top Accomplishment
What was the single most important thing completed today?
One sentence. Specific. Not "worked on the plan" — "created the strategic-layer directory with all 4 files."
Step 3: Top Learning or Insight
What's the most important thing learned or realised during today's execution?
Could be technical (a tool works differently than expected), strategic (a market insight), operational (a process that should change), or personal (a pattern in how you work).
If nothing significant: that's fine. Say so. Don't manufacture learnings.
Step 4: Friction Identified
What slowed things down, created confusion, or felt harder than it should?
This is AIOS improvement data. Friction is a signal. Small frictions repeated over sessions compound into significant drag.
If friction was identified:
- Note it here
- Log it to
gtd/inbox.mdfor the next/processsession — it may become an improvement task
Step 5: Execution Pattern
Did anything repeat today that could become a reusable knowledge chunk?
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.
- 11d ago First seen · 158 lines · 0 tokens per session scan A 36689e9ce546
reflect is a command published in the GitHub repository revaya-ai/revaya-aios-workspace-template (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,260 tokens. 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
context-restore
Expert Context Restoration Specialist focused on intelligent, semantic-aware context retrieval and reconstruction across complex multi-agent AI workflows. Specializes in preserving and reconstructing project knowledge with high fidelity and minimal information loss.
ingest
Ingest source material into an active wiki. Accepts URLs, file paths, PDFs, freeform text, or processes the inbox. Supports tweets via Grok MCP.
session
Capture, list, rehydrate, and promote llm-wiki session context. Supports automated hook capture into HUB/.sessions plus explicit promotion into topic raw notes.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
pull-learnings
Pulls shared organization patterns into local org-patterns.toon.
memory-list
DEPRECATED: Use Serena listmemories instead. Lists recent memories from Forgetful with optional project filtering.