Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add thrashr888/AllBeads/plugin install allbeadsWrote 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/thrashr888/allbeads/prime)<a href="https://agentmods.dev/commands/thrashr888/allbeads/prime"><img src="https://agentmods.dev/badge/commands/thrashr888/allbeads/prime.svg" alt="Measured on agentmods" 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 | $0.00006 | $0.00332 |
| Opus 5 | $0.00003 | $0.00166 |
| Sonnet 5 | $0.00001 | $0.00066 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
prime 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 7d 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
Load project context into agent memory for effective work.
Usage
ab prime # Prime with all contexts
ab prime -C <context> # Prime specific context
What Gets Loaded
-
Project Overview
- Active contexts and paths
- Repository status (beads, agents)
-
Ready Work
- Unblocked beads ready to implement
- Priority-sorted task list
-
Recent Activity
- Recently modified beads
- Recent commits
-
Statistics
- Open/closed/blocked counts
- Work distribution
When to Use
- Start of new session
- After conversation compaction
- Switching between contexts
- Before starting complex work
Output
# AllBeads Context Priming
## Active Contexts
- AllBeads: /Users/me/Workspace/AllBeads (174 beads)
- rookery: /Users/me/Workspace/rookery (23 beads)
## Ready Work (5 tasks)
1. ab-xyz [P1] - Implement feature X
2. rk-123 [P2] - Fix bug in auth
## Recent Activity
- 3 beads closed today
- 2 beads created today
## Statistics
- Open: 45, In Progress: 3, Blocked: 2, Closed: 124
See Also
/ready- Just show ready work/stats- Just show statistics/list- Full bead listing
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.
- 7d ago First seen · 65 lines · 6 tokens per session scan A a561a18c1096
prime is a command published in the GitHub repository thrashr888/AllBeads (8 stars, last pushed 7d ago), licensed MIT. It adds 6 tokens to every session and 332 once invoked, about $0.0000 per session on Opus 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
lavra-learn
Curate raw knowledge comments into structured, well-tagged entries for future auto-recall.
lavra-recall
Search knowledge base mid-session and inject relevant context.
lavra-checkpoint
Save session progress by filing beads, capturing knowledge, and syncing state.
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.