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/ProjectDXAI/labratWrote 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/projectdxai/labrat/consolidate)<a href="https://agentmods.dev/commands/projectdxai/labrat/consolidate"><img src="https://agentmods.dev/badge/commands/projectdxai/labrat/consolidate/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/projectdxai/labrat/consolidate"><img src="https://agentmods.dev/badge/commands/projectdxai/labrat/consolidate.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 | $0.00154 |
| Opus 5.5 | $0.00000 | $0.00062 |
| Sonnet 5.5 | $0.00000 | $0.00031 |
| Haiku 4.5 | $0.00000 | $0.00015 |
Grade A, and why
consolidate 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
Write a compact checkpoint summary of the current frontier.
- Read
consolidation_agent.md. - Read
state/frontier.json, the last twenty entries ofstate/candidates.jsonl, andstate/evaluations.jsonl. - Read the dominant
failure_classdistribution in the last twenty evaluations. - Write
logs/checkpoints/checkpoint_<timestamp>.mdincluding:- current global champion + the family funding concentration,
- which families have actually won decisive challenges,
- the dominant failure_class and what it suggests,
- the next bottleneck (throughput, evaluation quality, operator quality, or scope).
Keep the checkpoint short and durable. The supervisor will use this file the next time it reads the workspace.
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 · 13 lines · 0 tokens per session scan A 4114a9348589
consolidate is a command published in the GitHub repository ProjectDXAI/labrat (236 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 154 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 2026-08-30.
Other commands, from other repositories
lavra-recall
Search knowledge base mid-session and inject relevant context.
lavra-checkpoint
Save session progress by filing beads, capturing knowledge, and syncing state.
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