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.
npx agentmods add skills/rockielab/rockie-codex/queue-refillnpx skills add Rockielab/rockie-codex --skill queue-refillgit clone --depth 1 https://github.com/Rockielab/rockie-codexWhat 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 | $0.00090 | $0.00877 |
| Opus 5 | $0.00045 | $0.00439 |
| Sonnet 5 | $0.00018 | $0.00175 |
| Haiku 4.5 | $0.00009 | $0.00088 |
Grade A, and why
queue-refill 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 3d 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.
This is a copy
100% identical to queue-refill — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/queue-refill — auto-refill the experiment queue
Keeps the autonomous agent's forward-looking work queue full. This is the "Prioritization Specialist" pattern from arXiv 2604.13018 adapted to our harness.
When to run
- Scheduled: every N hours (via
/scheduleor/loop), so the queue is always ≥ target when a GPU frees up. - Reactive: when
queue.py refill-neededexits non-zero. - Manual: user asks "what should we try next?" or "refill the queue".
What the skill does
-
Read recent context from workflow.db:
- Last 20
[LEARN]rules (learningstable) - All active
dead_endsfor this project best_so_farview (per metric)calibration_scorecard— which hypotheses were over/under-predictedexperimentstable — recent nodes, stages, failure_class distribution
- Last 20
-
Read STATE.md to understand current research direction.
-
Brainstorm 3–5 new queue items. Each item must:
- Be a single-sentence testable hypothesis
- Include a predicted metric delta (forced quantitative prior)
- Not overlap an active
dead_endsdirection (query before proposing) - Prefer building on best-so-far (extend the working path) unless there's evidence the path is saturating
- Match the current
stage.py getsuggestion (draft → tune → creative → ablation)
-
Call
queue.py addfor each. Example:python3 .codex/scripts/queue.py add \\ --hypothesis "Matrix token init with log-normal std 0.02 reduces warmup loss vs normal(0, 0.02)" \\ --metric val_loss --predicted-delta -0.04 \\ --priority 2 --minutes 45 --stage creative -
Report what was added in a single message to the user.
Constraints
- Never re-propose an active dead-end direction. If the FTS5 search
on
dead_endsmatches what you're about to add, skip it. - Always include a predicted_delta. Missing priors break hypothesis calibration.
- Priority scale: 1 = highest (block the queue if nothing else), 5 = nice-to-have. Default 3.
- Estimated_minutes: be honest. Under-estimating torches the budget controller. Over-estimating means the scheduler never picks it.
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.
- 3d ago First seen · 81 lines · 90 tokens per session scan A 9d4e11ffc4ea
queue-refill is a skill published in the GitHub repository Rockielab/rockie-codex (20 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 90 tokens to every session and 877 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to queue-refill, differing in 2 lines, and is treated as a copy.
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