queue-refill

A skill for keeping an experiment queue filled with new, testable research ideas. It uses recent findings, failed directions, current best results, and prediction accuracy to propose the next experiments.

In plain words
What is it for?
It is for refilling a research queue with three to five hypotheses, each including an expected metric change and a connection to the project's current direction.
Why use it?
It reduces the need to decide manually what to test next and helps avoid repeating approaches already marked as dead ends.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/rockielab/rockie-codex/queue-refill
Any agent
npx skills add Rockielab/rockie-codex --skill queue-refill
Clone the repo
git clone --depth 1 https://github.com/Rockielab/rockie-codex

Made for: Claude Code, Codex.

Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 877 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash 9d4e11ffc4ea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

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.

project-extension/agents/skills/queue-refill/SKILL.md · 81 lines

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 /schedule or /loop), so the queue is always ≥ target when a GPU frees up.
  • Reactive: when queue.py refill-needed exits non-zero.
  • Manual: user asks "what should we try next?" or "refill the queue".

What the skill does

  1. Read recent context from workflow.db:

    • Last 20 [LEARN] rules (learnings table)
    • All active dead_ends for this project
    • best_so_far view (per metric)
    • calibration_scorecard — which hypotheses were over/under-predicted
    • experiments table — recent nodes, stages, failure_class distribution
  2. Read STATE.md to understand current research direction.

  3. 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_ends direction (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 get suggestion (draft → tune → creative → ablation)
  4. Call queue.py add for 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
    
  5. 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_ends matches 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.

Read the full file on GitHub · 81 lines

Changes

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.

  1. 3d ago First seen · 81 lines · 90 tokens per session scan A 9d4e11ffc4ea

Subscribe to this mod's changes

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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