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/jagg111/coi-researchqueue/skill-retronpx skills add Jagg111/COI-ResearchQueue --skill skill-retrogit clone --depth 1 https://github.com/Jagg111/COI-ResearchQueueWrote 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/skills/jagg111/coi-researchqueue/skill-retro)<a href="https://agentmods.dev/skills/jagg111/coi-researchqueue/skill-retro"><img src="https://agentmods.dev/badge/skills/jagg111/coi-researchqueue/skill-retro.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 | $0.00076 | $0.00927 |
| Opus 5 | $0.00038 | $0.00464 |
| Sonnet 5 | $0.00015 | $0.00185 |
| Haiku 4.5 | $0.00008 | $0.00093 |
Grade A, and why
skill-retro 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 4d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 4d ago First seen · 77 lines · 76 tokens per session scan A 60cee134a06b
skill-retro is a skill published in the GitHub repository Jagg111/COI-ResearchQueue (5 stars, last pushed 3mo ago), with no licence file. It adds 76 tokens to every session and 927 once invoked, about $0.0004 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 skills, from other repositories
autoresearch
One-prompt agent autoresearch loop for StepCOVNet: goal + time budget → orient, hypothesize, run, log, repeat until success or deadline. Use when the user says "run autoresearch", gives a research goal with a budget, or names a profile (e.g. ar-tide-overfit). Not for unattended Python planners (--hours / lattice).
whats-next
Answers what to do now, what's next, where are we, project status, or orientation. When the user then says do it, go ahead, or start, execute the If you say do it steps from the last whats-next answer. Runs projectstatus.py and reads EXPERIMENTLOG § Current phase.
agent-brain-refresh
Audit and sync agent brain — read all rules from disk, reconcile skills/catalogs/routing. Triggers on refresh agent brain, refresh all rules, audit rules, or after steering promotion. Read-only audit script; agent writes doc updates.
steering-correction-promotion
On user steering corrections or remember-this, pick the smallest durable artifact (skill section, scoped rule, or rare always-apply rule), slim AGENTS.md, optimize agent-brain context, then prepend JRN. Use instead of creating new alwaysApply rules by default.
wsl-gpu-stepcovnet
Runs StepCOVNet GPU training and MERT extraction on Windows via WSL with TensorFlow CUDA libraries. Use for trainonset, overfit scripts, extractmertfeatures with cuda, or when TensorFlow sees zero GPUs in WSL.
ar-tide-autoresearch
Deprecated alias — use autoresearch skill with profile ar-tide-overfit.