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 skills add CheshireJCat/blender --skill quality-refinement-autoloopgit clone --depth 1 https://github.com/CheshireJCat/blenderWrote 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/cheshirejcat/blender/quality-refinement-autoloop)<a href="https://agentmods.dev/skills/cheshirejcat/blender/quality-refinement-autoloop"><img src="https://agentmods.dev/badge/skills/cheshirejcat/blender/quality-refinement-autoloop/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/skills/cheshirejcat/blender/quality-refinement-autoloop"><img src="https://agentmods.dev/badge/skills/cheshirejcat/blender/quality-refinement-autoloop.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 | $0.00083 | $0.01045 |
| Opus 5.5 | $0.00033 | $0.00418 |
| Sonnet 5.5 | $0.00017 | $0.00209 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
quality-refinement-autoloop 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Refinement Autoloop
Use this when the user says the result is wrong, ugly, not aligned, not textured, not animated, not exportable, or otherwise below expectation. The goal is not to keep tweaking blindly. The goal is to convert failure into a reusable, generic skill improvement before trying again.
Autoloop phases
0. Freeze and preserve
- Stop making product changes immediately.
- Preserve the last accepted baseline and the failed artifact.
- Name the failed branch/version honestly; do not overwrite accepted outputs.
1. Evidence capture
Collect the smallest evidence set that proves the failure:
- user feedback quote or summary;
- source/reference files used;
- current output path/version;
- relevant render/contact sheet/overlay/audit report;
- scene/material/object inventory if the failure is inside Blender.
2. Diagnose failure dimension
Classify the primary gap:
- geometry / silhouette / landmarks;
- multiview/depth consistency;
- UV / atlas / texture fit;
- closed surface coverage (front/back/side);
- look/material/lighting calibration;
- animation/motion/export truth;
- orchestration/handoff between skills;
- missing validator or missing deterministic helper script.
3. Skill-gap decision
Ask: does the current skill stack already contain a generic method for this failure?
- If yes: run the existing skill and repair the artifact.
- If no: add/refine a generic skill first, then repair.
- If repeated failures come from skill interplay, update the harmonizer/handoff rule, not just a leaf skill.
4. Sanitize the lesson
Before writing a skill change:
- remove project/client/asset names;
- remove secrets, raw logs, private paths, personal data, and copyrighted source content;
- keep only reusable method, gates, scripts, and failure patterns;
- describe inputs/outputs generically;
- prefer deterministic scripts for fragile audits.
5. Patch skill stack
Apply the smallest publishable change:
- one concise skill or one concise section in an existing skill;
- optional helper script if the validation is repeatable;
- harmonizer update if ordering/handoff changed;
- manifest entry/version update.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 118 lines · 83 tokens per session scan A d1e6eb2f32e5
quality-refinement-autoloop is a skill published in the GitHub repository CheshireJCat/blender (60 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 1,045 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
dsh-tui-guide
Use when the user asks about dsh-tui itself (usage, shortcuts, config, themes, migration, VS Code). Load before answering; skip for normal coding tasks.
novel-reviewer
A review and editing workflow for existing novel chapters. It checks story logic, timelines, character states, unresolved clues, world rules, and writing style.
video-recap
A workflow that turns a supplied video into a Chinese narrated recap by combining video understanding, story planning, editing, voiceover, and final assembly.
video-assemble
A video post-production skill for combining narration, source audio, and subtitles into a final video. It can mix voice-over with the original sound, create SRT and ASS subtitle files, optionally burn subtitles into the picture, and standardize loudness.
workflow-manager
A writing workflow tracker for chapter-based projects. It records whether each chapter is being assembled, drafted, reviewed, confirmed, polished, or archived.
dog-import-query
Query a smart-import DoG graph after a novel has been split into chapters. Use to inspect import completeness, AI precheck errors, and pending outline/asset/canonical setup.