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 ggg123124/vrchat-assistant --skill closed-loop-deliverygit clone --depth 1 https://github.com/ggg123124/vrchat-assistantWrote 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/ggg123124/vrchat-assistant/closed-loop-delivery)<a href="https://agentmods.dev/skills/ggg123124/vrchat-assistant/closed-loop-delivery"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/closed-loop-delivery.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.00869 |
| Opus 5 | $0.00017 | $0.00434 |
| Sonnet 5 | $0.00007 | $0.00174 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
closed-loop-delivery 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Closed-Loop Delivery
Overview
Treat each task as incomplete until acceptance criteria are verified in evidence, not until code is merely changed.
Core rule: deliver against DoD (Definition of Done), not against code diff size.
When to Use
Use this skill when:
- user gives a coding/fix task and expects end-to-end completion
- task spans code + tests + PR comments + dev deploy + runtime checks
- repeated manual prompts like "now test", "now deploy", "now re-check PR" should be avoided
Do not use this skill for:
- pure Q&A/explanations
- prod deploy requests without explicit human approval
- tasks blocked by missing secrets/account access that cannot be inferred
Required Inputs
Before execution, define these once:
- task goal
- acceptance criteria (DoD)
- target environment (
devby default) - max iteration rounds (default
2)
If acceptance criteria are missing, request them once. If user does not provide, propose a concrete default and proceed.
Issue Gate Dependency
Before execution, prefer using create-issue-gate.
- If issue status is
readyand execution gate isallowed, continue. - If issue status is
draft, do not execute implementation/deploy/review loops. - Require user-provided, testable acceptance criteria before starting execution.
Default Workflow
-
Define DoD
- Convert request into testable criteria.
- Example: checkout task DoD = "checkout endpoint returns a valid, openable third-party payment URL in dev".
-
Implement minimal change
- Keep scope tight to task goal.
-
Verify locally
- Run focused tests first, then broader checks if needed.
-
Review loop
- Fetch PR comments/reviews.
- Classify valid vs non-actionable.
- Fix valid items, re-run verification.
-
Dev deploy + runtime verification
- Deploy to
devwhen runtime behavior matters. - Verify via real API/Lambda/log evidence against DoD.
- Deploy to
-
Completion decision
- Only report "done" when all DoD checks pass.
- Otherwise continue loop until pass or stop condition.
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 · 121 lines · 35 tokens per session scan A c45db3685715
closed-loop-delivery is a skill published in the GitHub repository ggg123124/vrchat-assistant (21 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 869 once invoked, about $0.0002 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 2026-09-04.
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