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 agentsope/SkillAlchemy --skill agentsop-llm-artifact-versioninggit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-llm-artifact-versioning)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-llm-artifact-versioning"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-llm-artifact-versioning/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/agentsope/skillalchemy/agentsop-llm-artifact-versioning"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-llm-artifact-versioning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 110 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 415 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 505 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00223 | $0.07191 |
| Opus 5 | $0.00112 | $0.03596 |
| Sonnet 5 | $0.00045 | $0.01438 |
| Haiku 4.5 | $0.00022 | $0.00719 |
Grade A, and why
agentsop-llm-artifact-versioning 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 9d 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Versioned, Reproducible LLM Artifact — Overlay SOP
"Prompts are effectively the weights of an LLM application." — DSPy core philosophy [arxiv.org/abs/2310.03714] (R1 §1)
"Treat the compiled program as a (program × LM) pair. Changing the LM invalidates the artifact — recompile." — dspy-sop SKILL, Dilemma Case B (R1 §2)
This is an enhancement overlay, not a framework SOP. It sits on top of whatever stack you use (DSPy, LangChain, raw API) and adds one discipline: define, pin, and version the entire deployable bundle as a unit. It is the broad sibling of [[agentsop-per-model-artifacts]] — that skill versions one compiled prompt; this one versions everything that ships together.
1. 何时激活 (When to activate)
Activate when any of these appears in the user's intent, codebase, or workflow:
| Trigger | Signal |
|---|---|
| Preparing to deploy | "ship this to prod", a Dockerfile/deploy.yaml/serving entrypoint wrapping an LLM app, a release checklist |
| "What is running in prod?" | Nobody can name the exact prompt text + model snapshot + retriever config currently serving traffic |
| Reproducibility need | "reproduce the deploy from last quarter", an audit, a regulator asking what produced an output |
| Rollback need | Incident: prod behavior changed and the team needs the last known-good combination of components back |
| Drift symptoms | Score moved, no code change merged; or "we updated the prompt but forgot which model it was tuned for" |
| Multi-component apps | RAG + reranker + synthesizer + judge, each naming its own model/config, none bundled |
| Cross-skill bridges | DSPy save_program produced a compiled program → it is one component of the bundle; pin the rest. Per-prompt lifecycle handled by [[agentsop-per-model-artifacts]] → wrap as a bundle component here. |
Do NOT activate when:
- One-off prompt edit with no deploy and no reproduction requirement — just edit and run.
- A single-component proof-of-concept where there is exactly one prompt, one model, no
retrieval, and it will never be reproduced. Flat
v1.jsonis fine. - A vendor owns the whole lifecycle (OpenAI Prompt Library, Anthropic Workbench managed prompts, fully vendor-managed RAG) — let them version it.
- The team rewrites the app daily during exploration — bundle versioning has no shelf life until the app shape stabilizes (same boundary as dspy-sop "signature still changing").
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.
- 9d ago First seen · 509 lines · 223 tokens per session scan A 25740143d0ad
agentsop-llm-artifact-versioning is a skill published in the GitHub repository agentsope/SkillAlchemy (377 stars, last pushed 7d ago), licensed MIT. It adds 223 tokens to every session and 7,191 once invoked, about $0.0011 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 2026-08-30.
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