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/agentsope/skillalchemy/agentsop-per-model-artifactsnpx skills add agentsope/SkillAlchemy --skill agentsop-per-model-artifactsgit 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-per-model-artifacts)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-per-model-artifacts"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-per-model-artifacts.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 | $0.00168 | $0.08267 |
| Opus 5 | $0.00084 | $0.04133 |
| Sonnet 5 | $0.00034 | $0.01653 |
| Haiku 4.5 | $0.00017 | $0.00827 |
Grade A, and why
agentsop-per-model-artifacts 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 5d 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 — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Per-Model Prompt Artifacts — SOP
"Prompts are effectively the weights of an LLM application." — DSPy core philosophy [arxiv.org/abs/2310.03714]
"Treat the compiled program as a (program × LM) pair. Changing the LM invalidates the artifact — recompile." — dspy-sop SKILL, Dilemma Case B
1. 何时激活 (When to activate)
Activate this skill when any of the following appears in the user's intent, codebase, or workflow:
| Trigger | Signal |
|---|---|
| Adopting compiled prompts | compiled.save("v1.json"), dspy.load_program(), BootstrapFewShot, MIPROv2, GEPA, LangChain Hub hub.push/pull, prompt files checked into prompts/ or artifacts/ |
| Multi-LM production | The same program runs against ≥2 of: gpt-4o-*, gpt-4o-mini-*, gpt-4.1-*, claude-3-5-sonnet-*, claude-3-7-sonnet-*, claude-3-opus-*, Llama-3-*, Llama-3.1-*, DeepSeek-V3, gemini-2.5-pro |
| Provider deprecation | OpenAI/Anthropic deprecation notice mentioning a pinned snapshot; alias rollover (gpt-4o → new dated snapshot); silent model behavior drift reports |
| Framework deprecation | LlamaIndex ServiceContext → Settings; LangChain LLMChain → LCEL; DSPy major version bump; aider edit-format default change |
| Symptoms | prompts/system_prompt.txt (no model in path), generic alias pins (model="gpt-4o"), missing parent_artifact lineage, hand-edited compiled JSON, no held-out test set re-runs |
| Cross-skill bridges | DSPy compile produced output → ship via this SOP. LlamaIndex index baked with embed model → tag artifact per this SOP. Aider edit-format pin → per-model config artifact per Recipe 6 (R2) |
Do NOT activate when:
- Raw, one-off prompt edits with no compile step and no production deploy.
- A genuinely model-agnostic system prompt that has been swap-tested across ≥3 LMs with <2-point dev metric drift.
- Prompts that must remain verbatim human-authored for compliance — versioning still matters, but the optimizer/recompile loop does not apply.
- The whole pipeline lives behind a vendor's managed prompt (e.g. OpenAI's Prompt Library) where the vendor owns the artifact.
What ships with it
4 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.
- 5d ago First seen · 438 lines · 168 tokens per session scan A 0c6aedff534d
agentsop-per-model-artifacts is a skill published in the GitHub repository agentsope/SkillAlchemy (357 stars, last pushed 2d ago), licensed MIT. It adds 168 tokens to every session and 8,267 once invoked, about $0.0008 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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