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-dspynpx skills add agentsope/SkillAlchemy --skill agentsop-dspygit 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-dspy)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-dspy"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-dspy.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 5.1 | $0.00157 | $0.07485 |
| Opus 5 | $0.00078 | $0.03742 |
| Sonnet 5 | $0.00031 | $0.01497 |
| Haiku 4.5 | $0.00016 | $0.00749 |
Grade A, and why
agentsop-dspy 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 6d 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 — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy SOP — Programming, Not Prompting
"DSPy isn't a prompt-optimization agent framework. It's the LLM compiler for the shortest, cleanest code." — Eito Miyamura [eito.substack.com/p/dspy-the-most-misunderstood-agent]
"Prompts are effectively the weights of an LLM application." — Core philosophy [arxiv.org/abs/2310.03714]
1. 何时激活 (When to activate)
Activate this skill when any of the following triggers are present in the user's intent or codebase:
| Trigger | Signal |
|---|---|
| Imports / mentions | import dspy, dspy.Signature, dspy.ChainOfThought, dspy.ReAct, Predict, MIPROv2, BootstrapFewShot, GEPA, teleprompter, compile( on an LM program |
| Tasks | "auto-tune this prompt", "I want to swap GPT-4 for a smaller model without re-engineering prompts", "I have 50/200/1000 labeled examples — optimize this", "compile a pipeline for our metric", "distill GPT-4 into Llama-3-8B" |
| Symptoms | Hand-written prompts grow past ~50 lines; brittleness on model swap; the team manually tunes few-shot examples; a metric exists but isn't being used to drive prompt design |
| Cross-skill bridges | LangGraph node calls an LLM and needs better prompts → wrap the node body in a DSPy module. LlamaIndex retriever feeds a reranker → DSPy-compile the reranker against a labeled set |
Do NOT activate when:
- The task is one-shot ("just answer this question once") — use raw
client.messages.create. - No evaluation metric is possible and none is willing to be built — DSPy without a metric is just verbose prompting.
- Prompts must remain human-authored verbatim for compliance, audit, or stylistic reasons.
- The team is in rapid exploration mode where the task signature itself is changing daily — compile only after the signature stabilizes [dspy.ai/learn/optimization/overview/].
2. 核心心智模型 (Core mental model)
DSPy's full name is Declarative Self-improving Python. The three primitives form a PyTorch-like compile chain [arxiv.org/abs/2310.03714]:
What ships with it
7 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.
- 6d ago First seen · 443 lines · 157 tokens per session scan A 3a8174510552
agentsop-dspy is a skill published in the GitHub repository agentsope/SkillAlchemy (361 stars, last pushed 3d ago), licensed MIT. It adds 157 tokens to every session and 7,485 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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