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-framework-selectiongit 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-framework-selection)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-framework-selection"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-framework-selection/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-framework-selection"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-framework-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 Memory Poisoning · line 96 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
- medium Memory Poisoning · line 153 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00189 | $0.06467 |
| Opus 5.5 | $0.00076 | $0.02587 |
| Sonnet 5.5 | $0.00038 | $0.01293 |
| Haiku 4.5 | $0.00019 | $0.00647 |
Grade A, and why
agentsop-framework-selection 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Framework-Fit Decision Tree at Project Kickoff · SOP (ENHANCE overlay)
Overlay posture: this is the capstone Phase-D skill — the most-cited entry at any project kickoff. It decides which layer(s) you need and which framework owns each layer. It does not teach any framework's API; for that, descend to the per-framework SOPs (
langgraph-sop,llamaindex-sop,dspy-sop,crewai-sop,vllm-sop,aider-sop,dify-sop). Every load-bearing claim carries an inline source tag resolving inreferences/R1-decision-tree.md.Neutrality note: vendor pages each claim the center of the universe (LangChain: "use LangGraph for production"; LlamaIndex: "the document agent platform"; Dify: "scaffolding is the bottleneck"). This skill quotes those claims but does not adopt any of them. The 7 SOPs disagree on the crossover points; we surface the disagreements rather than papering over them.
1. 何时激活 (When to Activate)
Activate when any of the following fire:
- A new LLM / agent / RAG project is starting and no framework has been chosen yet.
- Someone asks "which framework should I use?" / "LangChain or LlamaIndex?" / "LangGraph vs CrewAI?" / "do we need a framework at all?"
- A coder is about to
pip installan orchestration / RAG / agent framework before having articulated what layers the project needs. - A project already picked one framework "for everything" and is now fighting it in a layer it was never good at (e.g., doing deep RAG inside CrewAI, or hand-rolling retrieval inside LangGraph).
- A no-code / visual builder (Dify, Flowise, LangFlow) has hit a complexity ceiling and the team is asking "do we rewrite in code?"
Do not re-run this skill mid-implementation for a layer already chosen — that
is churn. Run it once at kickoff, and again only when a new layer appears
(e.g., "we now need to self-host the model" → triggers [[agentsop-llm-engine-selection]]).
Mental check: the wrong framework is the single highest-cost decision in the project — it is a one-week-to-reverse mistake, sometimes a one-month one.
crewai-sop · OP-1,vllm-sop · OP-7. Spend 20 minutes on this tree before opening any tutorial.
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 · 399 lines · 189 tokens per session scan A 1ea746dc7ba5
agentsop-framework-selection is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 189 tokens to every session and 6,467 once invoked, about $0.0008 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.
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