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-prompt-history-inspectgit 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-prompt-history-inspect)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-prompt-history-inspect"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-prompt-history-inspect/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-prompt-history-inspect"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-prompt-history-inspect.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00203 | $0.08489 |
| Opus 5 | $0.00102 | $0.04244 |
| Sonnet 5 | $0.00041 | $0.01698 |
| Haiku 4.5 | $0.00020 | $0.00849 |
Grade A, and why
agentsop-prompt-history-inspect 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 12d 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt-History Inspect — First Move Before Anything Else
"The prompt you wrote is not the prompt the model received." — Operating axiom for every framework that templates, injects few-shots, appends tool definitions, or wraps system messages.
1. 何时激活 (When to activate)
Activate this skill the moment an LM call surprises you, BEFORE any other debug move.
| Trigger | Signal |
|---|---|
| Output wrong | "Why did it answer X?" / hallucinated fact / wrong format / refusal |
| Output truncated | mid-sentence cut, partial JSON, missing fields |
| Output empty / repeats | model returns "", repeats the same token, loops |
| Behaviour changed | "It worked yesterday" / "It worked on GPT-4o but not on Llama-3" |
| Cost / latency spike | tokens jumped 3× without code change → something got injected |
| Tool call wrong | wrong tool picked, args malformed, tool call missing |
| Schema validation failed | Pydantic / Outlines / guidance grammar refused output |
| Eval regression | metric dropped after upgrading framework version |
| Production bug | a user-facing thread produced a wrong answer — need to see what the LM saw |
Do NOT activate when:
- You are authoring a new prompt for the first time (no rendered prompt exists yet).
- The bug is clearly outside the LM call (retriever returned empty, API key invalid, network down).
- The framework hasn't even been called yet (e.g., import error, schema validation pre-call).
The trigger is universal across the stack. Any framework that templates a prompt — DSPy, LangChain, LangGraph, CrewAI, LlamaIndex, Aider, Guidance, Outlines — has a layer between "what you wrote" and "what the model received." This skill is the first-line probe into that gap.
2. 核心心智模型 (Core mental model)
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ What you wrote │ ≠ │ What was rendered│ ≠ │ What the LM saw │
│ (template, │ │ (after few-shot │ │ (after provider │
│ signature, etc) │ │ injection, tool │ │ reformatting, │
│ │ │ defs appended, │ │ message squash, │
│ │ │ system msg, │ │ token truncation)
│ │ │ history, etc) │ │ │
└──────────────────┘ └──────────────────┘ └──────────────────┘
layer 1 layer 2 layer 3
(your code) (framework render) (provider transport)
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.
- 12d ago First seen · 422 lines · 203 tokens per session scan A c419e6f3ca2e
agentsop-prompt-history-inspect is a skill published in the GitHub repository agentsope/SkillAlchemy (394 stars, last pushed 9d ago), licensed MIT. It adds 203 tokens to every session and 8,489 once invoked, about $0.0010 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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