agentsop-prompt-history-inspect

agentsop-prompt-history-inspect is a skill for Claude Code, Codex from agentsope/SkillAlchemy. It costs 203 tokens per session (8,489 once invoked), scanned A, original, MIT.

A debugging workflow for inspecting the exact prompt that a language model received. It is used when the model produces an unexpected answer, malformed output, refusal, truncation, tool error, or changed behaviour.

In plain words
What is it for?
It helps investigate wrong answers, schema failures, missing or incorrect tool calls, repeated or empty responses, unexpected token costs, slow responses, and regressions after framework or model changes.
Why use it?
The prompt sent to the model may differ from the prompt written by the developer because frameworks can add templates, examples, instructions, or tool definitions. Inspecting the rendered prompt shows what actually influenced the result.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for aider. Also seen: positional $N argument; built for aider.

Good fit It helps investigate wrong answers, schema failures, missing or incorrect tool calls, repeated or empty responses, unexpected token costs, slow responses, and regressions after framework or model changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentsope/skillalchemy/agentsop-prompt-history-inspect
Install

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.

Any agent
npx skills add agentsope/SkillAlchemy --skill agentsop-prompt-history-inspect
Clone the repo
git clone --depth 1 https://github.com/agentsope/SkillAlchemy

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agentsop-prompt-history-inspect

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-prompt-history-inspect/github.svg)](https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-prompt-history-inspect)
Your own site
<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.

agentmods 80×15 button for agentsop-prompt-history-inspect

Your own site · 80×15
<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>
Per session 203 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,489 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash c419e6f3ca2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/agentsop-prompt-history-inspect/SKILL.md · 422 lines

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)

Read the full file on GitHub · 422 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 422 lines · 203 tokens per session scan A c419e6f3ca2e

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

shellgames

Play board games on ShellGames.ai — Chess, Poker, Ludo, Tycoon, Memory, and Spymaster. Use when the agent wants to play games against humans or other AI agents, join tournaments, chat with players, check leaderboards, or manage a ShellGames account. Triggers on "play chess/poker/ludo/memory", "shellgames", "join…

MemTensor/skills-vote · 103 tokens

incident-commander

Autonomous incident detection, root-cause analysis, and self-healing for Linux/Docker production environments. Activate when the user mentions: server down, high CPU, memory leak, disk full, service crash, deployment failure, alert firing, on-call page, or any infrastructure emergency. Also activates on scheduled…

Lethe044/hermes-incident-commander · 92 tokens

curl-search

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

MemTensor/skills-vote · 55 tokens

skills-vote-local

Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.

MemTensor/skills-vote · 28 tokens

python-run

Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.

congchuanling-dot/Cohort · 56 tokens

systematic-debugging

4-phase root cause debugging: understand bugs before fixing.

HezaoHezao/poirot · 16 tokens