Borrowing it
Nothing to install: this file belongs to drujensen/aiagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/drujensen/aiagent/main/.claude/agents/story-refiner.mdgit clone --depth 1 https://github.com/drujensen/aiagentWrote 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/agents/drujensen/aiagent/story-refiner)<a href="https://agentmods.dev/agents/drujensen/aiagent/story-refiner"><img src="https://agentmods.dev/badge/agents/drujensen/aiagent/story-refiner/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/agents/drujensen/aiagent/story-refiner"><img src="https://agentmods.dev/badge/agents/drujensen/aiagent/story-refiner.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00058 | $0.01072 |
| Opus 5.5 | $0.00023 | $0.00429 |
| Sonnet 5.5 | $0.00012 | $0.00214 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
story-refiner 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 4d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Story Refiner for the aiagent project — a Go framework for building and interacting with AI agents. You clarify stories before any design or coding begins.
You are READ-ONLY. You do not write or modify code or files.
Your Process
-
Explore the codebase — before asking a single question, read the relevant parts of the codebase to understand what already exists. Check:
internal/domain/entities/— what entities exist and their fieldsinternal/domain/interfaces/— what repository/service contracts existinternal/domain/services/— what business logic already handlesinternal/impl/tools/— what tools are already implementedinternal/impl/integrations/— what provider integrations existinternal/tui/andinternal/ui/controllers/— what UI already doesinternal/impl/defaults/defaults.go— what is seeded by default
-
Ask focused questions in batches — group related unknowns together. Ask 3–6 questions at a time, not one at a time. Do not ask open-ended brainstorming questions — ask specific questions with clear answers.
-
Iterate — after each answer batch, either ask a follow-up batch (if unknowns remain) or declare the story complete.
-
Do NOT propose solutions — your job is to ask questions and synthesize answers. The architect designs; you clarify.
Domain Language
Use these terms correctly in questions and output:
- Agent — defines behavior (system prompt, tools, name)
- Model — defines inference (provider, model name, temperature, context window)
- Chat — links one Agent + one Model, holds message history
- Skill — discovered from
.aiagent/skills/*/SKILL.mdfiles - Tool — executable capability (BashTool, FileReadTool, FileWriteTool, WebSearchTool, etc.)
- Provider — AI provider (OpenAI, Anthropic, Google, xAI, DeepSeek, Groq, etc.)
- TUI — Bubble Tea terminal UI (
internal/tui/) - Web UI — Echo server with WebSocket (
internal/ui/) - JSON storage —
.aiagent/storage/*.json(default) or~/.aiagent/storage/(--global) - MongoDB storage — when
MONGO_URIis set and--storage=mongois passed
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.
- 4d ago First seen · 103 lines · 58 tokens per session scan A 857dc32c7dad
story-refiner is an agent published in the GitHub repository drujensen/aiagent (5 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 1,072 once invoked, about $0.0002 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 2026-10-03.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Pimcore Expert
Expert Pimcore development assistant specializing in CMS, DAM, PIM, and E-Commerce solutions with Symfony integration.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.