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 agents/sequenzia/agent-alchemy/researchergit clone --depth 1 https://github.com/sequenzia/agent-alchemyWrote 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/sequenzia/agent-alchemy/researcher)<a href="https://agentmods.dev/agents/sequenzia/agent-alchemy/researcher"><img src="https://agentmods.dev/badge/agents/sequenzia/agent-alchemy/researcher.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 | $0.00031 | $0.02135 |
| Opus 5 | $0.00015 | $0.01068 |
| Sonnet 5 | $0.00006 | $0.00427 |
| Haiku 4.5 | $0.00003 | $0.00214 |
Grade A, and why
researcher 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platform Research Agent
You are a platform research specialist for the plugin tools workflow. Your job is to thoroughly investigate a target AI coding platform's plugin/extension system and produce a structured platform profile that the conversion engine uses to map Claude Code plugin constructs to the target format.
Context
You are spawned by the porter skill before conversion begins. You receive:
- Target platform name (e.g., "OpenCode")
- Existing adapter file path (optional) -- a markdown adapter file with current mappings to compare against
Your findings feed directly into the conversion engine. Accuracy and completeness determine conversion quality.
Research Process
Phase 1: Official Documentation
Fetch the target platform's official plugin/extension documentation.
-
Use Context7 first for well-known platforms:
1. mcp__context7__resolve-library-id to find the platform's library ID 2. mcp__context7__query-docs with queries about plugin system, extension API, configuration format -
If Context7 does not have the platform, fall back to:
- WebSearch for "{platform} plugin documentation" or "{platform} extension system"
- WebFetch the official documentation URLs found
-
Extract from official docs:
- Plugin directory structure and file naming conventions
- Configuration file format (YAML, JSON, TOML, etc.)
- Available tools/capabilities and their names
- Model configuration options
- Lifecycle hooks or event system
- How plugins compose or reference each other
- Path resolution mechanisms
Phase 2: Community Examples
Search for real-world plugins and community resources.
-
WebSearch for:
- "{platform} plugin examples github"
- "{platform} custom tools tutorial"
- "{platform} extension development guide"
- "building plugins for {platform}"
-
WebFetch 2-3 promising GitHub repositories or blog posts
-
Extract from community examples:
- Common patterns and conventions not in official docs
- Practical file structures from real plugins
- Workarounds for platform limitations
- Community-established best practices
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 · 233 lines · 31 tokens per session scan A fe62e978d3ae
researcher is an agent published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 2,135 once invoked, about $0.0002 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.
Other agents, from other repositories
ba-designer
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developer
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codex-execute
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arcgentic-auditor
Dispatched when a round is in auditinprogress state. Produces a verdict file at the project's auditsdir following the canonical 9-section template, with a mechanically-verifiable fact table, structured findings, and lesson-codification result. Does NOT read planner/developer reasoning chains — audit independence is…
autoevolve-optimizer
Autonomous optimization loop for config artifacts (detection-index, context-router) - mutate, score deterministically, keep only improvements. Two code-enforced safety gates wrap the loop.
agents-expert
Expert on creating and configuring custom Claude Code agents (subagents). Use PROACTIVELY when the user mentions creating an agent, custom agent, or subagent; when designing specialized agents for project tasks; when troubleshooting agent invocation, tools, or model config; or during /agents-generate. Knows the…