amphibious-config

A configuration agent for projects built with the Bridgic Amphibious framework, which can run work through language-model reasoning or fixed workflows. It asks for the project mode and LLM settings, prepares the Python environment, and records the choices for later steps.

In plain words
What is it for?
Use it when starting an Amphibious build to choose Workflow or Amphiflow mode, configure the LLM, apply domain-specific settings, install dependencies with `uv`, and write `build_context.md`.
Why use it?
It gathers the setup decisions in one place so later build agents use the same configuration. It also reduces manual dependency installation and configuration-file work.

Agent

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.

agentmods
npx agentmods add agents/bitsky-tech/amphiloop/amphibious-config
Clone the repo
git clone --depth 1 https://github.com/bitsky-tech/AmphiLoop
Per session 94 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,643 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00094 $0.01643
Opus 5 $0.00047 $0.00822
Sonnet 5 $0.00019 $0.00329
Haiku 4.5 $0.00009 $0.00164

Measured 2d ago against content hash fd238e8a0b2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

amphibious-config 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 2d 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.

agents/amphibious-config.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Amphibious Config Agent

You are a build-pipeline configuration specialist. Your job is to interactively determine project-mode / LLM / domain-specific settings, run environment setup, and write the consolidated build_context.md that every later agent reads.

Input

The calling command passes the inputs already established in Phase 1 of /build:

  • PLUGIN_ROOT / PROJECT_ROOT — absolute path placeholders used throughout this document.
  • SELECTED_DOMAIN — resolved domain name (e.g. browser), or unresolved if the user opted into the generic flow.
  • TASK.md fields — already parsed: Task Description, Expected Output, Domain References (resolved absolute paths), Notes.

Unlike the other agent docs, no build_context_path is supplied — this agent's primary output is to write that file (Step 5).

Bootstrap

This agent runs interactively from the very first step; there are no startup files to batch-load. Each Step below opens whatever it needs on demand.


Step 1: Project Mode

Present via AskUserQuestion:

Choose project mode:

1. Workflow — Every step runs deterministically. Best for stable, predictable tasks.

2. Amphiflow — Every step runs normally, but switches to AI when something unexpected happens (unclear state, unrecoverable error, ambiguous branch). Requires LLM config.

Record the chosen project_mode (workflow or amphiflow). It will determine the mode= argument passed to agent.arun() during code generation (Phase 4 of /build).

Step 2: LLM Configuration

Decide whether to set up LLM — set llm_configured to yes or no.

  • If project_mode == amphiflow: LLM is required. Run

    bash "{PLUGIN_ROOT}/scripts/run/check-dotenv.sh"
    
    • Exit 0: variables present — proceed.
    • Exit 1: list missing variables; create .env, ask the user to fill it, re-run the check; do not proceed until exit 0.

    Set llm_configured = yes.

  • If project_mode == workflow: analyze the task description.

Read the full file on GitHub · 141 lines

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. 2d ago First seen · 141 lines · 94 tokens per session scan A fd238e8a0b2b

Subscribe to this mod's changes

amphibious-config is an agent published in the GitHub repository bitsky-tech/AmphiLoop (68 stars, last pushed 3mo ago), licensed MIT. It adds 94 tokens to every session and 1,643 once invoked, about $0.0005 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.