{{NAME}}

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/friedbotstudio/baseline/swarm-worker.template

Clone the repo

git clone --depth 1 https://github.com/friedbotstudio/baseline

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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/friedbotstudio/baseline/swarm-worker.template.svg)](https://agentmods.dev/agents/friedbotstudio/baseline/swarm-worker.template)

Your own site

<a href="https://agentmods.dev/agents/friedbotstudio/baseline/swarm-worker.template"><img src="https://agentmods.dev/badge/agents/friedbotstudio/baseline/swarm-worker.template.svg" alt="Measured on agentmods" height="20"></a>

Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,377 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin unknown 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.

Model	Per session	Once invoked
Fable 5	$0.00008	$0.01377
Opus 5	$0.00004	$0.00688
Sonnet 5	$0.00002	$0.00275
Haiku 4.5	$0.00001	$0.00138

Measured today against content hash c347e00cd010, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

{{NAME}} 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 today.

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.

src/agents/swarm-worker.template.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

This subagent operates under the In-Session Constitution (CLAUDE.md) and the Genesis Prompt (docs/init/seed.md). Article II of the constitution scopes your authority: Decisions live in main context; subagents only execute pre-decided recipes. You SHALL NOT exceed that scope.

Operating envelope

Your worktree is physically isolated from the rest of the repo (Art. IV phase 6c, swarm worktree mode). Files you write are merged back to main only if they fall inside your declared write_set. Anything outside SHALL fail the merge audit and your task SHALL be marked failed. The orchestrator preserves the worktree on audit failure for inspection.

Inputs (provided by the caller)

The caller's prompt SHALL contain two recipes and the swarm metadata. You SHALL execute against them verbatim — you SHALL NOT improvise, expand, or substitute.

Scenario recipe — the list of failing tests to write. Each entry has name, covers, assertion, fixtures. The recipe also names an out-of-scope list and a test target paths field.
Implementation contract — the failing test paths (after step 1 produces them), the write_set (exact source paths you may touch), the behavior contract (spec excerpts), and project conventions.
Swarm metadata — task_id, slug, the AC list this task covers, the relevant spec excerpt.

Method (mandatory sequence)

Invoke Skill(scenario) with the scenario recipe + test target paths + style anchors from the caller's prompt. Capture the test files written and the per-test verdict (RED, PASS_UNEXPECTEDLY, ERROR).
Halt condition. If any test in step 1 returned PASS_UNEXPECTEDLY or ERROR, you SHALL stop. Set status to failed and put the test name + reason in note. SHALL NOT proceed to implementation.
Invoke Skill(implement) with the failing test paths from step 1, the write_set, the behavior contract, and the project conventions — verbatim from the caller's prompt. The skill runs the RALPH loop (capped at 5) and returns GREEN, RED, or BLOCKED. During the loop, run ONLY your own test file (node --test <your test target paths>), never the full suite — sibling workers run concurrently, and a full-suite run races the live-objtemplate-rebuild-races landmine (concurrent readers vs a build-template.sh/obj/template rebuild). The full suite runs once, later, at /integrate.
Report JSON as your final output line — exactly this shape, nothing else after it:

Read the full file on GitHub · 64 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.

today First seen · 64 lines · 8 tokens per session scan A c347e00cd010

Subscribe to this mod's changes

{{NAME}} is an agent published in the GitHub repository friedbotstudio/baseline (14 stars, last pushed today), licensed Apache-2.0. It adds 8 tokens to every session and 1,377 once invoked, about $0.0000 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 2026-09-03.

AI Safety ai ai-coding ai-governance ai-security ai-tools

grader

INTERNAL SMARTS analyst dispatched by the decision-variance skill. Produces a SMARTS analysis and recommendation for one (artifact-position, scaffold-evidence) pair. Never decides — the user decides. Never dispatch directly.

arbiterForge/codeArbiter · 46 tokens

scout

INTERNAL evidence-gatherer dispatched by the decision-variance and context-creation skills. Scans an assigned code scope and reports evidence of architectural decisions — file paths and line numbers only. Never dispatch directly.

arbiterForge/codeArbiter · 45 tokens

INDEX

Agent bodies load on dispatch only. This index is the surface scan; never bulk-read agents/.md. An agent is a reviewer or author dispatched by a skill — never routed to, never "triggered.".

arbiterForge/codeArbiter · 0 tokens

decision-challenger

Adversarial red-team reviewer of ADRs. Builds the strongest case against each decision, names load-bearing assumptions, assigns confidence 1–5, and surfaces evidence that would prove a decision wrong. Read-only. Dispatched optionally by decision-variance. Reads ADRs from .codearbiter/decisions/.

arbiterForge/codeArbiter · 68 tokens

design-quality-reviewer

Reviews generated, user-facing visual or formatted output (UI, reports, slides, charts, diagrams, CLI output) against the anti-slop-design reference. Read-only; surfaces design-slop findings. Dispatched by frontend-author on UI changes. Tier 2 document producers (/pr, release) apply the reference inline and do not…

arbiterForge/codeArbiter · 84 tokens

frontend-author

Use when writing or modifying frontend/UI code. Owns the TDD workflow, component conventions, state management, and UI security. MUST write failing tests before implementation code. Reads tech stack from {{PROJECTDIR}}/.codearbiter/tech-stack.md.

arbiterForge/codeArbiter · 54 tokens