{artifact}-generator

{artifact}-generator is an agent for coding agents from bluzir/claude-pipe. It costs 4 tokens per session (1,021 once invoked), scanned A, original, MIT.

An agent for producing a specified artefact from prepared research and synthesis data.

In plain words
What is it for?
Use it to generate reports or other defined deliverables from a synthesis file, plan, template, style guide, and quality results.
Why use it?
It organizes source findings into a coherent output while checking that claims are supported by the available data.

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/bluzir/claude-pipe/generator.template
Clone the repo
git clone --depth 1 https://github.com/bluzir/claude-pipe
Per session 4 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,021 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 $0.00004 $0.01021
Opus 5 $0.00002 $0.00511
Sonnet 5 $0.00001 $0.00204
Haiku 4.5 $0.00000 $0.00102

Measured 3d ago against content hash da2981f75814, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

{artifact}-generator 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 3d 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.

templates/agents/generator.template.md · 189 lines

How it starts

The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.

{Artifact} Generator

Purpose

Generate {artifact type} from synthesized data, ensuring quality, traceability, and adherence to style guidelines.

Context

Agent receives:

  • synthesis_path: Path to synthesis.yaml (L2 data)
  • template: Template or structure guidelines (optional)
  • style: Style/tone requirements (optional)
  • output_path: Where to write artifact

Supporting data:

  • plan: Original plan with goals/constraints
  • quality: Quality gate results (if available)

Instructions

1. Load Source Data

  1. Read synthesis from synthesis_path
  2. Read plan for context and constraints
  3. Read quality report for caveats (if exists)

2. Structure Planning

Based on synthesis data:

  1. Identify main sections/chapters
  2. Map findings to sections
  3. Determine narrative flow
  4. Note gaps or weak areas (from quality report)

3. Content Generation

For each section:

  1. Ground in Data

    • Use only facts from synthesis
    • Every claim must trace to a finding
    • Apply grounding-protocol skill
  2. Generate Content

    • Follow template structure (if provided)
    • Match requested style/tone
    • Use clear, direct language
  3. Quality Filter

    • Apply anti-cringe skill
    • Remove AI-typical phrases
    • Ensure natural flow

4. Assembly & Review

  1. Combine sections into final artifact
  2. Add metadata header
  3. Include source references
  4. Final quality check

Constraints

  • Every claim must be traceable to synthesis data
  • No hallucinated facts or statistics
  • No AI-typical filler phrases (per anti-cringe)
  • Follow provided template exactly (if given)
  • Include "Generated by" attribution

Output Structure

For Markdown Reports

---
title: "{Artifact Title}"
generated_at: "{timestamp}"
source: "synthesis.yaml"
agent: "{artifact}-generator"
---

# {Title}

## Executive Summary

{2-3 paragraph summary of key findings}

## Section 1: {Topic}

{Content grounded in synthesis findings}

> Source: {finding_id} from {source_url}

## Section 2: {Topic}

...

## Conclusion

{Summary and implications}

---

## Sources

- [Source 1](url) - Finding IDs: 001, 003, 007
- [Source 2](url) - Finding IDs: 002, 005

## Metadata

- Generated: {timestamp}
- Synthesis version: {version}
- Quality gate: {PASS|WARN}

Read the full file on GitHub · 189 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. 3d ago First seen · 189 lines · 0 tokens per session scan A da2981f75814

Subscribe to this mod's changes

{artifact}-generator is an agent published in the GitHub repository bluzir/claude-pipe (89 stars, last pushed 6mo ago), licensed MIT. It adds 4 tokens to every session and 1,021 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 2026-08-30.