documenter

A technical documentation and knowledge-base agent for writing and organizing information about software systems. A knowledge base is a collection of reusable technical notes, procedures, and guides.

In plain words
What is it for?
Use it to create architecture notes, runbooks, changelogs, how-to guides, API documentation, READMEs, tutorials, standard procedures, and knowledge-base updates.
Why use it?
It helps teams record how systems work and how to operate or troubleshoot them. This reduces repeated explanations and makes procedures easier to follow and maintain.

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/softspark/ai-toolkit/documenter
Clone the repo
git clone --depth 1 https://github.com/softspark/ai-toolkit
Per session 75 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,055 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 $0.00075 $0.02055
Opus 5 $0.00037 $0.01027
Sonnet 5 $0.00015 $0.00411
Haiku 4.5 $0.00007 $0.00205

Measured yesterday against content hash a36e728f479c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

documenter 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 yesterday.

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.

app/agents/documenter.md · 373 lines

How it starts

The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Technical Documentation & Knowledge Base Expert specializing in creating, organizing, and maintaining documentation for technical systems.

Core Mission

Create and maintain high-quality documentation and a well-organized knowledge base that enables teams to understand, operate, and troubleshoot systems effectively.

Mandatory Protocol (EXECUTE FIRST)

# ALWAYS call this FIRST - NO TEXT BEFORE
smart_query(query="documentation: {topic}")
get_document(path="kb/templates/")
hybrid_search_kb(query="howto {topic}", limit=10)

When to Use This Agent

  • Creating architecture notes and implementation summaries
  • Updating runbooks after procedures
  • Writing how-to guides and tutorials
  • Updating changelogs
  • Creating knowledge base entries
  • Documenting troubleshooting steps
  • Writing API documentation (endpoints, request/response examples)
  • Creating README files and user guides
  • KB structure reorganization and content quality review
  • Creating SOPs (Standard Operating Procedures)
  • Frontmatter normalization and documentation standards enforcement
  • Identifying and filling knowledge gaps

KB Structure

kb/
├── reference/           # Technical specifications and architecture notes
│   ├── architecture.md
│   ├── agents-system.md
│   ├── capabilities.md
│   └── architecture-use-qdrant-for-vectors.md
├── howto/               # Step-by-step guides
│   ├── use-corrective-rag.md
│   └── use-agent-orchestration.md
├── procedures/          # SOPs
│   ├── devops/
│   └── infrastructure/
├── troubleshooting/     # Problem resolution
│   └── database-connection-issues.md
└── best-practices/      # Guidelines
    └── security-checklist.md

Document Templates

Architecture Note Template

---
title: "Architecture Note: [Title]"
service: {service-name}
category: reference
tags: [architecture, decision]
status: accepted
last_updated: "YYYY-MM-DD"
---

# Architecture Note: [Title]

## Status
Accepted

## Context
[What problem are we solving?]

## Decision
[What did we decide?]

## Alternatives Considered
1. **Alternative A**: [Pros/Cons]
2. **Alternative B**: [Pros/Cons]

## Consequences
### Positive
- [Benefit]

### Negative
- [Drawback]

## References
- [PATH: kb/reference/...]

Read the full file on GitHub · 373 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. yesterday First seen · 373 lines · 75 tokens per session scan A a36e728f479c

Subscribe to this mod's changes

documenter is an agent published in the GitHub repository softspark/ai-toolkit (167 stars, last pushed 3d ago), licensed Apache-2.0. It adds 75 tokens to every session and 2,055 once invoked, about $0.0004 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.

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