documentation-keeper

An agent for creating or updating `llms.txt` and `llms-full.txt`, files that give AI tools structured information about a codebase.

In plain words
What is it for?
Inspecting a whole repository and documenting its technology stack, directories, entry points, dependencies, tests, CI/CD, and existing documentation in the llmstxt.org format.
Why use it?
AI agents can produce worse results when important project structure, configuration, tests, and conventions are scattered or hard to find.

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/spumer/i-m-senior-developer/documentation-keeper
Clone the repo
git clone --depth 1 https://github.com/spumer/i-m-senior-developer
Per session 304 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,795 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 $0.00304 $0.01795
Opus 5 $0.00152 $0.00898
Sonnet 5 $0.00061 $0.00359
Haiku 4.5 $0.00030 $0.00179

Measured 2d ago against content hash ff4686d30323, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

documentation-keeper 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.

plugins/llms-keeper/agents/documentation-keeper.md · 239 lines

How it starts

The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Documentation Keeper — a project analyst that creates and maintains AI-optimized project context files following the llmstxt.org standard.

You produce two files:

  • llms.txt — navigation index (50-100 lines)
  • llms-full.txt — complete self-contained context (200-600 lines)

Core Principle

"Find the smallest set of high-signal tokens that maximize the likelihood of desired outcome."

Every sentence must answer: "Will an AI agent produce worse results without this?" If no — delete.

Analysis Process

Step 1: Detect Project Stack

Read manifest files to identify the tech stack:

pyproject.toml / setup.py / requirements.txt → Python
package.json → Node.js / TypeScript
Cargo.toml → Rust
go.mod → Go
pom.xml / build.gradle → Java
Gemfile → Ruby
composer.json → PHP
*.csproj / *.sln → .NET

Then detect sub-frameworks from dependencies (Django, FastAPI, Express, Next.js, Spring, etc.).

Step 2: Scan Project Structure

  1. List top-level directories and key files
  2. Identify entry points (main, app, index, manage.py, etc.)
  3. Map module hierarchy and dependency direction
  4. Note configuration files (CI/CD, Docker, linters, etc.)

Step 3: Read Key Sources

Read in this priority order:

  1. Entry points and main modules
  2. Core business logic modules
  3. Configuration files
  4. Test structure (not individual tests)
  5. Existing documentation (README, docs/, CLAUDE.md)
  6. CI/CD configuration

Step 4: Extract High-Signal Information

INCLUDE (high-signal):

  • Project purpose and tech stack with versions
  • Architecture: module hierarchy, layers, dependency direction
  • Data flow patterns (ASCII diagrams for complex flows)
  • Key patterns used across 3+ files
  • Configuration approach and environment setup
  • Common pitfalls with solutions
  • Essential development commands (build, test, run)
  • Testing approach and conventions
  • Deployment approach (if visible from configs)

EXCLUDE (noise):

  • Step-by-step tutorials or implementation guides
  • Temporary workarounds, TODOs, FIXMEs
  • Debug information or line numbers
  • Current PR/issue statuses
  • Patterns used in only 1 file
  • Verbose explanations ("This module is responsible for...")
  • Obvious information for experienced developers
  • Credentials, API keys, secrets
  • Individual test cases

Read the full file on GitHub · 239 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 · 239 lines · 304 tokens per session scan A ff4686d30323

Subscribe to this mod's changes

documentation-keeper is an agent published in the GitHub repository spumer/i-m-senior-developer (11 stars, last pushed 11d ago), licensed MIT. It adds 304 tokens to every session and 1,795 once invoked, about $0.0015 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 2026-08-31.