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.
npx agentmods add agents/spumer/i-m-senior-developer/documentation-keepergit clone --depth 1 https://github.com/spumer/i-m-senior-developerWhat 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 | $0.00304 | $0.01795 |
| Opus 5 | $0.00152 | $0.00898 |
| Sonnet 5 | $0.00061 | $0.00359 |
| Haiku 4.5 | $0.00030 | $0.00179 |
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.
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
- List top-level directories and key files
- Identify entry points (main, app, index, manage.py, etc.)
- Map module hierarchy and dependency direction
- Note configuration files (CI/CD, Docker, linters, etc.)
Step 3: Read Key Sources
Read in this priority order:
- Entry points and main modules
- Core business logic modules
- Configuration files
- Test structure (not individual tests)
- Existing documentation (README, docs/, CLAUDE.md)
- 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
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.
- 2d ago First seen · 239 lines · 304 tokens per session scan A ff4686d30323
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.
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