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 skills/codename-11/subframe/onboardnpx skills add Codename-11/SubFrame --skill onboardgit clone --depth 1 https://github.com/Codename-11/SubFrameWhat 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 | $0.00032 | $0.01766 |
| Opus 5 | $0.00016 | $0.00883 |
| Sonnet 5 | $0.00006 | $0.00353 |
| Haiku 4.5 | $0.00003 | $0.00177 |
Grade A, and why
onboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SubFrame Onboard
Analyze an existing project and bootstrap SubFrame-compatible output files: .subframe/STRUCTURE.json, .subframe/PROJECT_NOTES.md, and initial sub-tasks.
Dynamic Context
Root directory listing:
!ls -la
Package manifest:
!cat package.json 2>/dev/null || cat pyproject.toml 2>/dev/null || cat Cargo.toml 2>/dev/null || echo "No package manifest found"
Project overview:
!head -100 README.md 2>/dev/null || echo "No README found"
AI configuration (Codex):
!head -50 AGENTS.md 2>/dev/null || echo "No AGENTS.md found"
AI configuration (Gemini):
!head -50 GEMINI.md 2>/dev/null || echo "No GEMINI.md found"
Source file survey:
!find . -maxdepth 2 -name "*.ts" -o -name "*.tsx" -o -name "*.py" -o -name "*.rs" -o -name "*.go" -o -name "*.java" -o -name "*.rb" 2>/dev/null | head -50
Existing SubFrame state:
!cat .subframe/STRUCTURE.json 2>/dev/null || echo "No STRUCTURE.json yet"
Instructions
Argument: `$ARGUMENTS`
Dry-Run Mode
If `$ARGUMENTS` contains `--dry-run`, do not write any files. Instead, show the full output that would be written for each file, clearly labeled with the target path. Then stop.
Step 1: Analyze the Project
Using the gathered dynamic context, determine:
- Project type — What kind of project is this? (web app, CLI tool, library, monorepo, etc.)
- Language and framework — Primary language, framework, and build tooling
- Architecture — Entry points, module structure, process model (single, client-server, microservices, etc.)
- Key modules — Identify the most important source files and their purposes (scan up to 3 directory levels deep)
- Existing documentation — What context already exists in README, AGENTS.md, GEMINI.md, or other docs?
- Dependencies — Key runtime and dev dependencies from the package manifest
Step 2: Generate STRUCTURE.json
Build a SubFrame-compatible `STRUCTURE.json` following this schema:
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 · 215 lines · 32 tokens per session scan A ca6eb4c8bea4
onboard is a skill published in the GitHub repository Codename-11/SubFrame (10 stars, last pushed 8d ago), licensed MIT. It adds 32 tokens to every session and 1,766 once invoked, about $0.0002 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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