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 skills add fending/context-engineering --skill onboardgit clone --depth 1 https://github.com/fending/context-engineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fending/context-engineering/onboard)<a href="https://agentmods.dev/skills/fending/context-engineering/onboard"><img src="https://agentmods.dev/badge/skills/fending/context-engineering/onboard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fending/context-engineering/onboard"><img src="https://agentmods.dev/badge/skills/fending/context-engineering/onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01059 |
| Opus 5.5 | $0.00014 | $0.00424 |
| Sonnet 5.5 | $0.00007 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
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 2mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard
Discover and summarize the project's context structure so you can orient quickly.
What This Skill Does
-
Discover context files at all levels:
- Global (user-level AGENTS.md or tool-specific equivalent like
~/.claude/CLAUDE.md) - Project root (
AGENTS.mdas primary, check forCLAUDE.mdsymlink) - Subdirectories (any nested
AGENTS.mdfiles) - Context directory (
context/or similar with multiple files) - Agent definitions (
examples/agents/,examples/agent-teams/) - Skills (
.claude/skills/or equivalent) - Hooks (
.claude/settings.jsonhook configurations)
- Global (user-level AGENTS.md or tool-specific equivalent like
-
Summarize each file with a one-line description and last modification date. Focus on what each file tells the AI to do differently -- boundaries, conventions, workflow instructions.
-
Flag potential staleness:
- References to packages not in
package.json/requirements.txt/go.mod - References to directories that don't exist on the filesystem
- Build or test commands that don't match actual scripts
- Agent state files with timestamps older than 30 days
- References to packages not in
-
Produce a context map -- a navigation guide that tells agents which files to read depending on the task, without front-loading everything. Group discovered files by when they're relevant:
- The root AGENTS.md covers boundaries, stack, and commands -- always applicable.
- Subdirectory AGENTS.md files apply when working in the directories they govern.
- Context directory files apply when investigating specific concerns (architecture, API surface, data model).
- Agent definitions and state files apply only when running those specific workflows.
The map is the last section of the output. It answers "what should I read before starting this task?" not just "what exists?"
Report findings conversationally. This is orientation, not an audit -- surface what matters for starting work, not an exhaustive inventory.
When to Use
- At the start of a new session on an unfamiliar project
- When a new contributor (human or AI) joins the project
- After major refactoring that may have changed project structure
- When you're unsure what context files exist or how they relate
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.
- 2mo ago First seen · 81 lines · 35 tokens per session scan A ea5e40486db9
onboard is a skill published in the GitHub repository fending/context-engineering (12 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 1,059 once invoked, about $0.0001 per session on Opus 5.5. 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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