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/act-sdk/act-sdk-js/onboardnpx skills add act-sdk/act-sdk-js --skill onboardgit clone --depth 1 https://github.com/act-sdk/act-sdk-jsWrote 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/act-sdk/act-sdk-js/onboard)<a href="https://agentmods.dev/skills/act-sdk/act-sdk-js/onboard"><img src="https://agentmods.dev/badge/skills/act-sdk/act-sdk-js/onboard.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01742 |
| Opus 5 | $0.00014 | $0.00871 |
| Sonnet 5 | $0.00006 | $0.00348 |
| Haiku 4.5 | $0.00003 | $0.00174 |
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 5d 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.
This is a copy
91% identical to onboard — 17 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create or improve onboarding experiences that help users understand, adopt, and succeed with the product quickly.
Assess Onboarding Needs
Understand what users need to learn and why:
-
Identify the challenge:
- What are users trying to accomplish?
- What's confusing or unclear about current experience?
- Where do users get stuck or drop off?
- What's the "aha moment" we want users to reach?
-
Understand the users:
- What's their experience level? (Beginners, power users, mixed?)
- What's their motivation? (Excited and exploring? Required by work?)
- What's their time commitment? (5 minutes? 30 minutes?)
- What alternatives do they know? (Coming from competitor? New to category?)
-
Define success:
- What's the minimum users need to learn to be successful?
- What's the key action we want them to take? (First project? First invite?)
- How do we know onboarding worked? (Completion rate? Time to value?)
CRITICAL: Onboarding should get users to value as quickly as possible, not teach everything possible.
Onboarding Principles
Follow these core principles:
Show, Don't Tell
- Demonstrate with working examples, not just descriptions
- Provide real functionality in onboarding, not separate tutorial mode
- Use progressive disclosure - teach one thing at a time
Make It Optional (When Possible)
- Let experienced users skip onboarding
- Don't block access to product
- Provide "Skip" or "I'll explore on my own" options
Time to Value
- Get users to their "aha moment" ASAP
- Front-load most important concepts
- Teach 20% that delivers 80% of value
- Save advanced features for contextual discovery
Context Over Ceremony
- Teach features when users need them, not upfront
- Empty states are onboarding opportunities
- Tooltips and hints at point of use
Respect User Intelligence
- Don't patronize or over-explain
- Be concise and clear
- Assume users can figure out standard patterns
Design Onboarding Experiences
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.
- 5d ago First seen · 242 lines · 28 tokens per session scan A d3590b654e53
onboard is a skill published in the GitHub repository act-sdk/act-sdk-js (3 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 1,742 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to onboard, differing in 17 lines, and is treated as a copy.
Other skills, from other repositories
memora
Use when working with persistent memory across sessions, storing/retrieving knowledge, managing TODOs/issues, or when context from previous sessions would be helpful.
tsa-edit-then-verify
The full edit-and-verify loop mandated by CLAUDE.md and docs/agent-tooling-gap-report.md:58. Pre-edit gate (edit action=safe + baseline health action=file) → LLM edits → post-edit verify (health action=file diff + edit action=impact + scoped verificationcommand). Replaces "edit then run the whole pytest suite" (5 min)…
tsa-pr-review
AST-grounded PR / diff review. One workflow → per-file risk ranking, blast radius per changed symbol, the exact pytest command to gate merge, any architecture-constraint violations, and a final BLOCK / REVIEW / APPROVE verdict — in 1–2k tokens and 4–6 MCP calls. Goes beyond a generic LLM diff-read because only TSA's…
tsa-refactor-queue
Build a top-N prioritized refactoring queue by intersecting three signals: health grade (which files are F/D), temporal churn (which files change most often), and dead-code density (which files carry the most unreachable symbols). For each candidate the queue surfaces (a) the dimension that dragged the grade down, (b)…
tsa-constraints
Architectural constraint enforcement. Detect forbidden cross-module calls ("MCP must not depend on CLI") at index time and gate edits on them. Rules live in YAML at repo root; violations bubble up through edit action=safe and edit action=impact as UNSAFE verdicts. Use when: User asks "does this PR break architecture?"…
tsa-graph
Code archaeology via call graph + symbol resolution. Answer "who calls X", "what does Y call", "where is Z defined", "what's the path from A to B" in one MCP call instead of multi-step grep + read. Uses persisted cross-file resolution (Synapse) so cross-module edges are precise, not regex-guessed. Use when: User asks…