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/agentlas-ai/agentlas-desktop/draftnpx skills add agentlas-ai/agentlas-desktop --skill draftgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWhat 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 | $0.00015 | $0.00120 |
| Opus 5 | $0.00008 | $0.00060 |
| Sonnet 5 | $0.00003 | $0.00024 |
| Haiku 4.5 | $0.00002 | $0.00012 |
Grade A, and why
draft 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.
What it actually says
Steps
- Create
plugin.json, a human README, a router skill, one skill per workflow, and a letter-mark icon. - Write every file as UTF-8 without a byte-order mark.
- Add transport integrity hashes for every non-manifest package file.
Outputs
- A staged plugin package ready for the canonical gate.
Verification
- The generated manifest parses on its first read.
- The package contains no
.state/data or credential values.
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 · 20 lines · 15 tokens per session scan A da2cbaf98545
draft is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (5 stars, last pushed 3d ago), licensed Apache-2.0. It adds 15 tokens to every session and 120 once invoked, about $0.0001 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.
Other skills, from other repositories
skills-cli-sync
Sync skills-desktop's AGENTDEFINITIONS and UNIVERSALAGENTIDS with the upstream vercel-labs/skills CLI, then update docs and validate. Use when bumping SKILLSCLIVERSION, mirroring a new upstream skills-CLI release, or when the agent list / universal set drifts from upstream agents.ts.
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
update-codex-sdk
Update VS Code's bundled @openai/codex dependency to a version available from the private VS Code npm feed, regenerate its protocol client, run the relevant tests, and verify the Codex Agent Host in a launched Code OSS window. Use for bundled Codex SDK/CLI version bumps in the VS Code repository.
customizations-in-the-agent-host
Architecture and hard-won debugging lessons for customization enablement (plugins, MCP servers, agents, skills, instructions) in the agent host. Use when changing how customizations are discovered, published, enabled/disabled, or handed to a provider SDK; when a customization shows the wrong enabled state in the UI…
create-skill
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
assistant-migration
Migrate from ChatGPT, Claude, OpenClaw, Hermes, Manus, and other AI assistants into Vellum by inspecting their data exports, conversation archives, files, prompts, custom instructions, memory, saved memories, tools, GPTs, workflows, integrations, and relationships, then mapping as much as safely possible into Vellum…