Vellum Assistant is a personal AI assistant that remembers information about users, learns their preferences, and takes actions across connected apps. It is intended for people who want an assistant that can manage conversations, unfinished work, and proactive notifications over time. The catalogue skills, hooks, instruction, and setting configure or extend how the assistant works.
Borrowing it
Nothing to install: this file belongs to vellum-ai/vellum-assistant. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vellum-ai/vellum-assistant/main/.cursor/skills/vellum-pr-readiness/SKILL.mdgit clone --depth 1 https://github.com/vellum-ai/vellum-assistantWrote 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/vellum-ai/vellum-assistant/vellum-pr-readiness)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/vellum-pr-readiness"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/vellum-pr-readiness/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/vellum-ai/vellum-assistant/vellum-pr-readiness"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/vellum-pr-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00467 |
| Opus 5 | $0.00028 | $0.00234 |
| Sonnet 5 | $0.00011 | $0.00093 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
vellum-pr-readiness 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 12d 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
Vellum PR Readiness
Goal
Confirm the branch is reviewable, scoped, and verified before opening a PR. Prefer surfacing blockers over polishing summaries.
Checklist
- Inspect
git statusand identify unrelated changes, generated artifacts, deleted files, and untracked files. - Inspect staged and unstaged diffs before recommending a commit or PR.
- Check for secrets:
- Do not commit
.env, credentials, tokens, private keys, or local workspace data. .env.exampleis allowed only for placeholder values.
- Do not commit
- Check scope:
- If the branch is too large, suggest splitting it into smaller, reviewable PR branches.
- Separate unrelated UI, backend, migration, and infra changes when practical.
- Check required follow-ups:
- Migration needed for persisted data or workspace format changes.
- Docs needed for significant architecture, service, or data-flow changes.
- Companion
vellum-assistant-platformPR needed for platform-affecting contracts or new feature flags.
- Check Linear conventions:
- Branch, commit body, and PR body should include the Linear issue ID when one exists.
- Use
Closes JARVIS-123for single final PRs. - Use
Part of JARVIS-123for intermediate PRs in multi-PR plans.
- Check verification:
- Focused tests ran for changed behavior.
- Typecheck ran when exported contracts or cross-package types changed.
- Any skipped tests are explicitly called out.
PR Body Template
Use this compact structure unless the user asks for another format:
## Summary
- ...
## Test Plan
- ...
## Risk
- ...
Mention migrations, feature flags, rollout state, and companion PRs when relevant.
Human Attention Comments
For non-routine changes, leave a PR comment calling out review focus and risk level. Skip this for routine low-risk changes.
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.
- 12d ago First seen · 55 lines · 57 tokens per session scan A 40c0d3538537
vellum-pr-readiness is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,234 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 467 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
bootstrap-repo-analysis
First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the…
continual-learning
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt…
oracle
Oracle second-model review: bundle prompts/files, debug, refactor, design.
verify-implementation
A workflow that runs a project’s verification skills to produce a report on coding patterns, architecture rules, and project conventions. It is intended for work after implementation, before a pull request, or during code review.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
frontend-code-review
Trigger when the user requests a review of frontend files (e.g., .tsx, .ts, .js). Support both pending-change reviews and focused file reviews while applying the checklist rules.