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.
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 vellum-ai/vellum-assistant --skill influencergit 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/influencer)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/influencer"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/influencer/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/influencer"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/influencer.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 | $0.00019 | $0.00898 |
| Opus 5 | $0.00010 | $0.00449 |
| Sonnet 5 | $0.00004 | $0.00180 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
influencer 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 8d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use browser automation for collection and host_bash helper scripts for deterministic parsing, scoring, and comparison. All browser operations are executed through the assistant browser CLI, invoked via host_bash.
Required tools
host_bashforassistant browserCLI commands and helper scripts inscripts/.
Step graph (state machine)
Step 1: Route intent
Use deterministic routing when intent is unclear:
bun {baseDir}/scripts/influencer-intent.ts --request "<latest user request>" --has-candidates <true|false> --has-shortlist <true|false>
Use returned step to route to discover, enrich_profile, or compare_shortlist.
Step 2: Discover candidates (discover)
- Navigate to keyword search/post surfaces.
- Snapshot + extract:
assistant browser --session influencer --json snapshot
assistant browser --session influencer --json extract --include-links
- Parse candidates:
bun {baseDir}/scripts/influencer-parse-candidates.ts --platform instagram --input-json '<json payload with extracted text/links>'
TikTok
- Navigate to user search page for query.
- Use
assistant browser --session influencer scroll+assistant browser --session influencer wait-forto load additional candidates. - Extract and parse:
bun {baseDir}/scripts/influencer-parse-candidates.ts --platform tiktok --input-json '<json payload with extracted text>'
X/Twitter
- Navigate to people search view (
f=user). - Snapshot + extract:
assistant browser --session influencer --json snapshot
assistant browser --session influencer --json extract --include-links
- Parse:
bun {baseDir}/scripts/influencer-parse-candidates.ts --platform twitter --input-json '<json payload with extracted text/links>'
Step 3: Enrich profiles (enrich_profile)
For each selected candidate profile:
- Navigate to profile URL.
- Snapshot + extract profile metadata (bio, follower counts, verification indicators).
- Score with criteria:
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 1.4 KB
- scripts/__fixtures__/instagram-sample.txt 43 B
- scripts/__fixtures__/twitter-sample.txt 39 B
- scripts/__tests__/influencer-compare.test.ts 859 B runs code
- scripts/__tests__/influencer-intent.test.ts 841 B runs code
- scripts/__tests__/influencer-parse-candidates.test.ts 1.2 KB runs code
- scripts/__tests__/influencer-score.test.ts 1.1 KB runs code
- scripts/__tests__/influencer-theme-extract.test.ts 427 B runs code
- scripts/influencer-compare.ts 2.0 KB runs code
- scripts/influencer-intent.ts 3.0 KB runs code
- scripts/influencer-parse-candidates.ts 6.3 KB runs code
- scripts/influencer-score.ts 4.6 KB runs code
- scripts/influencer-theme-extract.ts 1.8 KB runs code
- scripts/lib/common.ts 3.0 KB runs code
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.
- 8d ago Changed · -5 lines 006a7c4e3bff
- 9d ago First seen · 139 lines · 19 tokens per session scan A 677052ca57d5
influencer is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,234 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 898 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 2026-09-03.
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