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 anton-abyzov/vskill --skill survey-passinggit clone --depth 1 https://github.com/anton-abyzov/vskillWrote 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/anton-abyzov/vskill/survey-passing)<a href="https://agentmods.dev/skills/anton-abyzov/vskill/survey-passing"><img src="https://agentmods.dev/badge/skills/anton-abyzov/vskill/survey-passing/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/anton-abyzov/vskill/survey-passing"><img src="https://agentmods.dev/badge/skills/anton-abyzov/vskill/survey-passing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 95 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00141 | $0.02677 |
| Opus 5 | $0.00071 | $0.01339 |
| Sonnet 5 | $0.00028 | $0.00535 |
| Haiku 4.5 | $0.00014 | $0.00268 |
Grade A, and why
survey-passing 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 10d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Survey Passing
Find and complete paid expert surveys/consultations from expert network platforms. The goal: meet expectations, be consistent, earn the reward, save the user time.
The user is a real domain expert who receives paid survey invitations. This skill automates the mechanical part — clicking through forms, selecting radio buttons, typing answers — using the user's genuine professional background from USER.md and MEMORY.md. The expertise is real; only the form-filling is automated.
Workflow
Step 1: Find the Survey
Option A — Check email for invitations: Search the user's Gmail for survey invitations using an authenticated method:
- Gmail MCP tools (preferred if available): Use
gmail_search_messageswith queries below - Persistent Chrome profile (fallback): Open Gmail via the user's profile. Refer to TOOLS.md for port config
Search strategies:
subject:reward surveysubject:$50 OR subject:$65 OR subject:$100from:arbolus OR from:guidepoint OR from:glgsubject:canopysubject:honorarium OR subject:compensation
Look for unread emails with dollar amounts. Open the email, find the survey/canopy link, click through.
Option B — Direct URL: If the user provides a survey URL directly, skip email search and proceed to Step 2.
Step 2: Pre-Flight Checks
Before attempting any survey, verify:
- No video/audio requirement — check for "microphone", "camera", "record", "video response" keywords. These cannot be completed by the agent. Alert the user instead.
- No AI-detection disclaimer — if the survey warns "responses generated using ChatGPT will be rejected" or similar, flag it to the user and let them decide.
- Save the survey URL — store it before starting, in case you need to recover from a timeout.
- Read the user's profile — load USER.md and MEMORY.md for personal/professional details.
- Check survey-log.md — review previous surveys for persona consistency.
Step 3: Complete the Survey
What ships with it
7 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.
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.
- 10d ago First seen · 282 lines · 141 tokens per session scan A d2a095ba74e1
survey-passing is a skill published in the GitHub repository anton-abyzov/vskill (45 stars, last pushed 3d ago), licensed MIT. It adds 141 tokens to every session and 2,677 once invoked, about $0.0007 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.
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