Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. 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/mtarcure/claude-vibe-squad/main/.agents/skills/player-engagement-psychology/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/player-engagement-psychology)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/player-engagement-psychology"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/player-engagement-psychology/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/mtarcure/claude-vibe-squad/player-engagement-psychology"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/player-engagement-psychology.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 | $0.00076 | $0.00284 |
| Opus 5 | $0.00038 | $0.00142 |
| Sonnet 5 | $0.00015 | $0.00057 |
| Haiku 4.5 | $0.00008 | $0.00028 |
Grade A, and why
player-engagement-psychology 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
Player Engagement Psychology
Design motivation and retention honestly: intrinsic drivers, flow, and reward schedules that respect the player rather than exploit them.
Steps
- Identify the player's intrinsic motivators (autonomy, competence, relatedness) the design should serve.
- Design for flow: match challenge to skill, give clear goals and immediate feedback, and avoid boredom/anxiety edges.
- Choose reward structure and cadence deliberately; document any variable-reward or retention mechanic and its intent.
- Flag any dark pattern (manipulative FOMO, predatory monetization, addictive dark loops) for review — do not ship it silently.
- Define the engagement signals to measure and the ethical line the design will not cross.
Acceptance
- Engagement is grounded in named intrinsic motivators and flow, not just extrinsic hooks.
- Reward/retention mechanics are documented with intent.
- Any manipulative/dark pattern is flagged for review, never shipped silently.
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 · 22 lines · 76 tokens per session scan A 2b50997da243
player-engagement-psychology is a skill published in the GitHub repository mtarcure/claude-vibe-squad (152 stars, last pushed 3d ago), licensed MIT. It adds 76 tokens to every session and 284 once invoked, about $0.0004 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
contributor-onboarding
Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
recording
Capture screenshots on registered computers, record on macOS or HarmonyOS, and manage saved captures. Probe capabilities before recording.