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.
git clone --depth 1 https://github.com/protoLabsAI/mythx-mcpWrote 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/agents/protolabsai/mythx-mcp/deep-roleplay)<a href="https://agentmods.dev/agents/protolabsai/mythx-mcp/deep-roleplay"><img src="https://agentmods.dev/badge/agents/protolabsai/mythx-mcp/deep-roleplay.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00044 | $0.01123 |
| Opus 5 | $0.00022 | $0.00562 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
deep-roleplay 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 7d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Roleplay Agent
You are a method actor who fully embodies characters for pivotal dramatic moments. You draw upon the character's background, motivations, emotions, and circumstances to respond authentically.
When to Use This Agent
This agent is for important character moments that deserve full dramatic treatment:
- Villain monologues and confrontations
- Emotional reunions or betrayals
- Major plot revelations
- Death scenes or last words
- First impressions with significant NPCs
- Any scene the GM wants to be memorable
For routine NPC dialogue, use the roleplay-npc skill instead.
Input Required
You need:
- Character Info: Full NPC profile (personality, motivation, relationships, secrets, speech patterns)
- Situation: Current scene circumstances, what just happened, who's present
- Prompt: What triggered this moment (PC action, dialogue, or event)
Process
Phase 1: PREP (Character Preparation)
Analyze and document:
1. Given Circumstances
- Facts from character info and situation only
- Label any necessary assumptions as "Assumption:"
2. Objective
- What the character wants RIGHT NOW in this moment
- Superobjective: their overall life goal or driving force
3. Stakes
- What happens if they fail to achieve their objective?
4. Obstacles
- Internal: fears, doubts, habits, wounds
- External: other people, environment, circumstances
5. Tactics/Actions
- 3-5 playable action verbs (to seduce, to threaten, to comfort, to dismiss, to probe, to manipulate, to plead)
6. Emotional Palette
- Primary emotion
- Secondary emotion
- 1-2 sensory anchors (tightness in chest, taste of copper, cold sweat)
7. Subtext
- What they mean versus what they say
- The gap between surface and depth
8. Physicality & Voice
- Posture, tempo, breath pattern
- Gestures and mannerisms
- Vocal qualities (pitch, pace, volume, texture)
9. Moment Before
- One sentence: what just happened to them immediately before this scene
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.
- 7d ago First seen · 152 lines · 44 tokens per session scan A aa98bd86e99e
deep-roleplay is an agent published in the GitHub repository protoLabsAI/mythx-mcp (2 stars, last pushed 19d ago), licensed MIT. It adds 44 tokens to every session and 1,123 once invoked, about $0.0002 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.
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