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Xerxes-2/tavern2agent

Compile SillyTavern character cards into pi-native, event-driven interactive narrative runtimes.

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Verified commit 601b1c3f447ae5472e7bc5b59fc17771129293c1

119GitHub stars
0stars in 7 days
1skill in this repository
0commits in 30 days

Heat

0.4
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  • Star growth, last 24 hoursNot available
  • Rank movement, last 7 daysNot available
  • Commits, last 30 daysNot available
  • Accounts asking about it through MCP, last 7 daysNot available
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Heat never changes the order by stars, and sponsorship never touches it. How heat works

Stars over time

Oct 25, 2026Oct 28, 2026120

30-day change not yet available: not enough history.

Individual skills in this repository

How it works

tavern2agent

When your agent uses it

用户提供 SillyTavern 角色卡(PNG/JSON)并要求转换、迁移、移植到 pi coding agent 时使用;覆盖纯 prompt、世界书、MVU、骰子、战斗、好感度、经济、隐藏信息、多 agent 场景。

What it loads into context

Frontmatter

85 tokens

Always available to the agent

SKILL.md

3,216 tokens

When the skill is activated

Reference documents

46,563 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

  1. 开工
  2. 探索命令
  3. 信息源 → IR
  4. 方案
  5. 多 agent 判定
  6. Reference 路由
  7. 产出
  8. 硬约束
  9. 完工

Scripts executed separately

  • scripts/extract_card.py
  • scripts/get_entry.py
  • scripts/list_entries.py
  • scripts/start.sh

Files

  • .gitignoreasset29 bytes
  • LICENSEasset1,065 bytes
  • README.mddoc5,689 bytes
  • README_ZH.mddoc4,893 bytes
  • SKILL.mdcore11,825 bytes
  • SKILL.mdcore11,825 bytes
  • SKILL.mdcore11,825 bytes
  • docs/developing-cards.mddoc2,639 bytes
  • docs/epistemic-audit-2026-07.mddoc12,047 bytes
  • docs/tooling.mddoc1,713 bytes
  • references/card-ir.mddoc4,034 bytes
  • references/codeact.mddoc3,280 bytes
  • references/data-layer.mddoc4,517 bytes
  • references/decision-tree.mddoc4,799 bytes
  • references/design-principles.mddoc4,241 bytes
  • references/engineering-discipline.mddoc6,556 bytes
  • references/event-packs.mddoc5,141 bytes
  • references/evented-runtime.mddoc8,481 bytes
  • references/multi-agent-architecture.mddoc11,682 bytes
  • references/mvu-mapping.mddoc9,569 bytes
  • references/pi-integration.mddoc7,297 bytes
  • references/prompt-composition.mddoc10,119 bytes
  • references/script-analysis.mddoc2,499 bytes
  • references/setup.mddoc2,534 bytes
  • references/state-schema-migrations.mddoc2,771 bytes
  • references/storytelling.mddoc1,314 bytes
  • references/tool-abstraction.mddoc21,580 bytes
  • references/ts-engine.mddoc8,816 bytes
  • references/two-pass-rendering.mddoc11,692 bytes
  • references/validation.mddoc13,485 bytes
  • scripts/extract_card.pyruns9,752 bytes
  • scripts/get_entry.pyruns2,087 bytes
  • scripts/list_entries.pyruns3,242 bytes
  • scripts/start.shruns3,784 bytes
Read SKILL.md

# Tavern → Agent

把 SillyTavern 卡编译成 pi-native 互动叙事 runtime。还原作者想做的游戏:prompt 描述世界,领域事件改变世界,engine/reducer 维护正确性,session 保存存档。不复刻 ST 宏、COT、JSON Patch、HTML 状态栏,不把状态栏字段搬进 `patch_state`。

## 开工

1. 解包卡片,确认输出目录。目录存在时先问:覆盖、增量、另建?
2. 先读 `references/evented-runtime.md`、`references/card-ir.md`、`references/event-packs.md`、`references/design-principles.md`。
3. 全量审计 `data` 字段、世界书、TH scripts、regex scripts、开场白。
4. 先输出或草拟 `world-data/card-ir.json`;不要直接从原卡文本生成代码。
5. 从 IR 形成 Runtime Plan:archetype、event packs、state roots、visibility policy、fact sources、tool surface、subagent roles、prompt modules、validation plan。
6. 写代码前先给用户看 Runtime Plan;复杂卡还要给 state schema、event catalog、reducer/API 清单、事实源边界和子代理边界。

增量更新:先看 `git log -20` + `git diff`。只改本次需求相关文件;不碰 `sessions/`、`runtime/`、`.pi/agent/`。
View the complete source on GitHub

Skills included

  • tavern2agent

Repository facts

License
MIT
Created
May 10, 2026
Last pushed
Jul 11, 2026
Forks
8
Open issues
0
Project activity
Slowing

Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 6, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

Show 2 notes
  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
Show all 19 signals by file

README.md

  • Network accessline 34
    Using [`npx skills`](https://github.com/vercel-labs/skills) (recommended):
  • Network accessline 43
    git clone --depth 1 https://github.com/Xerxes-2/tavern2agent \
  • Network accessline 126
    This skill's methodology is continuously validated and refined in [fate-sandbox](https://github.com/lolo-s-Cosmos/fate-sandbox), a Type-Moon universe sandbox spanning 13 timelines, including FSN Fuyuki, strange Fake, Tsukihime, and The Gard

README_ZH.md

  • Network accessline 34
    用 [`npx skills`](https://github.com/vercel-labs/skills)(推荐):
  • Network accessline 43
    git clone --depth 1 https://github.com/Xerxes-2/tavern2agent \
  • Network accessline 126
    本技能的方法论在 [fate-sandbox](https://github.com/lolo-s-Cosmos/fate-sandbox) 上持续验证迭代——一个型月世界观沙盒,13 条世界线(FSN 冬木、strange Fake、月姬、空之境界等)。这里的两段式结算/渲染拆分、引擎台账、确定性 compaction、缓存友好渲染历史等经验均出自它的长跑实测。想看一个完整的 evented standard + 多 subagent + 两段式项目长什么样,读它的源码和 

SKILL.md

  • Broad permissionsline 1
    Bash, Read, Write, Edit, Glob, Grep

references/mvu-mapping.md

  • Network accessline 31
    - 框架 API / 事件 / `stat_data` 存储 / `parseMessage`:`https://github.com/StageDog/tavern_helper_template/raw/refs/heads/main/.cursor/rules/mvu变量框架.mdc`
  • Network accessline 32
    - schema 契约(zod 4、幂等增量解析、transform 软约束)/ 世界书条目结构(`[initvar]`、`变量列表`、`[mvu_update]变量更新规则/变量输出格式`):`https://github.com/StageDog/tavern_helper_template/raw/refs/heads/main/.cursor/rules/mvu角色卡.mdc`

references/script-analysis.md

  • Network accessline 47
    curl -sL '<URL>' | head -100

scripts/extract_card.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Possible obfuscationline 82
    return json.loads(base64.b64decode(texts[key]))
  • Possible obfuscationline 113
    return json.loads(base64.b64decode(b64).decode("utf-8"))

scripts/get_entry.py

  • Executable scriptline 1
    #!/usr/bin/env python3

scripts/list_entries.py

  • Executable scriptline 1
    #!/usr/bin/env python3

scripts/start.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Network accessline 6
    if ! command -v pi &>/dev/null; then   echo "错误: pi 未安装,请先安装 pi coding agent" >&2   echo "安装指引: https://github.com/earendil-works/pi-coding-agent" >&2
  • Network accessline 7
    echo "错误: pi 未安装,请先安装 pi coding agent" >&2   echo "安装指引: https://github.com/earendil-works/pi-coding-agent" >&2   exit 1
  • Network accessline 8
    echo "安装指引: https://github.com/earendil-works/pi-coding-agent" >&2

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Signal confidence: 19 facts

Works with

Agent compatibility, from the files in this repository
AgentStatusEvidenceInstall to
Claude CodeCompatibleStandard SKILL.md SKILL.md.claude/skills/<skill-name>/
CodexCompatibleStandard SKILL.md SKILL.md.agents/skills/<skill-name>/
CursorCompatibleStandard SKILL.md SKILL.md.cursor/skills/<skill-name>/
Gemini CLICompatibleStandard SKILL.md SKILL.md.gemini/skills/<skill-name>/
GitHub CopilotCompatibleStandard SKILL.md SKILL.md.github/skills/<skill-name>/
OpenCodeCompatibleStandard SKILL.md SKILL.md.opencode/skills/<skill-name>/
  • Native The repository ships a manifest, folder or frontmatter field for this agent.
  • Compatible A standard SKILL.md that this agent can read.
  • Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.