Borrowing it
Nothing to install: this file belongs to suisya-systems/claude-org-ja. 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/suisya-systems/claude-org-ja/main/.claude/skills/dispatcher-resume/SKILL.mdgit clone --depth 1 https://github.com/suisya-systems/claude-org-jaWrote 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/suisya-systems/claude-org-ja/dispatcher-resume)<a href="https://agentmods.dev/skills/suisya-systems/claude-org-ja/dispatcher-resume"><img src="https://agentmods.dev/badge/skills/suisya-systems/claude-org-ja/dispatcher-resume/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/suisya-systems/claude-org-ja/dispatcher-resume"><img src="https://agentmods.dev/badge/skills/suisya-systems/claude-org-ja/dispatcher-resume.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 | $0.00146 | $0.09970 |
| Opus 5 | $0.00073 | $0.04985 |
| Sonnet 5 | $0.00029 | $0.01994 |
| Haiku 4.5 | $0.00015 | $0.00997 |
Grade A, and why
dispatcher-resume 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.
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dispatcher-resume: ディスパッチャーの復帰
/dispatcher-handover で書き出した .state/dispatcher-handover.md を読み込み、
ディスパッチャーとして最低限の自覚(組織員としての立ち位置・進行中の派遣・監視
対象ワーカー)を復元し、/loop 3m の worker monitoring を再開する。
前提:
- ワーカー / 窓口 / キュレーターのペインは前 session から生きたまま残っている。 新たに spawn しない(/org-start ではない)。
- 自ペイン(name=
dispatcher)も生きている。secretary がsend_keysで/clear→/dispatcher-resumeを打鍵した直後の状態。pane_id / peer_id は 変わっていないはずだが、必ず観測して state.db を atomic 更新する。- state DB (
.state/state.db) はそのまま使う。- 監視 gap を埋める内部状態ファイル(
.state/dispatcher-event-cursor.txt(backend session に束縛された poll_events cursor) /.state/dispatcher/worker-idle-state.json/.state/dispatcher/curate-inflight.json(存在時) /.state/pending_decisions.json)は 前 session から残っている。新規作成・初期化しない(既存値からそのまま継続)。- handover ファイルが存在しないか古すぎる場合は、
/org-startを案内して停止する。
輸送層(transport)両系 — 既定
broker/ opt-inrenga: 本ファイル(および各スキル)のmcp__org-broker__*呼び出しは 既定broker(ORG_TRANSPORT無設定)で書いてあり、そのまま従えばよい(既定挙動)。ORG_TRANSPORT=renga(opt-in・切戻し可)では MCP サーバー名がrenga-peersになり、ツールの 完全修飾名がmcp__org-broker__*→mcp__renga-peers__*に機械置換される(引数形・セマンティクスは同一なので手順の論理は変わらない)。輸送依存で手順が変わる点だけ renga 併記する:
- 受信モデル: 既定 broker は push 一次(各ペイン同居の channel sidecar
server:org-broker-channelが broker キューを ~1 秒間隔で claim→notifications/claude/channelで idle セッションへ本文注入。pull = ナッジ +check_messagesは sidecar 不在 / unhealthy / channel 非対応ペイン(codex pull-peer)/ claude.ai login 不在時のフォールバック層)。ORG_TRANSPORT=renga時は dispatcher / worker メッセージが<channel source="renga-peers" …>として in-band で push される。- spawn 儀式: 既定 broker は
--mcp-config <broker>注入による Claude Code folder-trust プロンプトのsend_keys(enter=true)機械承認に加え、push 一次のため channel sidecar を--dangerously-load-development-channels server:org-broker-channelで load し dev-channel 承認プロンプトをsend_keys(enter=true)で機械承認する(2 段承認)。ORG_TRANSPORT=renga時は--dangerously-load-development-channels server:renga-peersの「Load development channel?」を Enter 承認する 1 段。- エラー分岐: 既定 broker は shared codes(
pane_not_found/last_pane/invalid-params)に加え broker 固有[token_invalid]/[session_invalid]/[tool_not_authorized]/[no_backend](= adapter_unavailable)/[nudge_failed]/[peer_not_found]/[name_taken]を返しうる(未知コードは default-branch で escalate)。ORG_TRANSPORT=renga時は broker 固有コードは発生しない。
new_tab/focus_paneは broker surface に無い(意図的除外)。契約面の正本はdocs/contracts/backend-interface-contract.mdSurface 8 + push-primary amendment(broker push 一次が 既定の契約、pull は fallback として retain)。opt-inrengaは削除せず常時有効な切戻しの安全装置として維持する。broker 実走(dogfood)は Epic #6 Issue G スコープで本ファイルの既定運用経路ではない(二フレーム注記(Refs #604): ここの「既定broker」はコード既定(tools/transport.py: DEFAULT_TRANSPORT、生成面はこれで render)。運用既定は broker dogfood が Epic #6 Issue G まで未活性のためrengaで、両者は指す対象が異なり矛盾しない。総説は rootCLAUDE.md。)
What ships with it
1 file 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.
- 12d ago First seen · 437 lines · 146 tokens per session scan A 9c609ba01158
dispatcher-resume is a skill published in the GitHub repository suisya-systems/claude-org-ja (5 stars, last pushed today), licensed MIT. It adds 146 tokens to every session and 9,970 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 2026-08-31.
Other skills, from other repositories
takt
A workflow engine that runs a task through a sequence of steps defined in a YAML file, using separate coding agents for the work.
takt
A workflow engine for coordinating multiple coding agents. It reads a YAML workflow, a text configuration format, and moves agents through its defined steps and rules.
company-product-context
Compiles comprehensive company product context from PDF documents, web research, and industry knowledge.
codebase-context-extractor
This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.
skill-creator
Generates Anthropic Skills with complete workflow including GitHub PR creation and local download verification.
Research Synthesis Workflow
A step-by-step guide to synthesizing research from multiple sources into a coherent summary.