GitHub repository access in containerized environments using REST API and credential detection. Use when git clone fails, or when accessing private repos/writing files via API.
日本語の概要は準備中です。原文の説明を表示しています。
Convenes expert panels for problem-solving. Use when user mentions panel, experts, multiple perspectives, MECE, DMAIC, RAPID, Six Sigma, root cause analysis, strategic decisions, process improvement, or asks for philosophers/ancients (Socratic, Aristotelian, Stoic method experts).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill hand-rolls subagent orchestration via raw Anthropic API calls. A managed runtime now does the same job. Which one to use depends on your surface:
/deep-research, trigger a run with the workflow keyword, set
/effort ultracode, or spawn Task subagents — do that instead. The runtime gives
16-concurrent / 1000-agent ceilings, an approval gate, adversarial cross-review,
and in-session resume that this skill would otherwise reimplement badly. Dynamic
workflows shipped in research preview (Claude Code v2.1.154+, 2026).muninn_utils.dispatch
(17 pre-built lenses) already implements this panel over orchestrating-chat-agents' execution
model — prefer it over rebuilding the panel by hand. Proceed below.Discriminator: do you have a native subagent/Task tool or a workflow command? Yes → native. No → this skill. Never reimplement the runtime where it already exists.
Convene domain experts and methodological specialists to solve problems through multi-round collaborative discussion. Experts build on each other's insights, challenge assumptions, and synthesize recommendations.
For simpler problems requiring multiple viewpoints:
For complex problems requiring collaborative reasoning:
Available expertise spans:
See references/msd-domain-experts.md, references/consulting-frameworks.md, and references/ancient-philosophers.md for complete role catalogs.
Claude loads relevant references based on problem domain.
Claude selects 3-5 experts based on problem characteristics:
Problem type → Primary expert + Supporting experts
Name the panel up front, then give each expert's view before synthesizing.
Single-round: list the panel members, then each expert's independent analysis (in any clear order — parallel, not sequential), then a synthesis with attribution.
Multi-round: initial analysis from each expert, then a round where experts respond to specific points from named others (building on or challenging them), then convergence — resolving disagreements or flagging which remain productive — and a final synthesis. Skip rounds that would add nothing (e.g. no round 3 if round 2 already converged).
Headers separating experts and rounds help the reader; the exact heading text doesn't matter.
Domain Experts:
Philosopher Experts:
Framework Experts:
Cross-Panel Interaction:
Disagreement Handling:
When panel must recommend action:
RAPID (Bain)
Weighted Decision Matrix
Risk-Benefit Analysis
Apply MSD-specific context automatically:
Technical constraints:
Business context:
Cultural factors:
User: Our new assay is showing high background signal in serum samples
Claude convenes:
- Assay Scientist (primary)
- Systems Thinker (feedback loops)
- Five Whys Facilitator (root cause)
Format: Multi-round (technical nuance requires collaboration)
User: Should we build internal ML infrastructure or use vendor solutions?
Claude convenes:
- Software Engineer (implementation)
- McKinsey Consultant (strategic framing)
- Finance Analyst (cost analysis)
- DevOps Engineer (operational implications)
Format: Single-round → RAPID framework synthesis
User: Manufacturing yield dropped 8% after equipment upgrade
Claude convenes:
- Manufacturing Engineer (primary domain)
- Six Sigma Black Belt (DMAIC)
- Systems Thinker (unintended consequences)
Format: Multi-round (root cause needs collaborative analysis)
Skip the panel entirely when the problem has one clearly correct answer — give a direct answer instead of manufacturing perspectives around it.
Four constraints have no other source of truth in this document, so they stay explicit. Don't invent fictional names for experts (role titles only — "Software Engineer", not "Dr. John Smith, Software Engineer"), and don't invent MSD-specific details beyond general domain knowledge; both would misrepresent a generic panel as grounded in specifics it doesn't have.
The other two are failure modes the panel format itself invites: manufacturing consensus where the experts genuinely disagree (flag the disagreement and say what would settle it), and ending on "here are the perspectives, you decide" instead of a synthesis someone can act on.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
GitHub repository access in containerized environments using REST API and credential detection. Use when git clone fails, or when accessing private repos/writing files via API.
日本語の概要は準備中です。原文の説明を表示しています。
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.
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Securely manages API credentials for multiple providers (Anthropic Claude, Google Gemini, GitHub). Use when skills need to access stored API keys for external service invocations.
日本語の概要は準備中です。原文の説明を表示しています。
Guidance for asking clarifying questions when user requests are ambiguous, have multiple valid approaches, or require critical decisions. Use when implementation choices exist that could significantly affect outcomes.
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Pre-change blast-radius report for a symbol or file. Walks tree-sitting references, augments with a plain-text scan over non-parsed files (configs, plain docs), and clusters affected sites by feature (`_FEATURES.md`) or top-level package. Use when about to refactor, rename, or delete something in a repo you don't own — "what breaks if I change `validateUser`", "who calls this", "is this safe to remove", "where is this used", "blast radius", "impact analysis". This is the CONVERGENT pre-change risk skill — for "what is this repo?" use exploring-codebases; for "where is X?" use searching-codebases.
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Read Bluesky/ATProto without depending on Bluesky's AppView — interactions on a user's posts, thread replies, any repo's records, network-wide backlinks across every lexicon, an account's handle and PDS history, which collections are active network-wide, and layer-by-layer outage diagnosis. Use when bsky.app or the AppView is down or slow, when a Bluesky read returns timeouts or 5xx, when asked who liked/replied/quoted/reposted something, when pulling live Bluesky context cheaply, when reading records straight from a PDS, when asked who references a record or account anywhere on the network, whether an account has changed handle or migrated servers, or which lexicons and non-Bluesky apps are active on atproto. Complements browsing-bluesky, which routes everything through the AppView and fails when it does.
日本語の概要は準備中です。原文の説明を表示しています。