Indexé dans Registry
agent-to-agent
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
Vue d’ensemble
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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Agent-to-Agent (A2A) Communication Protocol
Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file .a2a-context.json and the Agent tool.
Contents
references/protocol.md— message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management.references/registry.md— agent registration, capability discovery, built-in agent templates.references/patterns.md— request/response, pipeline, fan-out/fan-in, conversation, supervisor.references/handoff.md— structured handoff, acceptance, rejection, chain tracking.references/error-handling.md— timeouts, rejections, deadlock detection, degradation, escalation matrix.references/workflows.md— worked examples (research+writer, code+review, sales+technical).references/operations.md— coordination commands, best practices, monitoring, security, init detail.
Workflow
- Understand the goal. Determine what the user wants to accomplish with multiple agents.
- Design the team. Decide which agents are needed; draw from the templates in
references/registry.mdor write custom specs. - Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from
references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent. - Initialize. Locate the project root. Read
.a2a-context.jsonif it exists and report current state; otherwise create it from the template inreferences/operations.md. Register every agent into theagentssection perreferences/registry.md. - Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see
references/operations.md). For parallelism, issue multiple Agent tool calls in a single response. - Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task
chain. Followreferences/handoff.md. - Monitor and recover. Read
.a2a-context.jsonto track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix inreferences/error-handling.md. Cap retries at 3 before escalating to the user. - Deliver. Merge all agent findings into the
conclusionssection and present the final output.
Core Rules
- Treat
.a2a-context.jsonas the single source of truth. Read it before acting; write the complete file back after modifying. Follow the atomic read-modify-write procedure inreferences/protocol.md. - Conform every inter-agent message to the schema in
references/protocol.md. - Confine each agent to writing its own section plus shared
conclusions. Restrict task assignment changes to the Coordinator. - Never write secrets to
.a2a-context.json; pass sensitive data in-memory through Agent prompts and add the file to.gitignore. - Require explicit ERROR messages for all failures; never fail silently. Run context summarization when the file exceeds 50KB.
Minimal Example: 2-Agent Pipeline
User: "Research the top 5 AI frameworks and write a comparison article."
Coordinator:
1. Create .a2a-context.json
2. Register: researcher, writer
3. Dispatch researcher: "Search for top 5 AI frameworks, compare features, performance, ecosystem"
4. Read researcher's findings from shared context
5. Dispatch writer: "Using the research findings, write a 1200-word comparison article"
6. Read writer's draft from shared context
7. Present the final article to the user
Métadonnées du fichier
name: agent-to-agent description: Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations. tools: Read, Write, Agent, Bash, Glob, Grep model: inherit
Voir le texte original
--- name: agent-to-agent description: Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations. tools: Read, Write, Agent, Bash, Glob, Grep model: inherit --- # Agent-to-Agent (A2A) Communication Protocol Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file `.a2a-context.json` and the Agent tool. ## Contents - `references/protocol.md` — message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management. - `references/registry.md` — agent registration, capability discovery, built-in agent templates. - `references/patterns.md` — request/response, pipeline, fan-out/fan-in, conversation, supervisor. - `references/handoff.md` — structured handoff, acceptance, rejection, chain tracking. - `references/error-handling.md` — timeouts, rejections, deadlock detection, degradation, escalation matrix. - `references/workflows.md` — worked examples (research+writer, code+review, sales+technical). - `references/operations.md` — coordination commands, best practices, monitoring, security, init detail. ## Workflow 1. Understand the goal. Determine what the user wants to accomplish with multiple agents. 2. Design the team. Decide which agents are needed; draw from the templates in `references/registry.md` or write custom specs. 3. Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from `references/patterns.md`. Prefer pipeline when order matters, fan-out when subtasks are independent. 4. Initialize. Locate the project root. Read `.a2a-context.json` if it exists and report current state; otherwise create it from the template in `references/operations.md`. Register every agent into the `agents` section per `references/registry.md`. 5. Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see `references/operations.md`). For parallelism, issue multiple Agent tool calls in a single response. 6. Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task `chain`. Follow `references/handoff.md`. 7. Monitor and recover. Read `.a2a-context.json` to track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix in `references/error-handling.md`. Cap retries at 3 before escalating to the user. 8. Deliver. Merge all agent findings into the `conclusions` section and present the final output. ## Core Rules - Treat `.a2a-context.json` as the single source of truth. Read it before acting; write the complete file back after modifying. Follow the atomic read-modify-write procedure in `references/protocol.md`. - Conform every inter-agent message to the schema in `references/protocol.md`. - Confine each agent to writing its own section plus shared `conclusions`. Restrict task assignment changes to the Coordinator. - Never write secrets to `.a2a-context.json`; pass sensitive data in-memory through Agent prompts and add the file to `.gitignore`. - Require explicit ERROR messages for all failures; never fail silently. Run context summarization when the file exceeds 50KB. ## Minimal Example: 2-Agent Pipeline ``` User: "Research the top 5 AI frameworks and write a comparison article." Coordinator: 1. Create .a2a-context.json 2. Register: researcher, writer 3. Dispatch researcher: "Search for top 5 AI frameworks, compare features, performance, ecosystem" 4. Read researcher's findings from shared context 5. Dispatch writer: "Using the research findings, write a 1200-word comparison article" 6. Read writer's draft from shared context 7. Present the final article to the user ```
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- Licence
- MIT
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Source du skill enregistrée
Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.
Réviser avant installation: Éviter l’installation automatique
Licence: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The SKILL.md references external files (references/*.md) that are not fully included in the excerpt, but the repository appears to contain them. This is acceptable as long as the full skill is distributed with those files.
- The shared context file .a2a-context.json is a single point of failure; while atomic read-modify-write is specified, concurrent agents could still race if not strictly serialized. The protocol relies on the coordinator to enforce ordering, which is a design assumption.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 287 stars, 43 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.
Commencer par une petite tâche
- 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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- 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.
Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.
Source et conseils d’utilisation
Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.
- Dépôt source
- OneWave-AI/claude-skills
- Licence
- MIT
- Version
- 1.0.0
- Dernier push GitHub
- 11 août 2026
- Registre mis à jour
- 6 sept. 2026
- Chemin des instructions
- agent-to-agent/SKILL.md @ 82859c0ebaff
Version déclarée dans le registre ; vérifiez les versions de la source.
Qualité
68/100
Prometteur
Confiance
57/100
Do not auto-install
Audit
73/100
Revue nécessaire
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The SKILL.md references external files (references/*.md) that are not fully included in the excerpt, but the repository appears to contain them. This is acceptable as long as the full skill is distributed with those files.
- The shared context file .a2a-context.json is a single point of failure; while atomic read-modify-write is specified, concurrent agents could still race if not strictly serialized. The protocol relies on the coordinator to enforce ordering, which is a design assumption.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 287 stars, 43 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Résultats
- —
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Plus de détails
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}Pour le créateur
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- OneWave-AI
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- OneWave-AI/claude-skills
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Revendiquer cette fiche de skill
Cette fiche Indexé par Registry est attribuée à OneWave-AI, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.
Kit de partage
Kit de backlinks créateur
Ajoutez les badges de preuve à votre README
Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.
[](https://www.openagentskill.com/skills/onewave-ai-agent-to-agent?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/onewave-ai-agent-to-agent?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/onewave-ai-agent-to-agent/audit)
[](https://www.openagentskill.com/skills/onewave-ai-agent-to-agent?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Signal de communauté
Indiquez si ce skill semble utile à votre workflow Agent. Les retours agrégés améliorent le classement au fil du temps.
