Name: u0102-memory-multi-agent-negotiation-mediator
Author: zwright8

Imported from zwright8/OpenClaw-Code (skills/generated/0102-memory-multi-agent-negotiation-mediator/SKILL.md). Install upstream with npx skills add zwright8/OpenClaw-Code --skill 0102-memory-multi-agent-negotiation-mediator. Copyright stays with the author.

Memory Multi-Agent Negotiation Mediator

Why This Skill Exists

We need this skill because agents lose performance when lessons are not retained and reused. This specific skill resolves resource and strategy conflicts with explicit tradeoffs.

When To Use

Use this skill when the request explicitly needs "Memory Multi-Agent Negotiation Mediator" outcomes in the Memory and Knowledge Operations domain.

Step-by-Step Implementation Guide

Define the scope and success metrics for Memory Multi-Agent Negotiation Mediator, including at least three measurable KPIs tied to repeated mistakes and context loss.
Design and version the input/output contract for episodic logs, knowledge nodes, and retrieval metadata, then add schema validation and failure-mode handling.
Implement the core capability using structured bargaining protocols, and produce negotiated agreement sets with deterministic scoring.
Integrate the skill into swarm orchestration: task routing, approval gates, retry strategy, and rollback controls.
Add unit, integration, and simulation tests that explicitly cover repeated mistakes and context loss, then run regression baselines.
Deploy behind a feature flag, monitor telemetry/alerts for two release cycles, and iterate thresholds based on observed outcomes.

Required Deliverables

Capability contract: input schema, deterministic scoring, output schema, and failure modes.
Runtime profile: collaboration-mediator using structured bargaining protocols to produce negotiated agreement sets.
Orchestration integration: memory-and-knowledge-operations:collaboration-mediator routing, approval gates, retries, and rollback controls.
Validation evidence: unit, integration, simulation, regression-baseline suites and rollout telemetry.

Operational Runbook

Preflight

Confirm the Memory Multi-Agent Negotiation Mediator request scope, source evidence, and measurable success criteria before execution.
Verify feature flag skill_0102_memory-multi-agent-negotiation-m, approval gates, and rollback owner before autonomous use.

Execution

Execute structured bargaining protocols with deterministic scoring and reproducible trace capture.
Produce negotiated agreement sets plus scorecard, assumptions, and unresolved-risk notes.

Recovery

Fail closed when required signals, evidence, or approval gates are missing.
Rollback to the last stable baseline when posture is critical or validation fails.

Handoff

Publish negotiated agreement sets, validation evidence, and telemetry links to downstream owners.
Queue follow-up tasks for unresolved risks, threshold tuning, or approval review.

Guardrails

[quality] Require deterministic scoring and validation evidence before promotion.
[reliability] Preserve retries, rollback controls, and failure-mode evidence for every run.
[safety] Route critical posture or missing approval gates to human review before autonomous action.

Install u0102-memory-multi-agent-negotiation-mediator and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

{ "ocm": "1", "id": "zwright8-openclaw-code-0102-memory-multi-agent-negotiati-7ca737", "kind": "skill", "name": "u0102-memory-multi-agent-negotiation-mediator", "description": "Build and operate the \"Memory Multi-Agent Negotiation Mediator\" capability for Memory and Knowledge Operations. Trigger when this exact capability is needed in mission execution.", "publisher": "zwright8", "version": "1.0.0", "capabilities": { "domains": [ "general" ], "tags": [ "skill-md", "github" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Build and operate the \"Memory Multi-Agent Negotiation Mediator\" capability for Memory and Knowledge Operations. Trigger when this exact capability is needed in mission execution." ], "primary": false, "metadata": { "source": { "provider": "github", "repository": "https://github.com/zwright8/OpenClaw-Code", "path": "skills/generated/0102-memory-multi-agent-negotiation-mediator/SKILL.md", "ref": "ec1b3b08616195dbe709f116bcfc092c009bb753", "url": "https://github.com/zwright8/OpenClaw-Code/blob/ec1b3b08616195dbe709f116bcfc092c009bb753/skills/generated/0102-memory-multi-agent-negotiation-mediator/SKILL.md", "key": "zwright8/OpenClaw-Code/skills/generated/0102-memory-multi-agent-negotiation-mediator/SKILL.md" } }, "instructions": "# Memory Multi-Agent Negotiation Mediator\n\n## Why This Skill Exists\nWe need this skill because agents lose performance when lessons are not retained and reused. This specific skill resolves resource and strategy conflicts with explicit tradeoffs.\n\n## When To Use\nUse this skill when the request explicitly needs \"Memory Multi-Agent Negotiation Mediator\" outcomes in the Memory and Knowledge Operations domain.\n\n## Step-by-Step Implementation Guide\n1. Define the scope and success metrics for `Memory Multi-Agent Negotiation Mediator`, including at least three measurable KPIs tied to repeated mistake", "cost": { "context_tokens": 741 } }

u0102-memory-multi-agent-negotiation-mediator

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