Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add plurigrid/asi --skill emergent-role-assignmentgit clone --depth 1 https://github.com/plurigrid/asiWrote 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/plurigrid/asi/emergent-role-assignment)<a href="https://agentmods.dev/skills/plurigrid/asi/emergent-role-assignment"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/emergent-role-assignment/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/plurigrid/asi/emergent-role-assignment"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/emergent-role-assignment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00581 |
| Opus 5 | $0.00000 | $0.00291 |
| Sonnet 5 | $0.00000 | $0.00116 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
emergent-role-assignment 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 6d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Emergent Role Assignment
Category: Phase 3 Core - Self-Organization
Status: Skeleton Implementation
Dependencies: sheaf-theoretic-coordination, chemical-organization-theory
Overview
Implements spontaneous role assignment in multi-agent systems through self-organization, dynamic hierarchy adaptation, and reward-based emergence without central coordination.
Capabilities
- Spontaneous Hierarchy: Agents self-organize into hierarchical structures
- Dynamic Role Adaptation: Roles change based on task demands
- Reward-Based Emergence: Roles emerge from collective optimization
- Stability Analysis: Verify organizational stability and convergence
Core Components
-
Role Dynamics (
role_dynamics.jl)- Role state representation
- Transition dynamics between roles
- Stability attractors
-
Hierarchy Formation (
hierarchy_formation.jl)- Emergent leadership via fitness
- Span of control optimization
- Dynamic reorganization triggers
-
Reward Shaping (
reward_shaping.jl)- Collective reward functions
- Credit assignment without centralization
- Multi-agent learning objectives
-
Stability Verification (
stability_verification.jl)- Lyapunov function construction
- Convergence guarantees
- Resilience to perturbations
Integration Points
- Input from:
sheaf-theoretic-coordination(consensus on roles) - Output to:
chemical-organization-theory(roles as stable organizations) - Coordinates with:
feedforward-learning-local(local learning signals)
Usage
using EmergentRoleAssignment
# Define multi-agent system
agents = [Agent(id=i, capabilities=rand(5)) for i in 1:20]
environment = GridWorld(10, 10)
# Initialize role assignment system
role_system = RoleSystem(
n_roles=4,
transition_rates=0.1,
reward_fn=collective_foraging_reward
)
# Simulate emergence
trajectory = simulate_emergence(role_system, agents, environment, steps=1000)
# Analyze stability
stability = analyze_role_stability(trajectory)
hierarchy = extract_hierarchy(trajectory.final_state)
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.
- 6d ago First seen · 83 lines · 0 tokens per session scan A f8d6f86faec0
emergent-role-assignment is a skill published in the GitHub repository plurigrid/asi (62 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 581 tokens. 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 2026-09-03.
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