Agent skill · magnus919
promise-theory
Design and diagnose coordination in hybrid human + AI agent workforces using promise theory (Burgess/Bergstra): model agents as autonomous, coordination as voluntary offers plus acceptance, and trust as calibrated assessment. Use for delegation modeling, capability manifests and agent contracts, coordination-failure diagnosis, trust/verification calibration, convergent self-healing systems, and converting obligation-based designs to promise-based. Do not use for enforceable centralized control, legal contract drafting (promise theory is not contract law), simple single-agent prompting, imperative push-based orchestration, or tool manuals — route those to the tool's own skill.
What it needs
About 5k tokens when loaded.
What this skill does
Promise Theory Promise theory (Mark Burgess; formalized with Jan Bergstra) is a method of analysis for systems of autonomous agents — humans, LLM agents, APIs, and deterministic automation. It supplies the vocabulary for designing and diagnosing delegation: promises, acceptances, assessments, breaches, and renegotiation. This skill is a thin router; load the dense material only when a row in Load By Need matches your task. Core model A promise is an autonomous declaration of intended, but as yet unverified, behaviour from a promiser to a promisee (body: label Λ, type τ, constraint χ). Agents are autonomous: no agent can promise another's behaviour. Coordination emerges from voluntary cooperation — an offer plus an acceptance (a counter-promise) — never from imposed obligation. Obligations are derived, non-autonomous impositions (imposition + penalty). Agents keep promises via an evaluation loop: observe → assess → act, converging on the promised state. The Downstream Principle: the most downstream party in a promise chain carries the greatest causal responsibility for the outcome. When to use Load this skill when any of these triggers matches: Modeling delegation between humans and agents — decide who may promise what to whom, and who accepts, in a human + AI workforce. Designing capability manifests or agent contracts — declare capabilities and intent with acceptance criteria, verification, and withdrawal semantics. Diagnosing coordination failures — explain unkept promises, refused acceptances, or missing assessments in multi-agent work. Calibrating trust and verification — decide how much to verify an agent, at what rate, and at what cost. Designing self-healing or convergent infrastructure — evaluation loops that observe, assess, and act toward a desired state. Converting obligation-based designs to promise-based ones — replace push commands and mandates with voluntary offers and acceptance. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/promise-theory