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 Owl-Listener/ai-design-skills --skill state-managementgit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote 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/owl-listener/ai-design-skills/state-management)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/state-management"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/state-management/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/owl-listener/ai-design-skills/state-management"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/state-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.01322 |
| Opus 5 | $0.00008 | $0.00661 |
| Sonnet 5 | $0.00003 | $0.00264 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
state-management 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 12d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
State Management
In a multi-agent system, state is the shared truth about what's happened, what's in progress, and what's been decided. Without state management, agents work with stale or conflicting information — and the user pays the cost in repeated questions, contradictory answers, and lost progress.
State management is the plumbing skill of multi-agent design. Get it wrong and every other skill in this plugin gets harder.
Types of state
- Task state: where the overall task is in its lifecycle. Which subtasks are complete, in progress, or pending.
- Context state: what each agent knows. What has been shared, summarised, or dropped.
- User state: preferences, history, and current emotional state.
- Decision state: decisions made, options considered, options rejected (and why).
- Error state: what has failed, been retried, been escalated.
State architecture patterns
- Centralised state: one shared store all agents read from and write to. Simple, debuggable. Bottleneck risk at scale.
- Distributed state: each agent maintains its own state and syncs with others. Flexible. Consistency risk.
- Event-sourced state: state is built from a log of events. Every change is recorded. Auditable. Complex.
- Blackboard pattern: shared workspace where agents post results and read others' contributions. Good for collaborative problem-solving.
Designing state for users
Users have expectations about what the system remembers:
- Within-session state: everything said in this conversation should persist consistently
- Cross-session state: preferences, decisions, and context from past sessions should carry forward
- Cross-agent state: if one agent learned something, other agents should know
- User-controlled state: users should be able to see, edit, and clear what the system remembers
Decision rules
- Default to centralised state. Reach for distributed only when measured cross-agent latency is genuinely the bottleneck. Most teams choose distributed prematurely and pay in consistency bugs forever.
- If a piece of state lives in a single agent's working memory, treat it as lost. Memory across model invocations is unreliable; promote anything that needs to persist to the shared store.
- Cross-session state requires explicit consent per category. "Remember preferences" ≠ "remember what we discussed". Granularity is the design constraint, not a nice-to-have.
- For state conflicts, prefer detection over silent merging. A surfaced conflict the user resolves is recoverable; a silently merged inconsistency is invisible damage.
- State a user can't see, they can't trust. Any state used to personalise behaviour must be visible somewhere the user can find within ~30 s of UI navigation.
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.
- 12d ago First seen · 96 lines · 15 tokens per session scan A d2776d7e6dff
state-management is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 1,322 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-30.
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