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 DanWahlin/ai-agent-board --skill tiered-memorygit clone --depth 1 https://github.com/DanWahlin/ai-agent-boardWrote 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/danwahlin/ai-agent-board/tiered-memory)<a href="https://agentmods.dev/skills/danwahlin/ai-agent-board/tiered-memory"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/tiered-memory/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/danwahlin/ai-agent-board/tiered-memory"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/tiered-memory.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 | $0.00025 | $0.01804 |
| Opus 5 | $0.00013 | $0.00902 |
| Sonnet 5 | $0.00005 | $0.00361 |
| Haiku 4.5 | $0.00003 | $0.00180 |
Grade A, and why
tiered-memory 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 10d 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.
This is a copy
100% identical to tiered-memory — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Tiered Agent Memory
Overview
Squad agents currently load their full context history on every spawn, resulting in 34–74KB payloads per agent (8,800–18,500 tokens). Measurement shows 82–96% of that context is "old noise" — information that is no longer relevant to the current task. The Tiered Agent Memory skill introduces a three-tier memory model that eliminates this bloat, achieving 20–55% context reduction per spawn in production.
Memory Tiers
🔥 Hot Tier — Current Session Context
- Size target: ~2–4KB
- Load policy: Always loaded. Every spawn includes hot memory by default.
- Contents: Current task description, active decisions made this session, immediate blockers, last 3–5 actions taken, who you are talking to right now.
- Lifetime: Current session only. Discarded after session ends (Scribe promotes relevant parts to Cold).
- Purpose: Provide immediate task context without any latency or load decision.
❄️ Cold Tier — Summarized Cross-Session History
- Size target: ~8–12KB
- Load policy: Load on demand. Include only when the task explicitly needs history.
- Contents: Summarized past sessions (compressed by Scribe), cross-session decisions, recurring patterns, unresolved issues from prior work.
- Lifetime: 30 days rolling window. After 30 days, Scribe promotes to Wiki tier.
- Purpose: Answer "what have we tried before?" and "what was decided?" without replaying full transcripts.
- How to include: Pass
--include-coldin spawn template or add## Cold Memorysection.
📚 Wiki Tier — Durable Structured Knowledge
- Size target: variable, structured reference docs
- Load policy: Async write, selective read. Load only when task requires domain knowledge.
- Contents: Architecture decisions (ADRs), agent charters, routing rules, stable conventions, external API contracts, known platform constraints.
- Lifetime: Permanent until explicitly deprecated.
- Purpose: Authoritative reference. Not history — structured facts.
- How to include: Pass
--include-wikior reference specific wiki doc paths in spawn template.
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.
- 10d ago First seen · 235 lines · 25 tokens per session scan A 9ee36cd7dac5
tiered-memory is a skill published in the GitHub repository DanWahlin/ai-agent-board (57 stars, last pushed 15d ago), licensed MIT. It adds 25 tokens to every session and 1,804 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to tiered-memory, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are supported, or when a connect adapter writes the wrong config path.
agentmemory-hooks
The agentmemory plugin hooks that capture observations automatically across the agent session lifecycle. Use when explaining how memory gets captured without manual saves, when debugging missing observations, or when tuning what gets recorded.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…
thisQuarter
Resolve "thisQuarter" to a concrete ISO date range relative to your run time — this quarter so far (quarter start → today). Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a quarter-to-date task (QTD…