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 hdl-tools/analog-chip-design-agents --skill memory-keepergit clone --depth 1 https://github.com/hdl-tools/analog-chip-design-agentsWrote 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/hdl-tools/analog-chip-design-agents/memory-keeper)<a href="https://agentmods.dev/skills/hdl-tools/analog-chip-design-agents/memory-keeper"><img src="https://agentmods.dev/badge/skills/hdl-tools/analog-chip-design-agents/memory-keeper/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/hdl-tools/analog-chip-design-agents/memory-keeper"><img src="https://agentmods.dev/badge/skills/hdl-tools/analog-chip-design-agents/memory-keeper.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 | $0.00055 | $0.00938 |
| Opus 5 | $0.00028 | $0.00469 |
| Sonnet 5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
memory-keeper 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 9d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Memory Keeper
Invocation
Invoke as /analog-design-infrastructure:memory-keeper --domain <domain> (or --all).
This skill runs the distillation helper, reviews the candidate patterns, and updates the
relevant <domain>/knowledge.md under the resolved memory root — it does not spawn an
orchestrator. Pass --init (runs memory_root.py --init) to resolve and seed the central
memory root and migrate any repo-local runtime data, without distilling.
Memory Root Resolution
Memory does not live at a fixed relative memory/ path. The active root is resolved by
memory_root.py (the single source of truth that distill.py and tools/qor_trends.py import),
in this priority order:
- an explicit
--memory-root PATHargument - the
$CHIP_DESIGN_MEMORY_ROOTenvironment variable - the central default
${XDG_DATA_HOME:-$HOME/.local/share}/chip-design-agents/analog/memory(Windows:%LOCALAPPDATA%\chip-design-agents\analog\memory) - the in-repo
memory/tree as a seed fallback (used only if the central root is unwritable)
The in-repo memory/ tree is the version-controlled seed: on first resolution each
<domain>/knowledge.md is copied into the central root if absent (never overwriting accumulated
data; runtime experiences.jsonl/run_state.md are never seeded). Orchestrators resolve this same
root at session start and use it as <MEM> for every read/write. Print it with
python3 plugins/infrastructure/skills/memory-keeper/memory_root.py (add --init to seed +
migrate + report). Per-project scoping: export CHIP_DESIGN_MEMORY_ROOT="$PWD/memory".
Purpose
Read the Tier-1 memory/<domain>/experiences.jsonl run records, identify new issue/fix
patterns and tool flags not already captured, and update the Tier-2
memory/<domain>/knowledge.md summaries without discarding still-valid content. This keeps
the knowledge each orchestrator reads at session start current as experience accumulates.
Domain Rules
- Run the helper:
python3 distill.py <domain> [--min-records N] [--memory-root PATH]. It emits a JSON summary (issue/fix pairs, tool-flag candidates, metric ranges, signoff rate) and exits with code 2 (skip) when fewer than--min-recordsrecords exist (default threshold: 5). - Supported domains are listed in
distill.pyVALID_DOMAINS(the 14 design domains plusinfrastructureandmeta). Each has a numerickey_metricsfield set inMETRIC_FIELDS. - Merge — do not overwrite: add new issue/fix patterns and tool flags to the matching
section of
knowledge.md; preserve still-valid existing content. De-duplicate near-identical entries. - Record metric ranges (min/median/max/latest) for the domain's numeric metrics so the orchestrator has a baseline for regression awareness.
- Never edit
experiences.jsonl— it is the append-only source of truth.
What ships with it
2 files 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.
- 9d ago First seen · 77 lines · 55 tokens per session scan A f4dd7bad7758
memory-keeper is a skill published in the GitHub repository hdl-tools/analog-chip-design-agents (22 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 938 once invoked, about $0.0003 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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