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 45ck/skill-harness --skill agent-memory-design-reviewergit clone --depth 1 https://github.com/45ck/skill-harnessWrote 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/45ck/skill-harness/agent-memory-design-reviewer)<a href="https://agentmods.dev/skills/45ck/skill-harness/agent-memory-design-reviewer"><img src="https://agentmods.dev/badge/skills/45ck/skill-harness/agent-memory-design-reviewer/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/45ck/skill-harness/agent-memory-design-reviewer"><img src="https://agentmods.dev/badge/skills/45ck/skill-harness/agent-memory-design-reviewer.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 | $0.00033 | $0.00255 |
| Opus 5.5 | $0.00013 | $0.00102 |
| Sonnet 5.5 | $0.00007 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
agent-memory-design-reviewer 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 1mo 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.
What it actually says
Agent Memory Design Reviewer
Use this skill when a project wants agents to remember lessons, retrieve prior work, or improve future runs.
Review
- Separate durable memory from run-local notes.
- Require source links for durable memory.
- Define freshness and revocation behavior.
- Exclude secrets, credentials, private logs, customer data, raw traces, and unsupported claims.
- Treat self-improvement as a proposal until verified by tests, reviews, or policy checks.
- Check poisoning paths: untrusted docs, generated content, user content, web content, issue comments, and previous agent outputs.
Memory Verdicts
record: durable, sourced, reusable, safesummarize: useful but too noisy or sensitive in raw formrun-local: useful only for the current taskreject: stale, unsafe, unsupported, or too specific
Output
Verdict
Memory Items
Source Links
Retrieval Rules
Revocation / Freshness
Poisoning Risks
Required Redactions
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.
- 1mo ago First seen · 35 lines · 33 tokens per session scan A 8f5868105d15
agent-memory-design-reviewer is a skill published in the GitHub repository 45ck/skill-harness (15 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 255 once invoked, about $0.0001 per session on Opus 5.5. 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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Interview
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Harvest
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