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 AndrewNgGirl/SkillLens --skill memorygit clone --depth 1 https://github.com/AndrewNgGirl/SkillLensWrote 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/andrewnggirl/skilllens/memory)<a href="https://agentmods.dev/skills/andrewnggirl/skilllens/memory"><img src="https://agentmods.dev/badge/skills/andrewnggirl/skilllens/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/andrewnggirl/skilllens/memory"><img src="https://agentmods.dev/badge/skills/andrewnggirl/skilllens/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 | $0.00034 | $0.00062 |
| Opus 5.5 | $0.00014 | $0.00025 |
| Sonnet 5.5 | $0.00007 | $0.00012 |
| Haiku 4.5 | $0.00003 | $0.00006 |
Grade A, and why
pr-memory-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 2mo 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
Memory reviewer
Specialist for memory concerns in PR review.
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.
- 2mo ago First seen · 8 lines · 34 tokens per session scan A 06ef55aade50
pr-memory-reviewer is a skill published in the GitHub repository AndrewNgGirl/SkillLens (44 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 62 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.
Other skills, from other repositories
session-checkpoint
Use when saving session state before context compaction, switching tasks, or ending a session. Runs 5 phases: context extraction → handoff write → memory save → preservation check → compact guidance (plus an optional attestation step).
learning
Capture a durable project lesson in Agent Runway memory. Use when the developer says "learning", "remember this", "save this lesson", "add to memory", or when a completed task reveals a recurring decision, failure, convention, or guardrail that should influence future work.
code-reviewer
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
memory-import
Bulk-import an external knowledge base into Prismer memory via the three-layer import pipeline (07 §3.1). Use when the user asks to import/migrate documents, a knowledge base, or notes into workspace memory. Layer 1 lands raw documents (zero LLM, instantly searchable); Layer 2 is YOUR distillation work — you read the…
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.