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 sneg55/agent-starter --skill remembergit clone --depth 1 https://github.com/sneg55/agent-starterWrote 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/sneg55/agent-starter/remember)<a href="https://agentmods.dev/skills/sneg55/agent-starter/remember"><img src="https://agentmods.dev/badge/skills/sneg55/agent-starter/remember/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/sneg55/agent-starter/remember"><img src="https://agentmods.dev/badge/skills/sneg55/agent-starter/remember.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 | $0.00038 | $0.00809 |
| Opus 5.5 | $0.00015 | $0.00324 |
| Sonnet 5.5 | $0.00008 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
remember 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Review
Goal
Review the user's memory landscape and produce a clear report of proposed changes, grouped by action type. Do NOT apply changes - present proposals for user approval.
Steps
1. Gather all memory layers
Read CLAUDE.md and CLAUDE.local.md from the project root (if they exist). Your auto-memory content is already in your system prompt - review it there. Note which team memory sections exist, if any.
Success criteria: You have the contents of all memory layers and can compare them.
2. Classify each auto-memory entry
For each substantive entry in auto-memory, determine the best destination:
| Destination | What belongs there | Examples |
|---|---|---|
| CLAUDE.md | Project conventions and instructions for Claude that all contributors should follow | "use bun not npm", "API routes use kebab-case", "test command is bun test", "prefer functional style" |
| CLAUDE.local.md | Personal instructions for Claude specific to this user, not applicable to other contributors | "I prefer concise responses", "always explain trade-offs", "don't auto-commit", "run tests before committing" |
| Team memory | Org-wide knowledge that applies across repositories (only if team memory is configured) | "deploy PRs go through #deploy-queue", "staging is at staging.internal", "platform team owns infra" |
| Stay in auto-memory | Working notes, temporary context, or entries that don't clearly fit elsewhere | Session-specific observations, uncertain patterns |
Important distinctions:
- CLAUDE.md and CLAUDE.local.md contain instructions for Claude, not user preferences for external tools (editor theme, IDE keybindings, etc. don't belong in either)
- Workflow practices (PR conventions, merge strategies, branch naming) are ambiguous - ask the user whether they're personal or team-wide
- When unsure, ask rather than guess
Success criteria: Each entry has a proposed destination or is flagged as ambiguous.
3. Identify cleanup opportunities
Scan across all layers for:
- Duplicates: Auto-memory entries already captured in CLAUDE.md or CLAUDE.local.md → propose removing from auto-memory
- Outdated: CLAUDE.md or CLAUDE.local.md entries contradicted by newer auto-memory entries → propose updating the older layer
- Conflicts: Contradictions between any two layers → propose resolution, noting which is more recent
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 · 68 lines · 38 tokens per session scan A 5514bc9c52a8
remember is a skill published in the GitHub repository sneg55/agent-starter (76 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 809 once invoked, about $0.0002 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
audit-context
Measures where a project's Claude context is going before applying any of clean-context's levers. Reports what Glob discovers with and without ignore files applied, the twenty largest files in the repo and which are generated, the cumulative size of every CLAUDE.md loaded for this working directory, and how many MCP…
project-memory
Use to create and maintain durable cross-session project memory and the progress ledger, so long autonomous builds resume cleanly after compaction, restarts, or new sessions. Read it when starting work and update it as decisions and tasks land.
whats-up
Fetch recent activity from Autographical — what the user and their AI agents have been working on. Use when starting a session, when the user mentions recent activity, when the user alludes to what they were doing or looking at, or picking up where they left off. Also use when the conversation feels like a topic…
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
assigns-audit
Inspect LiveView socket assigns for memory bloat — missing temporaryassigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporaryassigns.
recall
Recall prior work from past sessions — how a bug was fixed, what was decided, where a pattern lives. Use when asked 'have we done this before' or 'how did I fix X' in Elixir/Phoenix work. ccrider MCP when available, else git + solution docs.