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 agentmods add skills/jmstar85/oh-my-githubcopilot/remembernpx skills add jmstar85/oh-my-githubcopilot --skill remembergit clone --depth 1 https://github.com/jmstar85/oh-my-githubcopilotWhat 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 | $0.00036 | $0.00773 |
| Opus 5 | $0.00018 | $0.00387 |
| Sonnet 5 | $0.00007 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00077 |
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 2d 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 remember — 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remember
Promote durable, reusable knowledge into the right memory surface instead of leaving it buried in chat history.
When to Use
- User wants to preserve knowledge discovered during a session
- Organizing scattered findings into structured memory
- Cleaning up duplicate or conflicting stored information
When NOT to Use
- Ephemeral task notes → just keep in conversation
- Already documented in code or docs → reference directly
Memory Surfaces
| Surface | Use For | Durability |
|---|---|---|
| Project memory | Durable team/project knowledge | Permanent |
| Session context | Short-lived working notes | Session only |
| Docs / Instructions | Conventions, instructions | Permanent |
Workflow
- Gather the relevant session findings
- Classify each item:
- Durable project fact
- Temporary working note
- Operator preference or instruction
- Duplicate / stale / conflicting information
- Propose the best destination for each item
- Write or update only the appropriate memory surface
- Call out duplicates or conflicts that should be cleaned up
Rules
- Do not dump everything into one store
- Prefer project memory for durable team knowledge
- Keep entries concise and actionable
- If something is uncertain, mark it as uncertain rather than storing it as fact
Output
- What was stored
- Where it was stored
- Any duplicates/conflicts found
Quality Gate (Before Storing)
Before committing anything to memory, apply this 3-question filter. Skip storage if the answer to any question is "No":
| Question | Rationale |
|---|---|
| Is it actionable? "Does this tell someone what to DO in a future situation?" | Observations are not memory entries; decisions are. |
| Is it durable? "Will this still be true in 3 months, or is it tied to a temporary workaround?" | Ephemeral findings belong in session notes, not project memory. |
| Is it unique? "Does something close already exist in memory that covers this?" | Duplicate entries create confusion; prefer updating existing entries. |
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.
- 2d ago First seen · 89 lines · 36 tokens per session scan A 2d89dd3c51e7
remember is a skill published in the GitHub repository jmstar85/oh-my-githubcopilot (153 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 773 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to remember, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…