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/thinkfleetai/memmesh/pinnpx skills add ThinkfleetAI/memmesh --skill pingit clone --depth 1 https://github.com/ThinkfleetAI/memmeshWhat 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 | $0.00053 | $0.00359 |
| Opus 5 | $0.00026 | $0.00179 |
| Sonnet 5 | $0.00011 | $0.00072 |
| Haiku 4.5 | $0.00005 | $0.00036 |
Grade A, and why
pin 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 3d 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
pin
Mark a memory as protected. MemMesh has no separate "pinned" flag — pinning is
expressed through importance + impact + confirmation, which the dream
consolidation pass treats as do-not-retire.
Pin
- Find the item (
memory_search) and read it (memory_recall). - Re-assert it at maximum durability with
memory_save(upsert on the same id):
{ "name": "memory_save",
"arguments": { "id": "<same id>", "platformId": "local", "scope": "project",
"type": "rule", "content": "<verbatim content>",
"importance": 10, "metadata": { "pinned": true, "impact": "HIGH" } } }
Setting importance: 10 and metadata.pinned: true is the signal the dream
skill checks before deleting/superseding anything.
Unpin
Re-save the same id with importance back to a normal value (≈5) and
metadata.pinned: false. The item stays in memory but becomes eligible for
consolidation again.
When to pin
Architecture decisions, security constraints, compliance rules, immutable conventions — anything where losing it silently would cause real harm. Don't pin routine preferences; let the engine manage those.
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.
- 3d ago First seen · 42 lines · 53 tokens per session scan A 39941764c338
pin is a skill published in the GitHub repository ThinkfleetAI/memmesh (440 stars, last pushed 8d ago), licensed Apache-2.0. It adds 53 tokens to every session and 359 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.
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…