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/context-loadernpx skills add ThinkfleetAI/memmesh --skill context-loadergit 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 | $0.00068 | $0.00497 |
| Opus 5 | $0.00034 | $0.00249 |
| Sonnet 5 | $0.00014 | $0.00099 |
| Haiku 4.5 | $0.00007 | $0.00050 |
Grade A, and why
context-loader 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.
What it actually says
context-loader
⚙️ Requires MemMesh hosted mode. Calibrated prediction and behavior discovery run on the hosted engine — set your
mm-API key. On a local / open-source install these tools (memory_predict,memory_build_context) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back tosearch/recallfor what's already known.
Prime the session with the right memory before you act.
General project/session context
{ "name": "memory_search", "arguments": { "projectId": "<repo>", "limit": 20 } }
Skip only on pure pleasantries; the moment the task is substantive, load first.
A specific subject — the synthesized bundle
When you're about to reason about one entity (a user, contact, account), don't fire five searches — get the assembled picture in one call:
{ "name": "memory_build_context",
"arguments": { "subjectKind": "user", "subjectId": "<id>", "maxTokens": 2000,
"include": ["profile","patterns","predictions","memories"] } }
This returns the profile, active behavior patterns, forward predictions, and top memories — with provenance ids — ready to drop at the top of your prompt. That prediction section is the differentiator: you enter the task already knowing what the subject is likely to do next.
Anticipatory follow-on
Once you're working with a few memories, pull what's most likely needed next via spreading activation over the graph:
{ "name": "memory_prefetch_related", "arguments": { "seedMemoryIds": ["<id1>","<id2>"], "limit": 10 } }
Cite what you use
When a loaded memory shapes your response, mention it briefly so the user can correct stale info.
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 · 53 lines · 68 tokens per session scan A ac96635fb84d
context-loader is a skill published in the GitHub repository ThinkfleetAI/memmesh (440 stars, last pushed 7d ago), licensed Apache-2.0. It adds 68 tokens to every session and 497 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.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…