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/tc407-api/titan-memory/setupnpx skills add TC407-api/Titan-Memory --skill setupgit clone --depth 1 https://github.com/TC407-api/Titan-MemoryWhat 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 | $0.00010 | $0.00474 |
| Opus 5 | $0.00005 | $0.00237 |
| Sonnet 5 | $0.00002 | $0.00095 |
| Haiku 4.5 | $0.00001 | $0.00047 |
Grade A, and why
setup 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
Titan Memory Setup
Guide the user through configuring Titan Memory. Check each requirement and help fix issues.
Steps
-
Check environment variables:
- Run:
echo $ZILLIZ_URI(or$env:ZILLIZ_URIon Windows) - Run:
echo $ZILLIZ_TOKEN(or$env:ZILLIZ_TOKENon Windows) - Run:
echo $VOYAGE_API_KEY(or$env:VOYAGE_API_KEYon Windows)
- Run:
-
For each missing variable, explain:
ZILLIZ_URI: Get from Zilliz Cloud console → Clusters → your cluster → Connection Details. Format:https://xxx.api.gcp-us-west1.zillizcloud.comZILLIZ_TOKEN: Get from Zilliz Cloud console → API Keys. Create a read/write key.VOYAGE_API_KEY: Get from dash.voyageai.com → API Keys. Optional — Titan falls back to local embeddings without it, but quality is lower.
-
Guide shell profile setup:
- bash/zsh: Add to
~/.bashrcor~/.zshrc:export ZILLIZ_URI="your-uri" export ZILLIZ_TOKEN="your-token" export VOYAGE_API_KEY="your-key" - PowerShell: Add to
$PROFILE:$env:ZILLIZ_URI = "your-uri" $env:ZILLIZ_TOKEN = "your-token" $env:VOYAGE_API_KEY = "your-key"
- bash/zsh: Add to
-
Verify connectivity:
- Call
titan_statsto test the MCP connection - If it works, report success with memory counts
- If it fails, show the error and suggest fixes
- Call
-
Report status:
- Which env vars are set
- Whether Zilliz connection works
- Whether Voyage AI embeddings are available
- Embedding mode: "voyage" (cloud) or "local" (fallback)
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 · 47 lines · 10 tokens per session scan A 35eaeab3b30c
setup is a skill published in the GitHub repository TC407-api/Titan-Memory (5 stars, last pushed 4d ago), licensed Apache-2.0. It adds 10 tokens to every session and 474 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.
agt-policy-authoring
Create and validate a minimal AGT Copilot CLI policy tailored to the repository being inspected.
alive:demo
Generate a believable, lived-in ALIVE world from a free-text persona description (custom path) or a deterministic sandbox preset. Routes the create/list/activate/deactivate/delete/status surface and orchestrates the 5-stage subagent generation pipeline.
mnemo-cortex
Installs and wires Mnemo Cortex (local-first persistent memory) into OpenClaw and other MCP-capable agents. Use for cross-session recall, decision history, or multi-agent shared memory.
alive:system-upgrade
Upgrade ALIVE to the current version. Handles v1/v2/v3.x source states, multi-surface aware (alive-mcp / Hermes / Codex), retroactive version detection, partial-failure resume, dry-run previews, and rollback inspection.
hugging-face-dataset-creator
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, and streaming row updates. Designed to work alongside HF MCP server for comprehensive dataset workflows.