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 jamesyang124/agent-skills --skill graphify-monitorgit clone --depth 1 https://github.com/jamesyang124/agent-skillsWrote 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/jamesyang124/agent-skills/graphify-monitor)<a href="https://agentmods.dev/skills/jamesyang124/agent-skills/graphify-monitor"><img src="https://agentmods.dev/badge/skills/jamesyang124/agent-skills/graphify-monitor/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/jamesyang124/agent-skills/graphify-monitor"><img src="https://agentmods.dev/badge/skills/jamesyang124/agent-skills/graphify-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00073 | $0.02268 |
| Opus 5 | $0.00036 | $0.01134 |
| Sonnet 5 | $0.00015 | $0.00454 |
| Haiku 4.5 | $0.00007 | $0.00227 |
Grade B, and why
graphify-monitor scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls .agents/skills/graphify/SKILL.md 2>/dev/null || ls ~/.claude/skills/graphify/SKILL.md 2>/dev/null || echo "SKILL_NOT_FOUND" How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graphify Monitor
Builds a live knowledge graph of the current project and maintains a background subagent that detects and reports newly-discovered structural changes every 30 seconds. The monitor runs in an isolated subagent so it does not compete with the user's main session or other agents.
When to Use
Invoke when another coding agent is actively modifying a project and you want to track structural knowledge changes in real time.
- Trigger:
/graphify-monitor - Stop:
/graphify-monitor stop
Stop Mode (argument: "stop")
If invoked with the argument stop:
-
Resolve the project root (required for correct sentinel path):
git rev-parse --show-toplevel 2>/dev/null || pwdStore as
STOP_PROJECT_PATH. -
Write the stop sentinel using the absolute path:
touch "$STOP_PROJECT_PATH/graphify-out/.monitor-stop" -
Confirm:
Stop signal sent. The background monitor will exit on its next 30-second cycle. To restart: /graphify-monitor -
Exit — do not proceed to Phase 1.
Phase 1: Dependency Setup
1.1 Check Python runtime (macOS only)
uname -s
If output is Darwin, check whether uv and python3.12 are installed:
uv --version 2>/dev/null && python3.12 --version 2>/dev/null || echo "MISSING"
If MISSING, hint the user:
python3.12 and uv are required as a graphify runtime dependency.
Install with: brew install [email protected] uv
Install now? (y/n)
- If
y: runbrew install [email protected] uv - If
n: tell the user to install these manually, then stop.
If not Darwin, skip — uv and python3.12 must already be in PATH.
1.2 Check and install the graphify-ts CLI
Check whether the CLI is installed:
graphify --version 2>/dev/null || echo "CLI_NOT_FOUND"
If found: print ✅ graphify CLI already installed and continue to 1.3.
If CLI_NOT_FOUND: hint the user:
The graphify CLI is not installed. It is required to build and update the knowledge graph.
Package: graphify-ts (npm)
Install: npm i -g graphify-ts
Install now? (y/n)
- If
y: runnpm i -g graphify-ts. If it fails, report the error and stop. - If
n: tell the user to install it manually and stop.
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.
- 12d ago First seen · 265 lines · 73 tokens per session scan B ecc8749329ad
graphify-monitor is a skill published in the GitHub repository jamesyang124/agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 2,268 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.