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 duc01226/EasyPlatform --skill graph-codegit clone --depth 1 https://github.com/duc01226/EasyPlatformWrote 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/duc01226/easyplatform/graph-code)<a href="https://agentmods.dev/skills/duc01226/easyplatform/graph-code"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/graph-code/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/duc01226/easyplatform/graph-code"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/graph-code.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 | $0.00056 | $0.02334 |
| Opus 5.5 | $0.00022 | $0.00934 |
| Sonnet 5.5 | $0.00011 | $0.00467 |
| Haiku 4.5 | $0.00006 | $0.00233 |
Grade A, and why
graph-code 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex compatibility note:
- Invoke
$skill-name: load the registered.agents/skills/<name>/SKILL.mdand execute it with Codex tools; no separateSkilltool or Claude mode switch is needed. Author.claude/**; mirrors are generated.- Track steps before execution and keep progress/evidence current. Explicit skill requests execute their protocol. Use the native question tool for required questions.
- Map foreign tool names to available host capabilities. A name mismatch needs no approval; an actually unavailable required capability stops with its step/evidence and a user question before adaptation.
- Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required
spawn_agentsubagent(s) for that task.- Preserve protocol order unless the user approves a deviation. Workflows use
$start-workflow: gates stay fixed; other steps may flex with logged reasons and evidence. [BLOCKING] Mode routing — detect FIRST. The mode is--mode=build|query|trace|blast-radius|connect-api|export, or the first argument when it is exactly one of those six words ($graph-code export --format=mermaid <file>). Everything after the mode is that mode's own arguments (--scope=,--direction,--node-mode,--format=,--json, a target). Read the mode file in full before anything else (see Mode Dispatch). Each of the six modes works called directly with no workflow.
Quick Summary
Goal: [Code Intelligence] Operate the structural code knowledge graph (Tree-sitter nodes and edges in .code-graph/graph.db, SQLite) through the python .claude/scripts/code_graph CLI: build or sync it, query relationships, trace system flow, analyze the blast radius of a change, match frontend calls to backend routes, and export JSON snapshots or single-file Mermaid diagrams.
Workflow: Detect the mode → read its references/mode-<x>.md in full → run the CLI live → report the JSON result with file:line evidence.
Key Rules:
- One mode per invocation. No mode and no mode word: show the Mode Dispatch table and ask which mode via
ask user question tool; a natural-language request that matches exactly one row of the Intent column selects that row's mode (the Intent column holds action phrases only: a prompt that merely names "code graph", "knowledge graph" or "uncommitted changes" carries no action and gets the table); anything else gets the table, never a guess. - Always pass
--jsonto the CLI so the output is structured and parseable. - Graph not built (
.code-graph/graph.dbabsent): report plainly — "graph not built — run $graph-code --mode=build, or continue with grep" — and stop that mode. It is never an error for a caller. - MUST ATTENTION keep claims evidence-based (
file:line) with confidence >80% to act; MUST ATTENTION keep task tracking updated as each step starts/completes.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 83 lines · 56 tokens per session scan A c46351744740
graph-code is a skill published in the GitHub repository duc01226/EasyPlatform (12 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 2,334 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-18.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…