fireworks-tech-graph is an agent skill that turns natural-language descriptions into validated technical diagrams and related visual formats. It is used to create SVG, PNG, animated GIF, and offline interactive HTML diagrams, including UML and AI or agent workflows.
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 yizhiyanhua-ai/fireworks-tech-graph --skill fireworks-tech-graphgit clone --depth 1 https://github.com/yizhiyanhua-ai/fireworks-tech-graphWrote 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/yizhiyanhua-ai/fireworks-tech-graph/fireworks-tech-graph)<a href="https://agentmods.dev/skills/yizhiyanhua-ai/fireworks-tech-graph/fireworks-tech-graph"><img src="https://agentmods.dev/badge/skills/yizhiyanhua-ai/fireworks-tech-graph/fireworks-tech-graph/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/yizhiyanhua-ai/fireworks-tech-graph/fireworks-tech-graph"><img src="https://agentmods.dev/badge/skills/yizhiyanhua-ai/fireworks-tech-graph/fireworks-tech-graph.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 | $0.00128 | $0.07908 |
| Opus 5 | $0.00064 | $0.03954 |
| Sonnet 5 | $0.00026 | $0.01582 |
| Haiku 4.5 | $0.00013 | $0.00791 |
Grade A, and why
fireworks-tech-graph 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 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.
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 — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fireworks Tech Graph
Generate geometry-checked SVG technical diagrams, high-resolution PNG, validated SVG-to-GIF semantic motion, and sanitized offline interactive HTML.
Runtime Compatibility
Use this repository unchanged in both Codex and Claude Code. It follows the Agent Skills layout: SKILL.md is the shared entry point, bundled resources use relative paths, and agents/openai.yaml adds optional Codex UI metadata without affecting Claude Code.
Before reading a reference or running a script, resolve the directory containing this SKILL.md as SKILL_ROOT. Do not assume the current working directory is the skill directory, and do not assume a variable set in one shell call persists into the next.
- In Claude Code, use
${CLAUDE_SKILL_DIR}. - In Codex, use the absolute skill directory shown in the loaded skill metadata.
Every command block below sets SKILL_ROOT itself. In Codex, replace /absolute/path/from-codex-skill-metadata with the absolute skill directory before running the command.
Helper Scripts (Recommended)
The unified scripts/fireworks.py CLI and compatibility helpers provide stable rendering, geometry validation, inspection, animation, and export:
1. generate-diagram.sh - Validate SVG + export PNG
SKILL_ROOT="${CLAUDE_SKILL_DIR:-/absolute/path/from-codex-skill-metadata}"
"$SKILL_ROOT/scripts/generate-diagram.sh" -t architecture -s 1 -o ./output/arch.svg
- Validates an existing SVG file
- Exports PNG after validation
- Example:
"$SKILL_ROOT/scripts/generate-diagram.sh" -t architecture -s 1 -o ./output/arch.svg
2. generate-from-template.py - Create starter SVG from template
SKILL_ROOT="${CLAUDE_SKILL_DIR:-/absolute/path/from-codex-skill-metadata}"
mkdir -p ./output
python3 "$SKILL_ROOT/scripts/generate-from-template.py" architecture ./output/arch.svg '{"title":"My Diagram","nodes":[],"arrows":[]}'
- Loads a built-in SVG template
- Renders nodes, arrows, and legend entries from JSON input
- Escapes text content to keep output XML-valid
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 · 501 lines · 128 tokens per session scan A fcb6ea52730a
fireworks-tech-graph is a skill published in the GitHub repository yizhiyanhua-ai/fireworks-tech-graph (11,352 stars, last pushed 6d ago), licensed MIT. It adds 128 tokens to every session and 7,908 once invoked, about $0.0006 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.
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