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/dafthunk-com/dafthunk/template-generatornpx skills add dafthunk-com/dafthunk --skill template-generatorgit clone --depth 1 https://github.com/dafthunk-com/dafthunkWhat 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 | $0.00013 | $0.01431 |
| Opus 5 | $0.00006 | $0.00715 |
| Sonnet 5 | $0.00003 | $0.00286 |
| Haiku 4.5 | $0.00001 | $0.00143 |
Grade A, and why
template-generator 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Template Generator Skill
Generate workflow templates: discover nodes, design graphs, wire edges correctly, create tests.
Runtime Essentials
- Topological execution: Nodes run in dependency order (edges define order)
- Data flows through edges:
sourceOutput→targetInput(port names must match exactly) - Skipping: If all upstream edges fail/skip, downstream nodes skip (not an error)
- No cycles: Runtime rejects circular dependencies
Discover Nodes
Directory structure:
packages/runtime/src/nodes/
├── input/ # TextInputNode, ImageInputNode, NumberInputNode...
├── preview/ # TextPreviewNode, ImagePreviewNode, NumberPreviewNode...
├── text/ # Summarization, translation, sentiment
├── image/ # Generation, manipulation
├── audio/ # Processing, transcription
├── anthropic/ # Claude models
├── openai/ # GPT models
├── logic/ # ConditionalForkNode, ConditionalJoinNode
└── ... # json/, math/, fetch/, browser/, etc.
Search commands:
Grep pattern="translate" path="apps/api/src/nodes" glob="*.ts"
Glob pattern="packages/runtime/src/nodes/text/*.ts"
Read node interface - look for nodeType.inputs and nodeType.outputs:
Read file_path="packages/runtime/src/nodes/text/bart-large-cnn-node.ts"
Key fields: inputs[].name → targetInput, outputs[].name → sourceOutput
Trigger Types
The type field defines how the workflow is triggered:
| Type | Description | Entry Node |
|---|---|---|
manual |
User-initiated via UI/API | Input nodes (TextInputNode, etc.) |
email_message |
Triggered by incoming email | ReceiveEmailNode |
http_request |
Triggered by HTTP request (sync) | HttpRequestNode |
http_webhook |
Triggered by webhook (async) | HttpRequestNode |
scheduled |
Triggered on schedule (cron) | ReceiveScheduledTriggerNode |
queue_message |
Triggered by queue message | ReceiveQueueMessageNode |
Finding trigger-compatible nodes: Nodes declare which triggers they work with via the compatibility field in their nodeType. Search for compatible nodes:
Grep pattern="compatibility:.*email_message" path="apps/api/src/nodes" glob="*.ts"
Grep pattern="compatibility:.*http_request" path="apps/api/src/nodes" glob="*.ts"
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 · 169 lines · 13 tokens per session scan A c112254ca074
template-generator is a skill published in the GitHub repository dafthunk-com/dafthunk (119 stars, last pushed 6d ago), licensed MIT. It adds 13 tokens to every session and 1,431 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 2026-08-30.
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