Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/growthxai/outputnpx agentmods add skills/growthxai/output/output-dev-evaluator-functionWrote 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/growthxai/output/output-dev-evaluator-function)<a href="https://agentmods.dev/skills/growthxai/output/output-dev-evaluator-function"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-dev-evaluator-function/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/growthxai/output/output-dev-evaluator-function"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-dev-evaluator-function.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.05253 |
| Opus 5.5 | $0.00012 | $0.02101 |
| Sonnet 5.5 | $0.00006 | $0.01051 |
| Haiku 4.5 | $0.00003 | $0.00525 |
Grade A, and why
output-dev-evaluator-function 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 1mo 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 — 732 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating Evaluator Functions
Overview
This skill documents how to create evaluator functions in evaluators.ts for Output SDK workflows. Evaluators are used to assess quality, validate outputs, and provide confidence-scored judgments about workflow results.
When to Use This Skill
- Implementing quality assessment for workflow outputs
- Adding validation logic with confidence scores
- Creating LLM-powered content evaluation
- Building reusable evaluation components
File Organization
Option 1: Flat File (Default)
For smaller workflows, use a single evaluators.ts file:
src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts
├── evaluators.ts # All evaluators in one file
├── types.ts
└── ...
Option 2: Folder-Based (Large workflows)
For larger workflows with many evaluators, use an evaluators/ folder:
src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts
├── evaluators/ # Evaluators split into individual files
│ ├── quality.ts
│ ├── accuracy.ts
│ └── completeness.ts
├── types.ts
└── ...
Component Location Rules
Important: evaluator() calls MUST be in files containing 'evaluators' in the path:
src/workflows/my_workflow/evaluators.ts✓src/workflows/my_workflow/evaluators/quality.ts✓src/shared/evaluators/common_evaluators.ts✓src/workflows/my_workflow/helpers.ts✗ (cannot contain evaluator() calls)
Activity Isolation Constraints
Evaluators are Temporal activities with strict import rules to ensure deterministic replay.
Evaluators CAN import from:
- Local workflow files:
./utils.js,./types.js,./helpers.js - Local subdirectories:
./lib/helpers.js - Shared utilities:
../../shared/utils/*.js - Shared clients:
../../shared/clients/*.js - Shared services:
../../shared/services/*.js
Evaluators CANNOT import:
- Other evaluator files (activity isolation)
- Step files
- Workflow files
Example of WRONG imports:
// WRONG - evaluators cannot import other evaluators
import { otherEvaluator } from '../../shared/evaluators/other.js'; // ✗
import { anotherEvaluator } from './other_evaluators.js'; // ✗
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.
- 1mo ago First seen · 732 lines · 31 tokens per session scan A 023432a536aa
output-dev-evaluator-function is a skill published in the GitHub repository growthxai/output (443 stars, last pushed yesterday), licensed Apache-2.0. It adds 31 tokens to every session and 5,253 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
ax-ai
This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, routing, adaptive balancing, presets, embeddings, batch audio with ai.transcribe() or ai.speak(), extended thinking, context caching, or mentions…
ax-agent-rlm
This skill helps an LLM generate correct AxAgent RLM/runtime code using @ax-llm/ax. Use when the user asks about RLM code execution, AxJSRuntime, contextFields, contextPolicy, liveRuntimeState, promptLevel, stage prompt controls, executorModelPolicy, maxRuntimeChars, agent.test(...), llmQuery(...), recursionOptions…
ax-agent
This skill helps an LLM generate correct core AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, clarification, bubbleErrors, host-side final/clarification protocol, or ordinary agent runtime behavior. For MCP clients, native runtime modules…
ax-cpp-ai
Use when writing C++ code with axllm for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers.
ax-cpp-gen
Use when writing C++ code with axllm for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.
ax-go-gen
Use when writing Go code with github.com/ax-llm/ax/packages/go for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.