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 agents/shep-ai/shep/contributor-onboardinggit clone --depth 1 https://github.com/shep-ai/shepWrote 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/agents/shep-ai/shep/contributor-onboarding)<a href="https://agentmods.dev/agents/shep-ai/shep/contributor-onboarding"><img src="https://agentmods.dev/badge/agents/shep-ai/shep/contributor-onboarding.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01282 |
| Opus 5 | $0.00000 | $0.00641 |
| Sonnet 5 | $0.00000 | $0.00256 |
| Haiku 4.5 | $0.00000 | $0.00128 |
Grade A, and why
contributor-onboarding 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 5d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contributor-Onboarding Agent
Purpose
The Contributor-Onboarding Agent is responsible for converting GitHub issues into structured, contributor-ready onboarding recommendations.
It takes raw issue data and produces a consistent grooming artifact used by downstream automation to:
- classify the issue into a lane
- estimate difficulty
- generate acceptance criteria
- suggest labels
- optionally provide a welcome comment for good-first issues
The agent is read-only and recommendation-only. It does not mutate GitHub state directly.
All side effects are handled externally via supervisor-gated workflows.
Agent Identity
| Property | Value |
|---|---|
| Agent | contributor-onboarding |
| Lane | agents |
| Registration | Custom-agent rail |
| Integration Point | packages/core/src/application/use-cases/contributors/groom-issue.use-case.ts |
| Spec | specs/097-ai-native-contributor-onboarding/spec.yaml |
The core orchestration is implemented in GroomIssueUseCase, which composes lane classification, acceptance criteria generation, and difficulty inference.
System Prompt
The agent behavior is defined in the prompt layer and should be treated as the source of truth.
Prompt files:
prompts/contributor-onboarding/system.mdprompts/contributor-onboarding/output-schema.md
These files define:
- lane classification rules
- difficulty heuristics
- acceptance criteria constraints
- label generation rules
- welcome comment behavior
This documentation intentionally does not duplicate prompt logic. Any behavioral changes must be made in the prompt files.
Output Schema
The agent outputs a structured object defined by TypeSpec:
tsp/agents/contributor-onboarding-output.tspContributorOnboardingAgentOutput(generated inpackages/core/src/domain/generated/output.ts)
Shape
{
lane: ContributorLane;
difficulty: ContributionDifficulty;
acceptanceCriteria: string;
suggestedLabels: string[];
welcomeComment?: string;
}
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.
- 5d ago First seen · 237 lines · 0 tokens per session scan A 224af1124889
contributor-onboarding is an agent published in the GitHub repository shep-ai/shep (247 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,282 tokens. 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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