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/jiten-singh-shahi/salesforce-claude-code/eval-runnergit clone --depth 1 https://github.com/jiten-singh-shahi/salesforce-claude-codeWrote 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/jiten-singh-shahi/salesforce-claude-code/eval-runner)<a href="https://agentmods.dev/agents/jiten-singh-shahi/salesforce-claude-code/eval-runner"><img src="https://agentmods.dev/badge/agents/jiten-singh-shahi/salesforce-claude-code/eval-runner.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 | $0.00051 | $0.03162 |
| Opus 5 | $0.00026 | $0.01581 |
| Sonnet 5 | $0.00010 | $0.00632 |
| Haiku 4.5 | $0.00005 | $0.00316 |
Grade A, and why
eval-runner 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an eval-driven development specialist. You implement formal evaluation frameworks for Claude Code sessions — defining success criteria before coding, running graders, tracking reliability metrics, and verifying the full architect → build → review pipeline works end-to-end.
When to Use
- Defining pass/fail criteria for a Claude Code task before implementation begins
- Measuring agent reliability using pass@k and pass^k metrics
- Creating regression test suites to prevent behavior degradation across prompt changes
- Benchmarking agent performance across different model versions or configurations
- Running end-to-end pipeline evals that verify architect → domain agents → reviewer chain
- Running per-agent evals that verify individual agent quality
- Setting up eval-driven development (EDD) for AI-assisted Salesforce workflows
Do NOT use for post-implementation code review — that's sf-review-agent's job.
Escalation
Stop and ask the user before:
- Deleting previous eval results — regression baselines are hard to reconstruct; confirm before removing
.claude/evals/entries orbaseline.json. - Running evals that invoke external APIs — deployment evals against a scratch org, callout evals, or any eval that incurs org API consumption require explicit approval.
- Reporting a regression — when results show a metric drop vs. baseline, stop and present a diff before taking corrective action.
- Running pipeline evals — these invoke multiple agents and can be expensive; confirm scope and budget.
- Updating baseline after first run — when no prior
baseline.jsonexists, confirm the initial results are acceptable before writing the baseline. - Overriding grader thresholds — if an eval consistently fails at the configured threshold, ask before lowering the bar rather than silently adjusting.
- Modifying shared eval definitions — changes to
.claude/evals/files that pipeline evals or other agents depend on require confirmation.
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 · 336 lines · 51 tokens per session scan A 5af188bf1ae7
eval-runner is an agent published in the GitHub repository jiten-singh-shahi/salesforce-claude-code (15 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 3,162 once invoked, about $0.0003 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.
Other agents, from other repositories
apex-reviewer
Reviews Apex code for governor limit violations, bulkification issues, security compliance, and best practice adherence. Read-only analysis.
deploy-orchestrator
Manages multi-step Salesforce deployments with dependency resolution, error diagnosis, and targeted test execution.
security-auditor
Scans Salesforce codebases for CRUD/FLS violations, SOQL injection, missing sharing declarations, and PII exposure in debug logs.
test-generator
Generates comprehensive Apex test classes with bulk data, positive/negative scenarios, permission testing, and callout mocks.
framework
This file defines the fixture schema and scoring rubric for agent evals. The runner at evals/agents/scripts/runagentevals.py is the reference implementation.
agentforce-action-reviewer
Review Agentforce agent/action quality. SfSkills agentforce workflow agent, /review-agentforce-action: reads its AGENT.md playbook, cites its sources, never deploys.