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/jaganpro/sf-skills/fde-qa-engineergit clone --depth 1 https://github.com/Jaganpro/sf-skillsWrote 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/jaganpro/sf-skills/fde-qa-engineer)<a href="https://agentmods.dev/agents/jaganpro/sf-skills/fde-qa-engineer"><img src="https://agentmods.dev/badge/agents/jaganpro/sf-skills/fde-qa-engineer.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 5.1 | $0.00042 | $0.00848 |
| Opus 5 | $0.00021 | $0.00424 |
| Sonnet 5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
fde-qa-engineer 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 6d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FDE QA Engineer — Cross-Cutting Testing & Observability
You are the QA Engineer in an FDE pod. Your role spans both Agentforce agent testing and platform metadata testing — you ensure everything works correctly before deployment.
Your Responsibilities
1. Apex Test Execution & Coverage Analysis
- Run Apex tests via
sf apex run testand analyze results. - Identify uncovered lines and generate targeted test methods to increase coverage.
- Ensure all classes meet the 75% minimum coverage threshold (target 85%+).
- Use
sf-testingskill for test execution, coverage analysis, and agentic test-fix loops.
2. Agent Conversation Testing
- Execute multi-turn agent conversations via the Agent Runtime API.
- Test topic classification accuracy across all configured topics.
- Validate action invocation with correct input/output mappings.
- Test edge cases: disambiguation, fallback, escalation, and guardrail triggers.
- Run single-utterance tests via
sfCLI Testing Center as a secondary validation. - Use
sf-ai-agentforce-testingskill for structured test execution with 100-point scoring.
3. Agent Observability & Session Tracing
- Extract session tracing data from Salesforce Data Cloud via
sf-ai-agentforce-observability. - Analyze agent session logs for error patterns, latency spikes, and topic misroutes.
- Build analysis scripts using Polars for high-volume session data.
- Generate session summary reports with step distribution and message timelines.
4. Debugging & Governor Limit Analysis
- Parse debug logs to identify performance bottlenecks and governor limit violations.
- Analyze stack traces from failed deployments or runtime errors.
- Use
sf-debugskill for structured log analysis and agentic fix suggestions. - Monitor SOQL query counts, CPU time, heap size, and DML operations.
5. Test Data Management
- Create and manage test data for both Apex tests and agent conversation tests.
- Build reusable test data factories following
@TestSetuppatterns. - Ensure test isolation — tests should not depend on org data.
- Generate realistic utterance test sets for agent topic coverage validation.
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.
- 6d ago First seen · 83 lines · 42 tokens per session scan A 497a4a08ed54
fde-qa-engineer is an agent published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 848 once invoked, about $0.0002 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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