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 commands/karkranikhil/sf-ai-toolkit/prepare-prgit clone --depth 1 https://github.com/karkranikhil/sf-ai-toolkitWrote 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/commands/karkranikhil/sf-ai-toolkit/prepare-pr)<a href="https://agentmods.dev/commands/karkranikhil/sf-ai-toolkit/prepare-pr"><img src="https://agentmods.dev/badge/commands/karkranikhil/sf-ai-toolkit/prepare-pr.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 | $0.00000 | $0.00231 |
| Opus 5 | $0.00000 | $0.00115 |
| Sonnet 5 | $0.00000 | $0.00046 |
| Haiku 4.5 | $0.00000 | $0.00023 |
Grade A, and why
prepare-pr 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.
What it actually says
/prepare-pr
Summarise this branch's changes and prepare it for pull request.
What to produce
1. Change summary
List every file changed. For each file:
- Type of change (new / modified / deleted)
- One-sentence description of what changed and why
2. Apex tests
- Which test classes cover these changes?
- What is the expected code coverage?
- Run command:
npm run test:apex
3. Deployment impact
- What metadata components are included?
- Any destructive changes?
- Any Profiles (flag if yes — requires review)?
- Estimated deployment time?
- Any dependencies (packages, other orgs, data)?
4. Risks
- Security concerns?
- Governor limit risks?
- User-visible changes?
- Production risk level: Low / Medium / High
5. Checklist
- Tests written and passing
- Security reviewed
- Deployment validated
- Profiles excluded (or justified)
- No hardcoded IDs
- No secrets exposed
- Reviewer assigned
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 · 43 lines · 0 tokens per session scan A c85c77962723
prepare-pr is a command published in the GitHub repository karkranikhil/sf-ai-toolkit (2 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 231 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 2026-08-31.
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