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 skills/keploy/keploy/keploy-pr-workflownpx skills add keploy/keploy --skill keploy-pr-workflowgit clone --depth 1 https://github.com/keploy/keployWhat 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 | $0.00071 | $0.00982 |
| Opus 5 | $0.00036 | $0.00491 |
| Sonnet 5 | $0.00014 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
keploy-pr-workflow 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 yesterday.
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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
keploy-pr-workflow
When to use
- About to run
gh pr create,gh pr edit, orgh issue create. - Writing a commit message that will land in
main. - Reviewing your own diff before pushing.
- Copying error output, logs, or sample data into a PR description, issue, test fixture, or README.
1. Customer-data hygiene (non-negotiable)
Keploy records real user applications. Traces, mocks, recordings, and logs routinely carry customer data — headers with auth tokens, bodies with PII, internal hostnames, request IDs that map back to users. Treat every fixture, log snippet, and error dump as tainted until you've checked it.
Before anything leaves your machine, scrub for:
- Credentials — API keys, bearer tokens, JWTs, DB passwords, session
cookies, OAuth client secrets, AWS/GCP/Azure keys. If a test needs one,
read from env; use placeholders in docs (
sk-xxxxxxxx,Bearer <token>). - Internal hostnames and URLs —
*.internal,*.prod,*.corp, real company domains. Useexample.com,httpbin.org, or loopback in samples. - IP addresses that aren't RFC1918 / loopback / TEST-NET — assume any
public IP in a log is traceable. Replace with
192.0.2.1(TEST-NET-1). - User identifiers — emails, usernames, account IDs, order IDs, customer
names. Substitute with
[email protected],user-123, etc. - Request/trace IDs — these tie back to real traffic in observability systems. Redact them from pasted logs.
- Real recorded traffic — never commit a customer's
keploy/test-set-*directory. Even anonymized ones tend to keep giveaways in paths or timings. If you need sample recordings, generate them againstsamples-go,samples-python, etc. - Stack traces from production runs — they leak file paths, binary versions, and sometimes in-memory values.
If you're unsure whether something is customer-derived, it is. Err on the side of redaction — you can always add detail back, you can't un-publish.
2. Commit messages
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.
- yesterday First seen · 93 lines · 71 tokens per session scan A 16a7f9c2d212
keploy-pr-workflow is a skill published in the GitHub repository keploy/keploy (18,436 stars, last pushed 2d ago), licensed Apache-2.0. It adds 71 tokens to every session and 982 once invoked, about $0.0004 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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