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/sandeep-alluru/polaroid/pr-prepgit clone --depth 1 https://github.com/sandeep-alluru/polaroidWrote 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/sandeep-alluru/polaroid/pr-prep)<a href="https://agentmods.dev/commands/sandeep-alluru/polaroid/pr-prep"><img src="https://agentmods.dev/badge/commands/sandeep-alluru/polaroid/pr-prep.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 | $0.00184 |
| Opus 5 | $0.00000 | $0.00092 |
| Sonnet 5 | $0.00000 | $0.00037 |
| Haiku 4.5 | $0.00000 | $0.00018 |
Grade A, and why
pr-prep 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.
This is a copy
100% identical to pr-prep — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Run the full pre-PR checklist and report what's ready and what needs fixing.
Steps:
- Run lint:
make lint(ruff check + ruff format --check) - Run type check:
make typecheck(mypy) - Run tests:
make test(pytest with coverage) - Check CHANGELOG.md has an entry under [Unreleased] for this change
- Check that no debug print() statements or TODO comments were left in modified files
Report in this format: ✅ Lint — clean ✅ Types — clean ✅ Tests — 43 passed, 87% coverage ⚠️ CHANGELOG — no [Unreleased] entry found ✅ No debug artifacts
If anything fails, show the exact error and the file:line to fix. Do not mark the PR ready until all 5 checks pass.
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 · 19 lines · 0 tokens per session scan A bdaef57ccde0
pr-prep is a command published in the GitHub repository sandeep-alluru/polaroid (0 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 184 tokens. A static security scan graded it A with 0 findings. It is 100% identical to pr-prep, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
riskreview
The user invoked the /riskreview facade command from the risk-review-pipeline pack.
resume
Resume a previous session. Reads recent session logs, open tasks, and last decisions — gives Claude full context without re-explaining the project.
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
infogenius
Research-First Image Generation - Facts → Visual Prompt → High-Quality Image.
ops
Weekly operations loop — triage the inquiry inbox, advance the CRM pipeline, sweep open PRs across repos, and surface the content plan. Loads the ops skill.