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 skills add gvkhosla/compound-engineering-pi --skill feature-videogit clone --depth 1 https://github.com/gvkhosla/compound-engineering-piWrote 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/skills/gvkhosla/compound-engineering-pi/feature-video)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/feature-video"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/feature-video/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/feature-video"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/feature-video.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00061 | $0.03651 |
| Opus 5.5 | $0.00024 | $0.01460 |
| Sonnet 5.5 | $0.00012 | $0.00730 |
| Haiku 4.5 | $0.00006 | $0.00365 |
Grade A, and why
feature-video 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- feature-video — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 383 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Video Walkthrough
Record browser interactions demonstrating a feature, stitch screenshots into an MP4 video, upload natively to GitHub, and embed in the PR description as an inline video player.
Prerequisites
- Local development server running (e.g.,
bin/dev,npm run dev,rails server) agent-browserCLI installed (load theagent-browserskill for details)ffmpeginstalled (for video conversion)ghCLI authenticated with push access to the repo- Git repository on a feature branch (PR optional -- skill can create a draft or record-only)
- One-time GitHub browser auth (see Step 6 auth check)
Main Tasks
1. Parse Arguments & Resolve PR
Arguments: $ARGUMENTS
Parse the input:
- First argument: PR number, "current" (defaults to current branch's PR), or path to an existing
.mp4file (upload-only resume mode) - Second argument: Base URL (defaults to
http://localhost:3000)
Upload-only resume: If the first argument ends in .mp4 and the file exists, skip Steps 2-5 and proceed directly to Step 6 using that file. Resolve the PR number from the current branch (gh pr view --json number -q '.number').
If an explicit PR number was provided, verify it exists and use it directly:
gh pr view [number] --json number -q '.number'
If no explicit PR number was provided (or "current" was specified), check if a PR exists for the current branch:
gh pr view --json number -q '.number'
If no PR exists for the current branch, ask the user how to proceed. Use the platform's blocking question tool (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini):
No PR found for the current branch.
1. Create a draft PR now and continue (recommended)
2. Record video only -- save locally and upload later when a PR exists
3. Cancel
If option 1: create a draft PR with a placeholder title derived from the branch name, then continue with the new PR number:
gh pr create --draft --title "[branch-name-humanized]" --body "Draft PR for video walkthrough"
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.
- 1mo ago First seen · 383 lines · 61 tokens per session scan A d57c4fcd40a0
feature-video is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (52 stars, last pushed 25d ago), licensed MIT. It adds 61 tokens to every session and 3,651 once invoked, about $0.0002 per session on Opus 5.5. 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 skills, from other repositories
pi-package-sandbox-test
Use when verifying that published Pi packages install and load for normal users, including pi.dev package compatibility checks, release validation, Daytona sandbox tests, and package installability audits.
pi-release-workflow
Use when preparing or validating Pi monorepo releases, including Changesets, pnpm lockfile updates, package previews, release-age policy, and installability checks.
pi-review-gated-delivery
Use when coordinating review-gated delivery of discrete GitHub issues between one implementation peer and a review-only lead, with user-owned Changesets and approval-gated commits.
omk-computeruse
Route OMK computer-use and Neo tasks by verified tool and model capabilities, without a second orchestrator. Use for native desktop apps, WSL-to-Windows experiments, browser sessions, site previews, screenshots, mouse/keyboard input, structured web extraction, or questions about Neo skills and MCP setup availability.
pi-observability-local-dev
Use when developing or reviewing packages/pi-observability dashboard/server changes locally, especially rebuilding dist, restarting the 43190 server, validating UI behavior, or keeping the local dashboard in sync with source edits.
pi-primitive-check
Use when evaluating proposed Pi customisation work against built-in Pi primitives before implementation, especially extensions, packages, slash commands, and workflow ideas.