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/ashaychangwani/imprint/prnpx skills add ashaychangwani/imprint --skill prgit clone --depth 1 https://github.com/ashaychangwani/imprintWrote 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/ashaychangwani/imprint/pr)<a href="https://agentmods.dev/skills/ashaychangwani/imprint/pr"><img src="https://agentmods.dev/badge/skills/ashaychangwani/imprint/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 | $0.00018 | $0.00417 |
| Opus 5 | $0.00009 | $0.00209 |
| Sonnet 5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
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 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.
What it actually says
PR Skill
Open a pull request for the current branch with proper conventions.
Step 1 — Pre-flight checks
Run these in parallel and report results:
bun run lint
bun run typecheck
bun test
If any fail, show the errors and ask the user whether to proceed or fix first.
Step 2 — Gather context
Run these to understand the branch:
git branch --show-current— current branch namegit log main..HEAD --oneline— commits on this branchgit diff main..HEAD --stat— files changed
Step 3 — Determine PR title
The PR title must be a conventional commit format: <type>(<scope>): <description>
Pick type and scope the same way as the /commit skill, but based on the overall branch intent (not individual commits).
Step 4 — Write PR description
Use this structure:
## Summary
- Bullet 1: what changed and why
- Bullet 2: key design decisions
- Bullet 3: anything reviewers should pay attention to
## Test plan
- [ ] Tests pass locally
- [ ] Specific scenario tested
Step 5 — Check remote
Run git remote -v and git rev-parse --abbrev-ref --symbolic-full-name @{u} 2>/dev/null to check if the branch is pushed.
If not pushed, run git push -u origin <branch>.
Step 6 — Create PR
gh pr create --title "<title>" --body "$(cat <<'EOF'
<body>
EOF
)"
Show the PR URL to the user when done.
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 · 72 lines · 18 tokens per session scan A b5914b723b9d
pr is a skill published in the GitHub repository ashaychangwani/imprint (21 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 417 once invoked, about $0.0001 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.
Other skills, from other repositories
writing-gauntlet-stories
Use when writing or reviewing a Gauntlet story card. Establishes the calibration framing — outcomes, persona binding, observable acceptance criteria — and explicitly rules out wrong-shaped frames (Agile user stories, QA step-scripts, BDD Given/When/Then) so prior knowledge does not contaminate the card.
x402
Set up Browser Use Cloud payments with x402 — pay per request from a crypto wallet (USDC on Base mainnet), no signup or API key. Two setups it works out up front — "just use it" (set up a wallet so you or Claude Code can run cloud browser tasks paid from the wallet — Claude writes and runs throwaway scripts, nothing…
browser-use
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work.
cloud
Documentation reference for using Browser Use Cloud — the hosted API and SDK for browser automation. Use this skill whenever the user needs help with the Cloud REST API (v2, v3, or v4), browser-use-sdk (Python or TypeScript), X-Browser-Use-API-Key authentication, cloud sessions, browser profiles, profile sync, CDP…
open-source
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…