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.
git clone --depth 1 https://github.com/javiermrcom/awesome-claude-codeWrote 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/javiermrcom/awesome-claude-code/pr)<a href="https://agentmods.dev/commands/javiermrcom/awesome-claude-code/pr"><img src="https://agentmods.dev/badge/commands/javiermrcom/awesome-claude-code/pr/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/commands/javiermrcom/awesome-claude-code/pr"><img src="https://agentmods.dev/badge/commands/javiermrcom/awesome-claude-code/pr.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 | $0.00012 | $0.02293 |
| Opus 5.5 | $0.00005 | $0.00917 |
| Sonnet 5.5 | $0.00002 | $0.00459 |
| Haiku 4.5 | $0.00001 | $0.00229 |
Grade A, and why
pr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Smart Pull Request Command
This command creates a GitHub PR by:
- Analyzing the diff between branches
- Reading commit messages to understand intent
- Detecting Linear issues from session context, branch names, and commits
- Automatically linking detected Linear issues
- Generating an intelligent title and description
- Creating the PR using GitHub CLI
Task
Create a pull request following these steps:
1. Gather Information
First, collect all necessary information:
# Get current branch if not specified
BRANCH="${branch:-$(git rev-parse --abbrev-ref HEAD)}"
# Get default base branch if not specified
BASE="${base:-$(git symbolic-ref refs/remotes/origin/HEAD | sed 's@^refs/remotes/origin/@@')}"
# Get repository info
REPO_INFO=$(gh repo view --json owner,name)
REPO_OWNER=$(echo "$REPO_INFO" | jq -r '.owner.login')
REPO_NAME=$(echo "$REPO_INFO" | jq -r '.name')
# Get the diff
DIFF=$(git diff "$BASE...$BRANCH")
# Get commit messages
COMMITS=$(git log "$BASE..$BRANCH" --pretty=format:"- %s" --reverse)
2. Detect Linear Issues
Analyze multiple sources to detect related Linear issues:
Detection Sources (in priority order):
- Session Context: Scan the current conversation for Linear issue references (IA-XXX, ENG-XXX, etc.)
- Branch Name: Extract issue IDs from branch name patterns:
feature/IA-123-descriptionIA-456/fix-somethingbugfix/IA-789
- Commit Messages: Look for issue references in commit messages:
feat: add feature (IA-123)Closes IA-456Related to IA-789
Detection Logic:
# Pattern for Linear issues (TEAM-NUMBER format)
LINEAR_PATTERN="[A-Z]{2,4}-[0-9]+"
# Extract from session context (highest priority)
# Note: This requires analyzing the conversation history for Linear issue mentions
# Extract from branch name
BRANCH_ISSUES=$(echo "$BRANCH" | grep -oE "$LINEAR_PATTERN" | head -1)
# Extract from commit messages
COMMIT_ISSUES=$(echo "$COMMITS" | grep -oE "$LINEAR_PATTERN" | sort -u)
# Combine all detected issues (session context takes priority)
DETECTED_ISSUES="$SESSION_ISSUES $BRANCH_ISSUES $COMMIT_ISSUES"
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 · 295 lines · 12 tokens per session scan A b3c6b785db93
pr is a command published in the GitHub repository javiermrcom/awesome-claude-code (3 stars, last pushed 1y ago), licensed MIT. It adds 12 tokens to every session and 2,293 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
speckit.spex.submit
Push and create PR for team review, with optional watch mode for CI monitoring.
git
The pre-finish status: branch, hygiene findings, message checks, workflow lint, template state.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
release-tag
Create and push a version tag after a release PR has been merged.
factory
Linha de montagem multi-agente (Software Factory) — spec-writer → builder em worktree isolado → 4 revisores paralelos (Security, Code, UI, UX) → approver com gate de risco e dashboard visual.
repo-audit
Audit a codebase (local or remote GitHub/GitLab) against architecture principles and requirements, surfacing drift, risk, and missing decisions.