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/x-zero-l/agent-skills/auto-commitnpx skills add X-Zero-L/agent-skills --skill auto-commitgit clone --depth 1 https://github.com/X-Zero-L/agent-skillsWhat 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 | $0.00053 | $0.01188 |
| Opus 5 | $0.00026 | $0.00594 |
| Sonnet 5 | $0.00011 | $0.00238 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
auto-commit 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 2d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Commit
Automatically analyze git working tree, group changes into logical batches, commit each batch with a concise message, and push to remote. No Co-Authored-By or any AI attribution is added.
When to Use
- User says "auto commit", "batch commit", "commit and push", "提交代码", "自动提交"
- User triggers
/auto-commit
Workflow
Step 1: Analyze Git Status
Run these commands in parallel to understand the current state:
git status --short
git diff --stat
git diff --cached --stat
git log --oneline -5
git branch --show-current
Step 2: Identify Logical Groups
Analyze all changed files (staged + unstaged + untracked) and group them by logical concern. Common grouping strategies:
- By module/feature: Files that belong to the same feature or module go together
- By type of change: Schema changes, service changes, UI changes, config changes, etc.
- By dependency: If file A depends on changes in file B, they should be in the same commit
Typical groups (adapt based on actual changes):
convex/schemas/changes → schema/type commitsconvex/repositories/+convex/services/→ backend logic commitsconvex/*.ts(functions layer) → API layer commitssrc/components/→ UI component commitssrc/routes/→ route/page commits- Config files (
package.json,tsconfig.json,vite.config.ts, etc.) → config commits - Test files → test commits
- Migration files → migration commits
Step 3: Exclude Other People's Changes
CRITICAL: Before committing, check git status carefully:
- Only commit files that are part of the current user's work
- If there are files that appear to be from other people's branches or unrelated work, skip them
- Do NOT
git restoreorgit checkoutother people's files — leave them as-is - Do NOT use
git add -Aorgit add .— always add specific files by name
Step 4: Batch Commit
For each logical group, use && to chain the git add and git commit commands in a single Bash call, ensuring they run sequentially and stop on failure:
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.
- 2d ago First seen · 116 lines · 53 tokens per session scan A 188b0cf5a4f3
auto-commit is a skill published in the GitHub repository X-Zero-L/agent-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 53 tokens to every session and 1,188 once invoked, about $0.0003 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 2026-08-31.
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