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/cloudai-x/opencode-workflow/commitgit clone --depth 1 https://github.com/CloudAI-X/opencode-workflowWhat 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 | $0.00009 | $0.00461 |
| Opus 5 | $0.00005 | $0.00230 |
| Sonnet 5 | $0.00002 | $0.00092 |
| Haiku 4.5 | $0.00001 | $0.00046 |
Grade A, and why
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.
What it actually says
Git Commit Command
Create a well-structured git commit by analyzing staged changes and generating a conventional commit message.
Phase 1: Gather Context
Run these commands in parallel to understand the current state:
git status- See all staged and unstaged changesgit diff --cached- View the actual staged changes that will be committedgit log --oneline -5- Review recent commit message style for consistency
Phase 2: Analyze Changes
Based on the staged diff, determine:
-
Change Type (use conventional commits):
feat: New featurefix: Bug fixrefactor: Code restructuring without behavior changedocs: Documentation onlytest: Adding or updating testschore: Maintenance tasksstyle: Formatting, whitespaceperf: Performance improvementci: CI/CD changes
-
Scope (optional): Affected module or component
-
Description: Concise summary focusing on WHY, not WHAT
Phase 3: Generate Commit Message
Format: type(scope): description
Rules:
- Use imperative mood ("Add feature" not "Added feature")
- Keep first line under 72 characters
- Focus on the purpose and impact
- Add body for complex changes explaining reasoning
Phase 4: Execute Commit
- If there are unstaged changes that should be included, ask user first
- Stage any additional files if requested
- Execute the commit with the generated message
- Run
git statusto verify success
Output Format
Commit Analysis
---------------
Type: [type]
Scope: [scope or "none"]
Files: [number] files changed
Generated Message:
[commit message]
Commit Status: [success/failure]
Safety Checks
- NEVER commit files that appear to contain secrets (.env, credentials, API keys)
- WARN if committing lock files or large binary files
- CONFIRM before committing if there are untracked files that might be forgotten
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 · 73 lines · 9 tokens per session scan A 1f6590fc7a7d
commit is a command published in the GitHub repository CloudAI-X/opencode-workflow (274 stars, last pushed 7mo ago), licensed MIT. It adds 9 tokens to every session and 461 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.