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/darrenhinde/openagentscontrol/code-executionnpx skills add darrenhinde/OpenAgentsControl --skill code-executiongit clone --depth 1 https://github.com/darrenhinde/OpenAgentsControlWhat 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 | $0.00027 | $0.01080 |
| Opus 5 | $0.00014 | $0.00540 |
| Sonnet 5 | $0.00005 | $0.00216 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
code-execution 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Execution
Overview
Execute coding subtasks with self-review and quality validation. Runs in isolated coder-agent context with pre-loaded standards.
Announce at start: "I'm using the code-execution skill to implement [subtask title]."
The Process
Step 1: Read Subtask JSON
Load the subtask file specified in $ARGUMENTS:
Read: .tmp/tasks/{feature}/subtask_{seq}.json
Extract:
title— What to implementacceptance_criteria— What defines successdeliverables— Files/endpoints to createcontext_files— Standards to applyreference_files— Existing code to study
Step 2: Load Context Files
Read each file in context_files:
Read: .opencode/context/core/standards/code-quality.md
Read: .opencode/context/core/standards/security-patterns.md
Understand:
- Project coding standards
- Naming conventions
- Security patterns
- Code quality requirements
Step 3: Load Reference Files
Read each file in reference_files:
Read: src/middleware/auth.middleware.ts
Study:
- Existing patterns
- Code structure
- Conventions in use
Step 4: Update Status to In Progress
Edit subtask JSON:
"status": "in_progress",
"agent_id": "coder-agent",
"started_at": "2026-02-16T00:00:00Z"
Step 5: Implement Deliverables
For each deliverable:
- Create or modify the specified file
- Follow acceptance criteria EXACTLY
- Apply standards from context_files
- Use patterns from reference_files
- Write clean, modular, functional code
Step 6: Run Self-Review (MANDATORY)
Type & Import Validation:
- ✅ Function signatures match usage
- ✅ All imports/exports exist
- ✅ No missing type annotations
- ✅ No circular dependencies
Anti-Pattern Scan:
grep "console.log" deliverables # NO debug statements
grep "TODO\|FIXME" deliverables # NO unfinished work
grep "api[_-]key\|secret" deliverables # NO hardcoded secrets
Acceptance Criteria Check:
- Re-read
acceptance_criteriaarray - Confirm EACH criterion met
- Fix unmet criteria before proceeding
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 · 174 lines · 27 tokens per session scan A 3bb426c9e8ad
code-execution is a skill published in the GitHub repository darrenhinde/OpenAgentsControl (4,799 stars, last pushed 18d ago), licensed MIT. It adds 27 tokens to every session and 1,080 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.
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