Borrowing it
Nothing to install: this file belongs to BenchBox-dev/BenchBox. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/BenchBox-dev/BenchBox/develop/.claude/skills/shared-agent-execution/SKILL.mdgit clone --depth 1 https://github.com/BenchBox-dev/BenchBoxWrote 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/benchbox-dev/benchbox/shared-agent-execution)<a href="https://agentmods.dev/skills/benchbox-dev/benchbox/shared-agent-execution"><img src="https://agentmods.dev/badge/skills/benchbox-dev/benchbox/shared-agent-execution.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 | $0.00060 | $0.01184 |
| Opus 5 | $0.00030 | $0.00592 |
| Sonnet 5 | $0.00012 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
agent-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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Execution
Use this component when another skill delegates work and needs a consistent model tier, reasoning effort, or agent harness configuration. The calling skill continues to own task decomposition, authorization, workspace isolation, and acceptance criteria.
Model Tiers
Tiers describe operating roles, not absolute quality rankings. Match models to task complexity and risk.
Selection Rule: Pick the tier here. For native dispatch, choose the listed
tier model directly. When an external harness is selected, take its exact
harness-specific identifier from
references/external-harnesses.md. Default
reasoning effort to medium. Use maximum effort only for Tier 1 adversarial
review; use low for mechanical bulk work.
- Tier 1: Strategic
- Models:
gpt-5.6-sol,claude-fable-5,grok-4.6,gemini-3.7-flash-high - Usage: Strategic planning, architecture, high-risk tradeoffs, and final adversarial review.
- Models:
- Tier 2: Generalist
- Models:
gpt-5.6-terra,claude-opus-5,grok-4.5,gemini-3.7-flash-medium,muse-spark-1.2 - Usage: Management, decomposition, integration, investigation, and routine review.
- Models:
- Tier 3: Contributor
- Models:
gpt-5.6-luna,claude-sonnet-5,gemini-3.7-flash-low,gemini-3.7-flash-tiered,muse-spark-1.2-contributor - Usage: Focused implementation, bounded research, bulk work, and parallel coverage.
- Models:
Reasoning Effort Reference
| Harness | CLI Flag / Option | Supported Values (Lowest to Highest) |
|---|---|---|
| pi | --thinking <level> |
off, minimal, low, medium, high, xhigh, max |
| claude | --effort <level> |
low, medium, high, xhigh, max |
| muse | --reasoning-effort <EFFORT> |
none, minimal, low, medium, high, xhigh, ultra |
| agy | --effort <level> |
low, medium, high |
| grok | --reasoning-effort <EFFORT> |
low, medium, high, xhigh |
| codex | -c model_reasoning_effort="<level>" |
low, medium, high, xhigh, max, ultra |
| prime-agent | --thinking <level> |
off, minimal, low, medium, high, xhigh, max |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 87 lines · 60 tokens per session scan A ef49342065c1
agent-execution is a skill published in the GitHub repository BenchBox-dev/BenchBox (12 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,184 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 2026-09-04.
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