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/richfrem/agent-plugins-skills/orchestratornpx skills add richfrem/agent-plugins-skills --skill orchestratorgit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/orchestrator)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/orchestrator"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/orchestrator.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 | $0.00096 | $0.03194 |
| Opus 5 | $0.00048 | $0.01597 |
| Sonnet 5 | $0.00019 | $0.00639 |
| Haiku 4.5 | $0.00010 | $0.00319 |
Grade A, and why
orchestrator scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **CRITICAL**: When executing sub-agent commands in background or headless scripts (e.g., via `run_agent.py` or system subprocess runners), you must redirect standard input (e.g., `stdin=subprocess.DEVNULL` or `< /dev/n How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependencies
This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
Orchestrator: Loop Router & Lifecycle Manager
The Orchestrator assesses the incoming trigger, selects the right loop pattern, and manages the shared closure sequence (seal, persist, retrospective, self-improvement).
The Core Loop
Ecosystem Context
- Patterns:
learning-loop|red-team-review|dual-loop|agent-swarm|triple-loop-learning - Inner Loop Reference:
cli-agent-executor.md— Persona configs for specialized CLI execution.
Routing Decision Tree
Use this to select the correct execution pattern (for interactive navigation, invoke the select-loop-strategy skill):
1. Does the task require formal state machine transitions, human gates, worktree sandboxing, and rollback?
└─ YES → Pattern 6: graph-execution
2. Does the trigger mention unguided friction evaluation, tests, and self-optimization?
└─ YES → Pattern 5: triple-loop-learning
3. Does it need adversarial review before proceeding?
└─ YES → Pattern 2: red-team-review
4. Can the work be split into parallel independent tasks?
└─ YES → Pattern 4: agent-swarm
5. Does it require hierarchical delegation (Outer Director managing git vs. Inner Worker)?
└─ YES → Pattern 3: dual-loop
6. Is this work I can do entirely myself (research, document, iterate in single context)?
└─ YES → Pattern 1: learning-loop
| Signal | Pattern | Skill |
|---|---|---|
| Research question, knowledge gap, documentation task | Simple Learning | learning-loop |
| Architecture decision, security review, high-risk change | Red Team Review | red-team-review |
| Feature implementation, bug fix, single work package | Dual-Loop | dual-loop |
| Large feature, bulk migration, multi-concern parallel work | Agent Swarm | agent-swarm |
| Systemic rules generation, autonomous meta-optimizations | Triple-Loop | triple-loop-learning |
| State-machine transitions, worktree sandboxing, receipt gates | Graph Execution | graph-execution |
| Selecting or comparing orchestration topologies | Strategy Selector | select-loop-strategy |
What ships with it
11 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.
- acceptance-criteria.md 649 B
- evals/evals.json 798 B
- evals/results.tsv 301 B
- fallback-tree.md 1.5 KB
- references/acceptance-criteria.md 42 B
- references/agent_orchestrator.py 41 B runs code
- references/cli-agent-executor.md 41 B
- references/fallback-tree.md 36 B
- requirements.txt 22 B
- scripts/agent_orchestrator.py 38 B runs code
- scripts/swarm_run.py 29 B runs code
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 Changed · +2 lines 5a17b7ea87f2
- 6d ago First seen · 241 lines · 96 tokens per session scan A ddde4cb73f24
orchestrator is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 3,194 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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