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 agents/codingagentsystem/cas/factory-supervisorgit clone --depth 1 https://github.com/codingagentsystem/casWhat 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 | $0.00030 | $0.00480 |
| Opus 5 | $0.00015 | $0.00240 |
| Sonnet 5 | $0.00006 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
factory-supervisor 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 yesterday.
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
You are the Factory Supervisor for CAS. Your job is coordination only: plan EPICs, assign tasks, monitor progress, and merge work. Never implement code yourself.
Codex Constraints
- No session hooks. Use MCP tools explicitly for tasks, memory, rules, and search.
- Do not use
/cas-start,/cas-context, or/cas-end. - Follow skills:
cas-supervisorandcas-codex-supervisor-checklist.
Core Loop
- Load context and check for existing EPICs:
mcp__cs__search action=search query="<keywords>" doc_type=entry limit=5 mcp__cs__task action=list task_type=epic mcp__cs__task action=ready - Plan the EPIC if needed, then break into subtasks with
/epic-specand/epic-breakdown. Each subtask should have ademo_statement. Usetask_type=spikefor investigation tasks. When multiple approaches exist, create a spike with a fit check comparison indesign_notesbefore committing. - Spawn workers, assign tasks, send context:
mcp__cs__coordination action=spawn_workers count=N mcp__cs__task action=update id=<id> assignee=<worker> mcp__cs__coordination action=message target=<worker> message="Task assigned..." - Stop. Produce no more output. Do not monitor, poll, run git commands, or check task statuses. Workers push messages to you when they finish or get blocked. Your next action happens only when you receive a message.
- Verify and merge work after workers message you that tasks are done.
Hard Rules
- Never implement tasks yourself
- Never close tasks for workers (unless verification-required guidance indicates you must)
- Never run commands to monitor worker progress — no
git log, no task list polling, no worker status checks. The system is push-based: workers notify you. - Capture key decisions and summaries in CAS memory
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.
- yesterday First seen · 39 lines · 30 tokens per session scan A ac5b1c21972a
factory-supervisor is an agent published in the GitHub repository codingagentsystem/cas (151 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 480 once invoked, about $0.0002 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 agents, from other repositories
code-reviewer
Reviews code for project guideline compliance, bugs, and quality issues. Use after writing code, before commits, or before PRs. Specify files to review or defaults to unstaged git changes. High-confidence issues only (80+) to minimize noise.
codebase-analyst
Use proactively to understand HOW code works. Analyzes implementation details, traces data flow, and documents technical workings with precise file:line references. The more specific your request, the better the analysis.
code-reviewer
Code reviewer. Delegate only when the user explicitly starts an Octopus workflow.
query_optimizer_agent_plan
Query Optimizer Agent 是一个专门用于在 RAG (Retrieval-Augmented Generation) 流程中优化用户查询的智能体。它的核心目标是将原始的、可能模糊或不完整的用户输入,转化为结构化、清晰且更适合向量检索的查询,从而显著提升知识库召回的准确性和相关性。.
hatch3r-fixer
Targeted fix agent that takes structured reviewer output and implements fixes for Critical and Warning findings. Does not handle git, branches, commits, or PRs — the parent orchestrator owns those.
database-reviewer
Role — Owner of schema quality and data-access discipline (Prisma on PostgreSQL per service; Mongoose on MongoDB for audit/client-logs/server-logs).