ad-subagent

A workflow for drafting a custom Codex subagent, which is a separate agent with its own role and instructions. It creates the required TOML configuration and checks whether delegation is appropriate.

In plain words
What is it for?
Use it to define a repeatable reviewer, explorer, researcher, test designer, or bug reproducer in a project or personal .codex/agents/ file.
Why use it?
It avoids creating unnecessary agents and makes reusable roles, tool limits, and model settings explicit.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/corridortech/posecap/ad-subagent
Any agent
npx skills add CorridorTech/PoseCap --skill ad-subagent
Clone the repo
git clone --depth 1 https://github.com/CorridorTech/PoseCap

Made for: Claude Code, Codex.

Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,223 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00095 $0.01223
Opus 5 $0.00048 $0.00611
Sonnet 5 $0.00019 $0.00245
Haiku 4.5 $0.00010 $0.00122

Measured 2d ago against content hash 72d984be13c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ad-subagent 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.

.agents/skills/ad-subagent/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<background_information> Drafts .codex/agents/<name>.toml (project) or ~/.codex/agents/<name>.toml (personal). Spec: https://developers.openai.com/codex/subagents.

Custom Codex agents are standalone TOML files. Required fields: name, description, developer_instructions. Optional fields such as model, model_reasoning_effort, sandbox_mode, mcp_servers, skills.config, and nickname_candidates inherit from the parent session when omitted. </background_information>

Step 2 — delegation-fit gate. Build a custom subagent only when at least one is true:

  • The role repeats often enough to justify a reusable prompt.
  • The work is self-contained and can return a summary or bounded patch.
  • The role needs tool or sandbox restrictions (for example read-only review).
  • The role needs a different model, reasoning effort, MCP server, or skill preload.

Do not build a custom subagent for a one-off question, frequent back-and-forth, a tightly coupled implementation step, or work whose next action blocks on the answer. Use the main session or built-in explorer / worker instead.

Common narrow shapes:

  • Fresh-context reviewer: read-only, adversarial, returns findings only.
  • Codebase explorer: read-only path mapper; prefer built-in explorer unless the repo needs a persistent role.
  • Docs researcher: read-only, uses a docs MCP/server when configured, returns citations.
  • Test designer: reads spec/task, proposes public-interface tests, does not implement production code.
  • Bug reproducer: creates or describes the smallest failing loop, then stops.
  • Bounded worker: owns a disjoint file/module set and returns changed paths plus verification.

Step 3 — interview to fill. Ask one question per missing field, in this order:

  • Role: one sentence, "You are a that when ."
  • Description: the routing signal. Specific, includes the task framings the parent agent would recognize.
  • Developer instructions: role, scope, allowed sources, output format, stop criterion, and what not to do.
  • Sandbox: read-only, workspace-write, or inherited. A reviewer should be read-only.
  • Model / reasoning effort: omit unless the user has a reason to override parent defaults.
  • Optional MCP / skills: include only when the role cannot work without them.

Do NOT invent values. When the user does not know something, ask. Do not invent TOML fields not supported by the Codex subagents docs.

Step 4 — write the file.

Path: .codex/agents/<name>.toml or ~/.codex/agents/<name>.toml.

Use TOML. For multi-line developer_instructions, triple-quoted strings preserve indentation, so dedent the body to column 0. If a convention from AGENTS.md is load-bearing for the subagent, restate it or point to the exact file path; do not rely on implicit parent-session memory.

Template:

name = "<name>"
description = "<when Codex should use this agent>"
# model = "gpt-5.4"                 # optional; omit to inherit parent default
# model_reasoning_effort = "high"   # optional
# sandbox_mode = "read-only"        # optional
developer_instructions = """
You are a <role>.

Scope:
- <what this agent owns>

Do:
- <allowed action>

Do not:
- <explicit non-goal>

Output:
- <format the parent session expects>

Stop when:
- <stop criterion>
"""

Step 5 — stop after writing. Do not dispatch to the new subagent and do not test it. The user will exercise it themselves.

<output_contract> A single new TOML file at .codex/agents/<name>.toml (or ~/.codex/agents/<name>.toml). Required fields present: name, description, developer_instructions. Optional fields declared only when the user chose them. Body is terse, imperative, and has explicit scope, output format, and stop criterion. No unsupported TOML fields. No external dependency the user did not ask for. </output_contract>

Read the full file on GitHub · 90 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 2d ago First seen · 90 lines · 95 tokens per session scan A 72d984be13c0

Subscribe to this mod's changes

ad-subagent is a skill published in the GitHub repository CorridorTech/PoseCap (190 stars, last pushed 10d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,223 once invoked, about $0.0005 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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