agent-control-channels

agent-control-channels is a skill for Claude Code, Codex from companion-inc/introspect. It costs 104 tokens per session (1,494 once invoked), scanned A, original, MIT.

A guide for sending an AI agent’s decisions and data to its host application in structured form, such as JSON with a defined shape or a tool call.

In plain words
What is it for?
Building or debugging agents that trigger actions, change state, choose a route, or return data that an application displays.
Why use it?
It avoids leaking control signals into user-facing text and removes the need to guess the agent’s intent by parsing changing prose.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Building or debugging agents that trigger actions, change state, choose a route, or return data that an application displays.

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Install with agentmods
npx agentmods add skills/companion-inc/introspect/agent-control-channels
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.

Any agent
npx skills add companion-inc/introspect --skill agent-control-channels
Clone the repo
git clone --depth 1 https://github.com/companion-inc/introspect

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-control-channels

README.md
[![agentmods](https://agentmods.dev/badge/skills/companion-inc/introspect/agent-control-channels/github.svg)](https://agentmods.dev/skills/companion-inc/introspect/agent-control-channels)
Your own site
<a href="https://agentmods.dev/skills/companion-inc/introspect/agent-control-channels"><img src="https://agentmods.dev/badge/skills/companion-inc/introspect/agent-control-channels/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for agent-control-channels

Your own site · 80×15
<a href="https://agentmods.dev/skills/companion-inc/introspect/agent-control-channels"><img src="https://agentmods.dev/badge/skills/companion-inc/introspect/agent-control-channels.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,494 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00104 $0.01494
Opus 5 $0.00052 $0.00747
Sonnet 5 $0.00021 $0.00299
Haiku 4.5 $0.00010 $0.00149

Measured 10d ago against content hash cd6393476f25, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

agent-control-channels 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 10d 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.

skills/agent-control-channels/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent control channels

The one rule

When the model must signal a structured decision to the host, that signal goes through a native structured channel — a tool call for actions/control, or a strict structured response schema for pure data that the app will render. It is not a marker or prompt-only JSON object the model types into its reply.

A typed marker (an XML-ish tag, a sentinel string, <<QUIET_RUN>>, a magic word) fails three ways at once:

  • It leaks. Anything the model types into its reply is part of the reply. It streams to the user verbatim. "Why is it leaking?" is the symptom; the typed marker is the cause.
  • It's emitted unreliably. The model stutters it, half-types it, rewords it, or wraps it in prose — because it's just more text being sampled, with no schema holding its shape.
  • It forces brittle recovery. The host now regex-scrapes the prose to recover intent (extractXFromParts). That parser is permanently chasing the model's phrasing.

A tool call has none of these: it's a separate structured field, schema-validated, never rendered into the user-facing transcript, and reliably shaped because the API constrains it. The model invoking archive() cannot accidentally show the user the word "archive."

Prompt-only JSON is the same smell

Return only JSON is not a control channel. It is prose with braces. It is acceptable only as a fallback when the provider or runtime truly lacks tool calls and structured outputs.

For a menu, routing choice, suggested action list, app mode, or other host-consumed payload:

  • use a tool/function call when the model is choosing an app action, changing state, invoking code, or handing the host a command;
  • use a strict structured response schema when the model is only returning data that will be rendered, such as a UI card, menu, or explanation parts;
  • keep the host parser as schema validation, not recovery from a natural-language prompt contract.

Do not ship a prompt that says "return only JSON" while the host JSONDecoders or JSON.parses the assistant text when the runtime supports one of those native channels.

Read the full file on GitHub · 74 lines

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. 10d ago First seen · 74 lines · 104 tokens per session scan A cd6393476f25

Subscribe to this mod's changes

agent-control-channels is a skill published in the GitHub repository companion-inc/introspect (10 stars, last pushed 22d ago), licensed MIT. It adds 104 tokens to every session and 1,494 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 2026-08-31.

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