tmb_cheatcode

A discovery process for finding an existing published skill, tool, or plugin when a project lacks a needed capability.

In plain words
What is it for?
Use it to search for, compare, and recommend an existing add-on that fits the task and codebase.
Why use it?
It helps avoid rebuilding common tools such as document extraction or cloud-service integrations from scratch.

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/trustmybot/plugin/tmb_cheatcode
Any agent
npx skills add trustmybot/plugin --skill tmb_cheatcode
Clone the repo
git clone --depth 1 https://github.com/trustmybot/plugin

Made for: Claude Code, Codex.

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,369 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 $0.00093 $0.01369
Opus 5 $0.00046 $0.00685
Sonnet 5 $0.00019 $0.00274
Haiku 4.5 $0.00009 $0.00137

Measured yesterday against content hash 8c53080875e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tmb_cheatcode 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.

skills/tmb_cheatcode/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

cheatcode

Already installed?

When the Human refers to "cheatcode(s)" directly — "do the cheatcodes work", "which cheatcodes are installed", "is X already a cheatcode" — inspect the installed registry first with cheatcode_list(agent='bro') (the cheatcodes table). That's the read surface for what's on hand; it's distinct from the discovery pipeline below (search → vet → install), which is for closing a gap that nothing installed covers.

Is the gap real?

Check the request against the project's surface — world model, installed skills/MCP, CLAUDE.md. It's a cheatcode play when:

  • The task needs a well-trodden domain with mature tooling (PDF extraction, OCR, a cloud SDK, a protocol client) and the project has nothing for it.
  • Building it in-repo would duplicate something the ecosystem already maintains.
  • The Human asked "is there a tool/skill for X."

If code you'd write anyway covers it, or a capability already on hand does, it's normal planning — route it that way.

Find and recommend

Name the capability and call cheatcode_search once — it searches, ranks, and records the audit row. Pin kind when the shape is obvious; leave it for the schema default when unsure.

Tier+relevance order is an input, not the verdict — you hold the actual requirement and know the codebase, so the pick is yours. Read what each candidate does, commit to the best fit (top two-three only if genuinely close), and lead with the reasoning plus its tier and source URL. Installing is a separate gate.

Vet before recommending

Once you've landed on a pick, vet it before putting it in front of the Human:

cheatcode_vet(agent='bro', candidate=<the pick>)

Pass the candidate straight through — the same {name, kind, source_url, tier} shape cheatcode_search handed you. One call returns a deterministic trust_tier (trusted, caution, untrusted, unknown), the capabilities[] it ships, and the audit row.

The trust_tier is a reproducible read of the signals; the install judgment is still yours. Weigh the tier against what the cheatcode would touch — capabilities[] carries the real weight, since a cheatcode shipping hooks, an MCP server, or scripts executes inside your project. If the signals come back thin (unknown, no maintainer, offline gather), say so plainly; an unconfirmed reputation is a finding the Human needs.

Read the full file on GitHub · 76 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. yesterday First seen · 76 lines · 93 tokens per session scan A 8c53080875e2

Subscribe to this mod's changes

tmb_cheatcode is a skill published in the GitHub repository trustmybot/plugin (6 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 1,369 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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