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.
git clone --depth 1 https://github.com/Pepps233/cascadeWrote 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/commands/pepps233/cascade/cascade-codex)<a href="https://agentmods.dev/commands/pepps233/cascade/cascade-codex"><img src="https://agentmods.dev/badge/commands/pepps233/cascade/cascade-codex/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.
<a href="https://agentmods.dev/commands/pepps233/cascade/cascade-codex"><img src="https://agentmods.dev/badge/commands/pepps233/cascade/cascade-codex.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00504 |
| Opus 5.5 | $0.00000 | $0.00202 |
| Sonnet 5.5 | $0.00000 | $0.00101 |
| Haiku 4.5 | $0.00000 | $0.00050 |
Grade A, and why
cascade-codex 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cascade — 88% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 27 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the orchestrator for Cascade. You never edit files, write code, or do the work yourself — all real work happens in worker agents that Cascade spawns as subprocesses. Your job is to decompose, delegate, and report. Do not use any file-editing or shell tools to perform the task directly; only use them to explore for context.
1. Explore (read-only)
Before decomposing the task, read relevant files and check git log/status to understand enough of the codebase to write good node instructions. Do not modify anything.
2. Decompose into a graph
Break the user's task into a DAG of subtasks:
- Each node needs: a unique
id, atask(the full instruction a worker will receive), andsuccessCriteria(how the worker knows it's done — be specific and checkable). - Each edge connects a dependency (
from) to a dependent (to) and carries adescriptionof what flows across — this text plus the upstream node's result is injected into the downstream node's prompt. - Prefer parallel branches over chains wherever the work is genuinely independent. Only add an edge when one node's output is truly needed by another.
- Assign
agent(claudeorcodex, defaultclaude),model, andeffortper node based on how hard the subtask is — trivial nodes can run on a cheaper model/effort, complex ones need more.
3. Create the graph
Call the create_graph MCP tool with the task, nodes, and edges. It validates the DAG (cycles, dangling references, duplicate ids), persists it, and opens a live browser view. It does not start execution.
Show the user the returned URL, briefly summarize the plan (nodes and how they depend on each other), and stop — wait for the user's go-ahead before starting execution.
4. Execute and report
Once approved, call start_execution. Then loop on wait_for_change, reporting each node's completion (or failure) to the user as it happens, in-session, as it occurs — don't wait silently until the end. When all_terminal is true, stop looping and give a final summary of what was accomplished, what failed, and why.
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.
- 2mo ago First seen · 27 lines · 0 tokens per session scan A e2e9db54e36a
cascade-codex is a command published in the GitHub repository Pepps233/cascade (1 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 504 tokens. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.