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/Taoidle/plan-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/taoidle/plan-cascade/mega-approve)<a href="https://agentmods.dev/commands/taoidle/plan-cascade/mega-approve"><img src="https://agentmods.dev/badge/commands/taoidle/plan-cascade/mega-approve/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/taoidle/plan-cascade/mega-approve"><img src="https://agentmods.dev/badge/commands/taoidle/plan-cascade/mega-approve.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 | $0.00110 | $0.11657 |
| Opus 5.5 | $0.00044 | $0.04663 |
| Sonnet 5.5 | $0.00022 | $0.02331 |
| Haiku 4.5 | $0.00011 | $0.01166 |
Grade A, and why
mega-approve 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 1,418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Approve Mega Plan and Start Execution
Approve the mega-plan and begin executing features in batch-by-batch order with FULL AUTOMATION.
Execution Flow Parameters
This command accepts flow control parameters that propagate to ALL feature executions:
Parameter Priority
Parameters are resolved in the following order when multiple sources exist:
-
Command-line flags to THIS command (highest priority)
- Example:
/plan-cascade:mega-approve --flow full --tdd on - Overrides all other sources
- Example:
-
mega-plan.json configuration (saved by
/plan-cascade:mega-plan)flow_config,tdd_config,spec_config,execution_config- Used when no command-line flag is provided
-
Default values (lowest priority)
Priority Resolution Example:
# Scenario 1: Command-line overrides mega-plan
mega-plan.json contains: flow_config.level = "standard"
You run: /plan-cascade:mega-approve --flow full
Result: All features use FLOW = "full"
# Scenario 2: Use mega-plan config
mega-plan.json contains: tdd_config.mode = "on"
You run: /plan-cascade:mega-approve
Result: All features use TDD = "on"
# Scenario 3: Partial override
mega-plan.json contains: flow="full", tdd="on"
You run: /plan-cascade:mega-approve --flow standard
Result: All features use FLOW = "standard", TDD = "on"
Parameter Propagation to Features:
- Resolved parameters are passed to PRD generation agents via prompt
- PRD generation agents write these parameters to each feature's
prd.json - Story execution agents read parameters from their feature's
prd.json
Special Case - Spec Interview Parameters:
--spec,--first-principles,--max-questionsare orchestrator-only- Used in Step 6.0 (this command) to run spec interviews
- NOT propagated to feature agents (see Step 2.0.2 notes)
--flow <quick|standard|full>
Override the execution flow depth for all feature approve phases.
| Flow | Gate Mode | AI Verification | Code Review | Test Enforcement |
|---|---|---|---|---|
quick |
soft | disabled | no | no |
standard |
soft | enabled | no | no |
full |
hard | enabled | required | required |
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.
- 1mo ago First seen · 1,418 lines · 110 tokens per session scan A ffc8827697b0
mega-approve is a command published in the GitHub repository Taoidle/plan-cascade (133 stars, last pushed 7mo ago), licensed MIT. It adds 110 tokens to every session and 11,657 once invoked, about $0.0004 per session on Opus 5.5. 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 commands, from other repositories
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review-pr
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