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/danielraffel/generous-corp-marketplaceWrote 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/danielraffel/generous-corp-marketplace/repeat-3x)<a href="https://agentmods.dev/commands/danielraffel/generous-corp-marketplace/repeat-3x"><img src="https://agentmods.dev/badge/commands/danielraffel/generous-corp-marketplace/repeat-3x/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/danielraffel/generous-corp-marketplace/repeat-3x"><img src="https://agentmods.dev/badge/commands/danielraffel/generous-corp-marketplace/repeat-3x.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 | $0.00019 | $0.01524 |
| Opus 5 | $0.00010 | $0.00762 |
| Sonnet 5 | $0.00004 | $0.00305 |
| Haiku 4.5 | $0.00002 | $0.00152 |
Grade A, and why
repeat-3x 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 8d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repeat Last Prompt (Triple)
Apply triple prompt repetition optimization to the user's last message. This variant repeats the query three times for maximum accuracy on specific task types.
What This Command Does
- Retrieves the user's last message from the conversation history
- Applies triple repetition:
<QUERY> Let me repeat that: <QUERY> Let me repeat that one more time: <QUERY> - Processes the triple-repeated prompt to improve model performance
- Provides a brief confirmation that triple repetition was applied
When to Use
Use this command specifically for tasks that benefit from maximum repetition:
Highly recommended for:
- List navigation: Finding Nth item, finding item between two others
- Pattern matching: Complex pattern recognition in lists
- NameIndex-type tasks: Locate specific item in long list
- MiddleMatch-type tasks: Find item between two others in list with repetitions
Less beneficial for:
- Simple multiple choice (use
/repeat-lastinstead) - Short queries (simple repetition sufficient)
- General fact retrieval (simple repetition sufficient)
Implementation Instructions
When this command is invoked:
-
Retrieve the last user message from the conversation history
- Get the most recent message from the user (not from Claude)
- If unavailable, inform the user that there's no previous message to repeat
-
Apply triple repetition
- Format:
<QUERY> Let me repeat that: <QUERY> Let me repeat that one more time: <QUERY> - Include framing text between each repetition
- Example: If user asked "What's the 25th item?", process it as:
What's the 25th item? Let me repeat that: What's the 25th item? Let me repeat that one more time: What's the 25th item?
- Format:
-
Process the triple-repeated prompt
- Treat the triple-repeated prompt as if it were the original user input
- Generate response based on the repeated context
- Do not include the repetition in the visible response
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.
- 8d ago First seen · 202 lines · 19 tokens per session scan A 4cf743bb50e8
repeat-3x is a command published in the GitHub repository danielraffel/generous-corp-marketplace (11 stars, last pushed 10d ago), licensed MIT. It adds 19 tokens to every session and 1,524 once invoked, about $0.0001 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.