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/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/ralph)<a href="https://agentmods.dev/commands/naimkatiman/continuous-improvement/ralph"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/ralph/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/naimkatiman/continuous-improvement/ralph"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/ralph.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 | $0.00018 | $0.00661 |
| Opus 5.5 | $0.00007 | $0.00264 |
| Sonnet 5.5 | $0.00004 | $0.00132 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
ralph 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ralph
Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.
Subcommands
/ralph init
Initialize Ralph in your project:
- Create
scripts/ralph/directory - Add
ralph.shloop script - Add prompt templates for Amp and Claude Code
- Create example
prd.json
/ralph convert <prd-file>
Convert a markdown PRD to Ralph's executable JSON format:
/ralph convert tasks/prd-auth-feature.md
Output: prd.json with structured user stories
/ralph run [iterations]
Run the autonomous loop:
/ralph run 10
Default: 10 iterations. Stops early if all stories complete.
Ralph Loop Behavior
- Create branch from PRD
branchName - Pick highest priority story where
passes: false - Implement story — fresh context, no pollution
- Run quality checks — typecheck, tests, lint
- Commit if passing — atomic commits per story
- Update prd.json — mark
passes: true - Log learnings — append to
progress.txt - Repeat until done or max iterations
Key Files
| File | Purpose |
|---|---|
prd.json |
Executable PRD with user stories |
progress.txt |
Accumulated learnings |
ralph.sh |
The loop script |
AGENTS.md |
Iteration memory (auto-updated) |
Workflow Integration
Ralph works best with:
- Superpowers — for structured development stages
- continuous-improvement — for reflection and learning between iterations
- workspace-surface-audit — to verify capabilities before starting
Critical Concepts
Fresh Context Per Iteration
Each story runs in isolation. Previous work is visible only via git history and prd.json.
AGENTS.md Updates
Ralph updates AGENTS.md after each story so subsequent iterations know what's already done.
Browser Verification
For UI stories, Ralph starts a dev server and uses Playwright to verify rendering.
Stop Conditions
- All stories pass
- Max iterations reached
- Critical failure (requires human intervention)
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 · 104 lines · 18 tokens per session scan A 02596baabe7c
ralph is a command published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 661 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
ap-swarm
Run a MultiAgentRuntime swarm in file mode — local journal at .runtime/conversation.md, no network.
reflect-promote
Promote learnings from skills to global CLAUDE.md.
reflect-meta
Show meta-learning statistics and pattern performance.
reflect-stats
Show cross-skill learning statistics.
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.