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.
npx agentmods add skills/alecs5am/ralphy/dev-loopnpx skills add alecs5am/ralphy --skill dev-loopgit clone --depth 1 https://github.com/alecs5am/ralphyWhat 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 | $0.00242 | $0.01785 |
| Opus 5 | $0.00121 | $0.00892 |
| Sonnet 5 | $0.00048 | $0.00357 |
| Haiku 4.5 | $0.00024 | $0.00178 |
Grade A, and why
dev-loop 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dev-loop — autonomous issue-driven dev cycle
A maintainer skill. It executes the open work in notes/issues/ (the inbox /dev-issues fills), one issue at a time, committing after each. It does not touch .ralphy/workspaces/<ws>/projects/ or call media models except where an issue explicitly requires it (and then only behind the paid-generation gate below).
Scope + limit
- Default scope: every open issue at the flat top level of
notes/issues/. Thenotes/issues/done/andnotes/issues/deprecated/subfolders are the closed archive — never in scope (status is encoded by folder; see Issues folder layout). - Default limit: 25 issues per loop run. If the user's prompt names more (or "no limit", or a specific subset / range), honor that.
- The user can narrow scope in their prompt ("just the landing ones", "only #052-#055", "everything tagged cli").
Workflow (per run)
-
Read the rules first.
docs/developing-ralphy.md(English-only, the lint suite, append-only contract, auto-generated files),AGENTS.md(the hard invariants — especially "wait for user 'go' before any paid generation"), andnotes/README.md(including the Issues folder layout — active work is the flat top level only). Skim every candidate issue. -
Build the execution order. Sort issues by dependency, not by number:
- Foundational / schema / data-model changes FIRST (everything keys off them).
- Cross-cutting renames or moves NEXT, as a single coordinated step (never parallelize these).
- Independent, low-collision, and plan-only/docs issues can go any time.
- Read each issue's
## Notesfor explicit "sequence after #NNN" hints. State the planned order up front and track it withTaskCreate/TaskUpdate(one task per issue; mark in_progress on start, completed on land).
-
For each issue, in order: a. Dispatch ONE sub-agent (the
Agenttool, usuallygeneral-purpose) with a tight, self-contained mandate: the mandatory reads, the exact scope, the gates it must pass, "do NOT git commit or push — leave changes in the working tree", and a required structured report-back. Give it the rename maps / file lists / schema decisions up front so it does not improvise on settled questions. Run agents sequentially — wait for one to finish and land before starting the next — to avoid collisions on shared files (AGENTS.md, skills, the landing app, the template schema). b. Review the diff yourself. Read the changed files / diffstat. Trust but verify the agent's report — re-run the real gates manually: the relevantlint:*scripts, the affectedbun testfiles,cli:surface:buildfor CLI-surface changes, andrg '\p{Cyrillic}' --pcre2over changed files (text only;.webp/.pngare binary false positives). Web and public-doc checks run in their sibling repositories. c. Close gaps. Fix small issues inline; for a substantive miss, dispatch a focused follow-up agent (you cannot resume a finished agent —SendMessageis not available — so either edit directly or spawn a fresh scoped agent). d. Commit + push to main, then move on. One commit per issue with a Conventional-Commits message referencing the issue number. As part of that same commit,git mvthe issue intonotes/issues/done/and update its> **Status:**line todone — <YYYY-MM-DD>(the folder is the live signal, the line carries the date/why — keep them in sync). If the issue turns out to be obsolete or superseded rather than implemented,git mvit intonotes/issues/deprecated/and set the line toSUPERSEDED by #NNN/dropped (<reason>)instead. Never delete the note — moving preserves the record. Push toorigin(this repo commits to main; nevergitlab).
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.
- yesterday First seen · 60 lines · 242 tokens per session scan A 936c98d0a215
dev-loop is a skill published in the GitHub repository alecs5am/ralphy (128 stars, last pushed 6d ago), licensed Apache-2.0. It adds 242 tokens to every session and 1,785 once invoked, about $0.0012 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 skills, from other repositories
clinical-reports
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation…
clinical-decision-support
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading…
gget
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
molecular-cloning
Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.
bioimage-analysis
Microscopy image analysis for cell biology. Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. For pathology WSI use pathml; for flow cytometry use flow-cytometry-analysis.
cancer-genomics-analysis
Computational cancer genomics workflows. Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity/ploidy estimation, NMF metagene extraction, and DNA damage response network analysis. For cancer mutation databases use cosmic-database; for variant clinical…