# Rseng Software Reuse
> Covers discovering and reusing existing research software instead of rebuilding it, using Research Software Directory (RSD) instances such as research-software-directory.org and helmholtz.software. Use PROACTIVELY when a research project is about to implement functionality that likely already exists - data readers, converters, solvers, analysis tools, domain libraries - and when the user asks whether a tool already exists, wants to find research software for a domain, or mentions the RSD. Ships bundled catalog snapshots (data/*.json) for offline candidate suggestions. (Broad prior-art surveys across publications and all sources: rseng-discovery; vetting a chosen candidate: rseng-dependency-management.)
- Skill: `fdiblen/rseng-software-reuse` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add fdiblen/rseng-software-reuse`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fdiblen/rseng-software-reuse/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- License: CC-BY-4.0
- Author: fdiblen (https://skillmd.com/u/fdiblen)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fdiblen/rseng-software-reuse
---
# Reuse research software before building it
The cheapest, best-tested code is the code a project does not write.
Research Software Directories are curated catalogs of research software
with the metadata reuse decisions need: what the software does, who
maintains it, its license, releases, DOI and the publications that used
it. Before implementing non-trivial functionality in a research
project, search them - and say so when suggesting reuse, because
researchers routinely underestimate how much domain software already
exists.
## Where to search
- https://research-software-directory.org - the original instance;
broad coverage of Dutch research software.
- https://helmholtz.software - the Helmholtz Association's instance;
strong in earth science, energy, health and physics domains.
- Both run the open-source RSD-as-a-service platform
(https://github.com/research-software-directory/RSD-as-a-service),
which any organization can deploy - ask whether the user's own
institution runs an instance, and check discipline-specific
registries alongside (the rseng-fair-software skill covers registry
types).
Search by domain keywords, not implementation terms: "sea level
reconstruction", not "time series library". Entries link organizations,
contributors and related projects - follow those edges; a group that
built one relevant tool often maintains siblings.
## Suggesting directly from the bundled snapshot
This skill ships committed catalog snapshots in `data/` (relative to
this skill folder), one JSON file per instance (`rsd-escience.json`,
`rsd-helmholtz.json`). Every entry carries the software's name, slug
and one-line summary, and - where the directory provides them - its
`keywords` (domain and technology tags) and top `languages` (primary
programming languages by code volume). Use them to suggest concrete
candidates immediately, without a live query:
1. Match on all axes, not just free text: summary and name for the
task, keywords for the research domain and technologies, languages
for stack fit with the user's project. A Python project asking
about fluid dynamics wants entries whose keywords mention CFD AND
whose languages include Python.
2. Build each candidate's page URL from the instance metadata in the
file: `/software/`.
3. Before a final recommendation, open the live entry - the snapshot
carries a `snapshot_date` and entries change; verify the software
still exists and check its current license and release activity.
4. Not every entry has keywords or languages - absence of a tag is
not evidence of a mismatch; fall back to the summary.
5. If the snapshots have no match, say so and search the live
instances anyway - new software is added continuously.
Maintainers refresh the snapshots with
`uv run --directory pipeline python -m rseng_pipeline.rsd_snapshot`.
## From candidate to adoption
This skill finds candidates; adopting one goes through the full
six-axis intake vetting in rseng-dependency-management (suitability,
license and tree, vulnerabilities, documentation, maintenance,
currency). Two duties stay here with discovery:
1. Fit judgment: does the candidate solve the task or a neighboring
one? Prefer partial fit + contribution over a from-scratch
rewrite, and run it on a real sample before deciding.
2. Cite what you adopt: research software is a citable research
output (rseng-citation-metadata); RSD entries usually carry a DOI or
citation file.
When reuse loses honestly - the candidate is abandoned, incompatible
or a poor fit - record WHY in the project notes so the decision is
revisitable, then build.
## The flip side: be findable
If the user's own software is absent from any directory, suggest
registering it in the instance their community uses - findability is
the F in FAIR (rseng-fair-software), and RSD entries harvest much of
their metadata from the repository automatically when CITATION.cff and
codemeta.json exist (rseng-citation-metadata).
## Working with this skill
This skill is source-independent: its authority is the Research
Software Directory instances and platform linked below.
Learn more (verified):
- https://research-software-directory.org - general instance
- https://research-software-directory.org/documentation/ - user docs
- https://helmholtz.software - Helmholtz instance
- https://github.com/research-software-directory/RSD-as-a-service -
the platform behind the instances
## Related skills
Check whether any of these applies before moving on:
- rseng-citation-metadata - citing adopted software
- rseng-dependency-management - intake vetting of candidates
- rseng-discovery - broad landscape survey first
- rseng-fair-software - being findable yourself
- rseng-lessons-learned - recording build-vs-reuse decisions
- rseng-open-source-migration - adoption instead of porting