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Research Wayfinder

Community
scdenney
research-wayfinder

Plans a research project as a decision map that persists across sessions, resolving one ticket per session until the design is defensible and pre-registerable. The map lives as Markdown in the repository, literature sweeps run as parallel research tickets, and the resolved map compiles into a pre-analysis plan. Use at project start, when a design has more open decisions than one conversation can hold, or when planning restarts each session. Not for executing analyses or writing the paper.

Overview

Publisherscdenney
Repositoryopen-science-skills
Skill nameresearch-wayfinder
Stars
69
Forks
3
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by scdenney on GitHub. Read the source before you install it.

Installation

Install the Research Wayfinder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/research-wayfinder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/scdenney/open-science-skills.git /tmp/open-science-skills
mkdir -p .claude/skills
cp -r /tmp/open-science-skills/plugin/skills/research-wayfinder .claude/skills/research-wayfinder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Wayfinder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Research Wayfinder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Research Wayfinder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Research Wayfinder

Heritage and scope

Adapted from Matt Pocock's wayfinder (MIT, mattpocock/skills; see RECOMMENDED.md in the library repository). His insight carries over whole: give up on the session as the unit of work. The map lives in the repository, any future session picks it up, and the context window stops being the source of truth. What changes here is the destination and the ticket taxonomy. Wayfinder charts a route to a shippable spec. Research-wayfinder charts a route to a defensible, pre-registerable research design, and its decisions are the ones experimental methodology actually forces: estimand before estimator, theory before if-then, power before fielding.

Start it early: a vague idea is the input the map is built to resolve. The map ends where pre-registration-writing begins — this skill does not run analyses or draft the paper.

The map

planning/map.md in the project repo (fits the research-repo scaffold; GitHub Issues works when the project is hosted on GitHub, but local markdown is the default and needs nothing). The map is an index, not a store:

markdown
# Map — <project name>
Destination — <what a finished design looks like — the study you could pre-register tomorrow>
Notes — <domain context, standing preferences, constraints — budget, panel, timeline>

## Decisions so far
- [x] tickets/001-estimand.md — <one-line gist of the resolution>

## Frontier (open, unblocked, unclaimed)
- [ ] tickets/004-power.md — blocked-by 002, 003

## Not yet specified
- <in-scope fog too unclear to ticket yet>

## Out of scope
- <consciously ruled out, so no session re-litigates it>

Decisions live in their tickets — one file each in planning/tickets/, claimed by marking it in-progress at the top before work begins. The map only gists and links them.

Ticket types

TypeDriverResearch meaningResolve with
researchagent, runs while you are awayliterature sweep, measure inventory, prior effect sizes, dataset scoutingliterature-review; parallelizable — one subagent per sweep
decisionyou + the researcher, livea design decision only the researcher can make — estimand, population, trade-offsthe frontier-question interview below; the default type
prototypeyou + the researcherraise fidelity with something concrete — power simulation, mock instrument, pilot analysis on simulated dataconjoint-design, survey-design, figures, simulation code
taskeitherunblocking logistics — IRB, data access, funding, panel quotesplain work; close with the artifact

A decision or prototype ticket resolves only through the live exchange — never stand in for the researcher's side of it.

The decisions a design map needs

Seed the first charting pass from the decision families experimental methodology forces, in rough dependency order — each becomes one or more tickets, with blocked-by lines added between them:

  1. Question and estimand — what quantity, for whom, under what counterfactual
  2. Theory and hypotheses — the "why" that yields falsifiable if-thens (hypothesis-building), including the smallest effect size of interest (SESOI)
  3. Design and identification — random assignment or the identification strategy, and what breaks it
  4. Sampling and power — population, recruitment, N for the SESOI (cross-national-design when multi-country)
  5. Measurement — instruments, scales, manipulation checks (survey-design, list-experiment, conjoint-design)
  6. Analysis plan — estimator matched to the data-generating process, covariates, multiple-comparison policy
  7. Ethics and logistics — IRB, consent, pre-registration venue, timeline

Chart the map (first session)

  1. Name the destination — interview the researcher grill-style (below) until "done" is concrete.
  2. Sweep the frontier breadth-first: list every decision now visible; sort into tickets (specifiable) versus Not-yet-specified fog.
  3. Write planning/map.md and the ticket files; add the blocked-by lines.
  4. Fire the research tickets in parallel, one subagent per sweep.
  5. Stop. Charting is one session's work; resolving starts next session.

Work the map (every later session)

  1. Read planning/map.md — the low-res view. Open only the ticket you claim.
  2. Claim one frontier ticket: open, unblocked, unclaimed.
  3. Resolve it with the matching skill or interview. Write the resolution into the ticket — the decision, the rationale, and what would change it.
  4. Close it: check it off under Decisions-so-far with a one-line gist. Graduate any fog it clarified into new tickets, and add the new blocked-by lines.
  5. Stop after one non-research ticket. Small resumable steps keep every decision in the files rather than in a context window that has to be rebuilt.

The grilling interview (live tickets)

Ask in rounds: the complete frontier of questions whose prerequisites are settled, numbered, each with your recommended answer so the researcher can accept or push back fast.

❓ Q1 — Population. Whose behavior does the claim cover — nationally representative, or the subgroup the theory names?
➡️ Recommended — the subgroup; the mechanism is specific to it, and representativeness costs power you need.

Facts get looked up, not asked — dispatch the lookup while the interview continues. Decisions go to the researcher, always. A round ends when its answers land in the ticket. research-grill is the standalone, single-sitting version of this interview (its heritage is Matt Pocock's grill-me, see RECOMMENDED.md); pair it with diverge when an answer needs genuinely distinct options on the table first.

Exit

When the frontier is empty and Not-yet-specified is empty or explicitly parked, the map is charted territory: hand the resolved decisions to pre-registration-writing to compile the pre-analysis plan. Keep planning/ in the repo — reviewers and future-you get the paper trail of every design decision and its rationale.

Notes

  • The map is versioned with the repo, so decisions get git history for free. A decision reversed later is a new ticket linking the old one, not an edit that erases the trail.
  • Multi-experiment papers: one map per study, plus a parent map linking them when the designs interlock.
  • The original wayfinder (coding destinations, issue-tracker mechanics) is recommended as-is for software work — see RECOMMENDED.md.

Frequently asked questions

What does the Research Wayfinder AI skill do?

Plans a research project as a decision map that persists across sessions, resolving one ticket per session until the design is defensible and pre-registerable. The map lives as Markdown in the repository, literature sweeps run as parallel research tickets, and the resolved map compiles into a pre-analysis plan. Use at project start, when a design has more open decisions than one conversation can hold, or when planning restarts each session. Not for executing analyses or writing the paper.

Why use Research Wayfinder on TypingMind?

Because you install it once and use it with any model. Research Wayfinder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Research Wayfinder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/research-wayfinder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Wayfinder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Research Wayfinder?

As many as you like. As long as a model supports skills, you can use Research Wayfinder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Research Wayfinder AI skill free?

It is published on GitHub by scdenney. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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