Resolve Beagle logo

Resolve Beagle

Organization
existential-birds
resolve-beagle

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.

Overview

Publisherexistential-birds
Repositorybeagle
Skill nameresolve-beagle
Stars
82
Forks
8
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Resolve Beagle 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.
  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/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-analysis/skills/resolve-beagle .claude/skills/resolve-beagle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Resolve Beagle 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 Resolve Beagle 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 Resolve Beagle 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.

Resolve: Close Spec Gaps

Take a spec produced by brainstorm-beagle and close its remaining gaps — both the explicit Open Questions and the latent ones the self-review missed — by researching, proposing answers, and rewriting the spec in place.

The terminal state is a spec with no known open questions and no placeholder requirements. Planning can start immediately after.

<hard_gate> This skill does not write code, scaffold projects, design architecture, or create implementation plans. It only edits the spec document. "Answering an open question" means proposing a WHAT/WHY answer with rationale — never a HOW. If a question turns out to require implementation design, defer it with a note and move on. </hard_gate>

Gates (pass before next step)

Objective pass conditions so steps are not skippable by assertion alone:

  1. Spec located — The target file path is known and Read succeeds (or the user supplied a valid path after you listed 3–5 recent docs/specs/ candidates).
  2. Gap list published — One message lists every explicit Open Question bullet and each latent gap you will treat as in-scope. Do not dispatch research until the user adjusts the list (add/remove/defer) or explicitly tells you to proceed with that list.
  3. Research artifact per gap — Before you present a proposal for gap G, you have a structured note for G: recommended answer, 1–2 rejected alternatives with reasons, and evidence (file:line, URL, or in-spec citation). No proposal without that artifact.
  4. One proposal queue — Do not open the next gap’s proposal until the current gap has a clear outcome (accepted, revised wording applied, rejected with what happens next, or deferred with reason).
  5. Rewrite reconciled — After editing, you read the full spec once and resolve cross-section contradictions; then run the self-review checklist from ../brainstorm-beagle/references/spec-reviewer.md with failures fixed in-session unless the user opts for a follow-up pass.
  6. Commit — No git commit unless the user answered yes to the commit prompt in § Committing.

Workflow

  1. Locate the spec — explicit path from the user, or the most recent file in docs/specs/
  2. Extract gaps — parse Open Questions and audit for latent issues (placeholders, vague requirements, missing rationale, contradictions)
  3. Show the gap list — present everything you plan to close in one summary so the user can add or remove items before research starts
  4. Dispatch research — one task per gap, in parallel via subagents when available; otherwise sequentially inline
  5. Propose answers — one proposal at a time, with recommendation + alternatives + evidence
  6. Rewrite the spec in place — migrate resolved items to the right sections; nothing silently dropped
  7. Self-review — same checklist brainstorm-beagle uses (see ../brainstorm-beagle/references/spec-reviewer.md)
  8. Ask about committing — prompt the user whether to commit the edit; don't commit unprompted
Locate spec → Extract gaps → Show list ──→ User adds/removes
                                          → Dispatch research (parallel if possible)
                                          → Propose answers (one at a time)
                                                → User decision → next
                                          → Rewrite spec in place
                                          → Self-review (fix inline)
                                          → Ask about committing

Locating the spec

If the user gave a path, use it.

Otherwise, list the 3–5 most recently modified files in docs/specs/ and ask: "Work on <most recent>, or another one?" Don't scan the whole directory tree — specs are top-level per brainstorm-beagle's convention.

If no spec directory exists, ask the user for the path.

Extracting gaps

Two categories count as gaps:

Explicit gaps — every bullet under the spec's Open Questions heading is one research task.

Latent gaps — issues that slipped past the brainstorm's self-review. Scan the spec for:

ProblemWhat it looks like
PlaceholderTBD, TODO, "to be determined", ellipsis used as content
Vague requirement"fast", "simple", "good", "user-friendly", "intuitive" — nothing to verify against
Missing rationaleConstraint or Out-of-Scope item with no "why"
ContradictionRequirement conflicts with another requirement, with a constraint, or with Out of Scope
Untestable successNo observable way to verify the requirement was met
Implementation leakageA requirement prescribes HOW instead of describing WHAT
Unconsumed surfaceA must-have introduces new externally-facing surface (API surface, command, endpoint, exported contract) that nothing else in the spec consumes
Unresolved compositionAn existing mechanism sits upstream/downstream in the same data pipeline and transforms (truncate, filter, buffer, reorder, dedupe) the data the feature depends on, but its composition with the feature is left unexamined

The reason to treat latent gaps as first-class: a spec that says "fast" or "good UX" hasn't been answered just because nothing was explicitly flagged. Planning will trip over those same words. Close them here.

Before dispatching research, show the combined list to the user in one message — "here's what I'm planning to close" — and let them add, remove, or defer items. Don't ask permission one-by-one; that's the proposal step.

Dispatching research

Each gap gets exactly one research task. Classify each task first — the type determines which tools the research needs:

Task typeLooks likeTools
Codebase pattern"How does the existing --start-at pattern work?" "Where is SKILL_MAP defined?"search the codebase (grep/glob/read)
External / API"What does the agent's SDK expose for sub-agent spawning?" "Does Codex have hooks?"web search and page fetch (if web access is available)
Design tradeoff"What should the merged report format be?" "How should deduplication work?"Reasoning + analogous reference points already in the spec
Scope / policy"Should config/docs files route to a stack or fallback?"Reasoning tied to the spec's own Core Value and Constraints

With subagents (preferred)

If the agent supports subagents, dispatch each research task as an independent subagent — all in the same turn, so they run in parallel. Each subagent gets its own context window, which matters: gaps often come with large supporting context (the spec, the codebase) that you don't want crammed into a single conversation.

See references/subagent-prompts.md for the prompt templates (one per task type).

The research return must be structured: recommended answer, 1–2 alternatives with why rejected, and concrete evidence (file:line citations, URLs, or references to existing spec sections). Cap each return at ~300 words — you want decisions, not transcripts.

Without subagents

If no subagent tool is available, do the research yourself, one question at a time. Work in cheapest-first order: codebase questions, then external, then tradeoffs, then scope/policy. Produce the same structured proposal for each. This is slower but never leaves gaps unresolved.

Proposing answers

Present proposals one at a time. For each:

  • The gap — restated in one line
  • Recommended answer — your best call, with WHY
  • Alternatives — 1–2 credible options and why they were rejected
  • Evidence — file:line citations, URLs, or references to decisions already in the spec

The user can accept, revise, or reject. Follow the thread if they want to discuss; move on once decided.

Order matters. Resolve in this rough sequence:

  1. Codebase-reality gaps first (they may inform design tradeoffs)
  2. Policy/scope gaps second (they shape everything downstream)
  3. Format/presentation gaps last (they depend on the above)

When a gap can't be resolved without input only the user has (budgets, stakeholder preferences, unstated constraints), don't guess — ask directly. It's cheaper than proposing the wrong answer and unwinding it.

Rewriting the spec

As decisions land, migrate them to where they belong:

Gap typeDestination after resolution
Architectural or policy decisionNew entry under Key Decisions (with alternatives considered)
Concrete behaviorNew entry under Requirements (assigned must/should/out-of-scope)
Hard limitNew entry under Constraints (with rationale)
External reference discovered during researchNew entry under Reference Points
Vague requirement replacedRewrite the existing requirement inline; do not duplicate
Intentionally deferredStays in Open Questions with a deferred: <reason> suffix

Two rules that matter:

  • Never silently drop an Open Question. Either resolve it, migrate it with rationale, or explicitly defer it with a reason. Dropping a question without a trail is how specs regress between sessions.
  • Rewrite the whole spec, not just the touched sections. Changes ripple: a new Key Decision may contradict an old Requirement, a resolved Open Question may invalidate a Constraint. Read the spec end-to-end after edits and reconcile.

Self-review

Run the checks from ../brainstorm-beagle/references/spec-reviewer.md:

  • No placeholders
  • No contradictions
  • No implementation leakage
  • All requirements testable
  • Constraints and Out-of-Scope items have rationale
  • No new externally-facing surface (API surface, command, endpoint, exported contract) without a named consumer — name it or move it to Future Considerations
  • Any existing upstream/downstream pipeline mechanism that transforms the feature's data is recorded as a Key Decision tagged needs-spike-before-planning, not left unexamined

Fix anything that surfaces inline before handing back to the user. If new gaps appear during the rewrite (they sometimes do), add them to a "new gaps surfaced" list and ask the user whether to resolve them now or leave for a later pass.

Committing

After the rewrite, summarize and ask:

"Spec updated. Resolved N explicit questions and M latent gaps. Want me to commit this as docs: resolve open questions in <topic> spec?"

If yes, commit. If no, leave the working tree for the user. Do not commit unprompted — the user may want to review the diff first or bundle it with other changes.

Key Principles

  • Close the loop. The goal is a spec with nothing unanswered that blocks planning. Half-resolved is worse than clearly deferred.
  • One research task per gap. Don't blur tasks together — it makes the proposal step harder to review and weakens the evidence trail.
  • Evidence over opinion. Every proposal cites something — a file, a URL, or an existing spec decision. "I think" is not enough.
  • Still WHAT, not HOW. Answers land as decisions in the spec, not as implementation designs. If an answer requires implementation thinking, defer it.
  • Defer honestly. A question too expensive to answer now stays an Open Question with a one-line reason. Better to admit a known gap than paper over it.
  • Parallelize when you can. Subagents run in isolation — fan out aggressively. Sequential research is the fallback, not the default.
  • Don't interrupt unnecessarily. Show the gap list once, then only stop the user for decisions that need human judgment.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Resolve Beagle AI skill do?

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwi...

Why use Resolve Beagle on TypingMind?

Because you install it once and use it with any model. Resolve Beagle 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 Resolve Beagle in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-analysis/skills/resolve-beagle. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Resolve Beagle?

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 Resolve Beagle?

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

Is the Resolve Beagle AI skill free?

Yes. It is published on GitHub by existential-birds under the Apache-2.0 license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇