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Clarify

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ericrisco
clarify

Use when a spec exists and must be de-risked before planning — hunt its ambiguities, unstated assumptions and edge cases, ask the few build-changing questions, bake the answers back into the spec. The rsc SDD gate between `specify` (writes the spec) and `plan` (designs the build). NOT the cross-artifact consistency check (that is `analyze`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill nameclarify
Stars
239
Forks
24
Bundled files
1
LicenseMIT
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 ericrisco on GitHub. Read the source before you install it.

Installation

Install the Clarify 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/ericrisco/rsc-harness/tree/main/skills/clarify
  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/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/clarify .claude/skills/clarify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clarify 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 Clarify 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 Clarify 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.

Clarify — the de-risking gate before planning

A spec written in one sitting always lies a little. It states what the author thought of, and stays silent on everything they didn't — the edge cases, the unstated defaults, the words that mean two things. Those silences don't disappear; they get discovered later, mid-implementation, where they cost ten times as much to fix. Clarify is the gate that drags those silences into the open while they are still cheap.

This is the fourth phase of the rsc SDD chain (constitution → specify → clarify → plan → tasks → analyze → implement → verify → review → ship); the method itself lives in ../sdd/SKILL.md. specify turned a fuzzy intent into a spec; clarify interrogates that spec, asks the user the questions that actually change the build, and writes the answers back so the spec becomes safe to plan from. It produces no new artifact — it sharpens the existing one in place. The line is specify creates, clarify de-risks, plan designs: if you find yourself proposing how to build it, you have left clarify.

Model tier: balanced — this phase ranks and asks the few high-leverage questions, it does not design architecture. Resolve and apply it per ../sdd/references/model-routing.md; routing is off unless models.enabled: true in 02-DOCS/wiki/sdd/config.yaml.

Register. Read technical_level from 02-DOCS/wiki/harness/user-profile.md (the 02-DOCS/wiki/ convention is owned by ../harness/SKILL.md). Clarify is question-heavy, so the voice matters here more than almost anywhere. Ask only the questions whose answer changes the build; propose safe defaults for everything else and list them as "assumed unless you object". Each question carries one line of why and the trade-off behind each option, in the orient voice. For a non-technical reader, frame each option with an everyday analogy. With no profile: use analogies and ask once "technical or with analogies?".

Read first — the inputs

Clarify never works blind. Before asking a single question, load three things:

  1. The spec. Read the target spec under 02-DOCS/wiki/sdd/specs/<slug>.md end to end. If the path wasn't given, find the most recently touched spec or ask which one. Its Points to clarify is a typed handoff, not a question list — read the types before you plan a single question (below).
  2. The constitution. Read 02-DOCS/wiki/sdd/constitution.md if it exists. Its principles (stack canon, quality bars, conventions) resolve a surprising number of "ambiguities" without bothering the user — if the constitution already fixes the auth method or the data region, that's answered, not open.
  3. The harness profile. 02-DOCS/wiki/harness/user-profile.md, for the register above.

Citing what you read ("checked the constitution — auth is already fixed to OAuth, so that's not an open question") shows your work and prevents re-litigating settled decisions.

The typed handoff — what specify already decided

Points to clarify holds four different objects, and each one gets a different action from you. Treating all four as questions is how clarify re-asks what was already decided and disturbs what was deliberately deferred:

Type in the specWhat it meansYour action
pregunta abiertaFormulable, unansweredAsk it — this is your queue
suposición tomadaspecify decided it; the basis is writtenValidate, don't re-ask: state the assumption and its basis back, and ask only whether it still holds
decisión diferidaSharp, out of this cycle on purposeLeave it. Do not reopen scope the author closed
área no formulableKnown to be coming, not yet phrasableNote it. If your pass sharpens it into a real question, it graduates — say that it did

Declare what you did with each entry. Close the pass with one line per point: asked, validated (held / broke), left deferred, or graduated. An entry you silently dropped is the gap that comes back at implementation time.

An untyped entry (an older spec, or a hurried one) is read as pregunta abierta — the costliest reading, so nothing gets skipped by accident. Type it as you go, so the spec improves on the way through.

Sharpness, not difficulty, is what separates a question from an unformulable area: can you state it precisely now? Not can you answer it?

The ambiguity taxonomy — where specs hide their gaps

Scan the spec against these categories. Most real gaps fall into one of them; walking the list is how you find the ones the author didn't think to write down.

CategoryWhat to huntTell-tale phrasing in the spec
Underspecified behaviorA described feature with a missing branch — what happens in the other case"the user logs in" (and if it fails? locked out? wrong password vs no account?)
Unstated assumptionsDefaults the author assumed everyone sharesno mention of auth, tenancy, currency, timezone, locale
Edge & boundary casesEmpty, zero, max, duplicate, concurrent, first-run, offlinelists with no empty-state, counts with no upper bound
Ambiguous termsA word doing two jobs"user" (end-user or admin?), "delete" (soft or hard?), "fast" (how fast?)
Missing acceptance criteriaA goal with no observable done-condition"should be performant", "easy to use", "handle errors gracefully"
Scope edgesWhat's explicitly OUT vs left danglingfeatures hinted at but never bounded — "for now", "eventually"
Data & stateLifecycle, ownership, retention, migration of existing datanew entity with no story for what happens to old records
Failure & recoveryWhat happens when a dependency is down, a write half-completes, input is hostilehappy-path-only flows
Non-functionalPerformance, scale, security, accessibility, i18n targetsvague "non-functional requirements" or none at all
Actors & permissionsWho can do each thinga verb with no subject — "can be edited" (by whom?)

You are not filling every cell for every spec. You are scanning all ten so the gaps that do exist surface instead of hiding.

The pass — five steps

Run in order. The discipline is: find many candidate gaps, keep only the ones that change the build, ask those well, write the answers back.

  1. Inventory. Walk the spec against the taxonomy above. Produce a raw list of every candidate ambiguity, edge case, and unstated assumption. Over-collect here; you'll prune next. Note for each which category it is and where in the spec it lives.

  2. Resolve what you already can. For each candidate, check the constitution and the spec's own later sections before asking the user. Many "gaps" are answered elsewhere. Mark each candidate resolved-internally (cite the source), inferable (a safe default you'll propose, not silently assume), or must-ask (only the user can decide).

  3. Rank by leverage. Sort the must-ask list by impact: how much does the build change depending on the answer? A question whose two answers lead to two different architectures ranks above a cosmetic one. Cut low-leverage questions — clarify is not an interrogation, it's the few questions that matter.

  4. Ask — one focused batch. How you ask determines whether you get a usable answer:

    • Make it a decision, not an essay prompt. "Should deletes be soft (recoverable, hidden) or hard (gone immediately)? I'd recommend soft because the spec mentions an audit trail — confirm?" beats "How should deletion work?".
    • Carry your own recommendation when there's a defensible default, matched to the constitution. The user confirms or overrides — far less effort than authoring from scratch.
    • Quote the spec. Anchor each question to the exact line or section it came from, so the user sees why it's open.
    • One batch, ranked, then stop. Don't drip questions one at a time over many turns; don't dump thirty at once. Then wait: never ask and answer in the same breath, and never assume the user's intent on a must-ask item.
  5. Bake the answers back into the spec. This is the deliverable — an un-baked answer is a lost answer. For each resolved item, edit the spec in place:

    • Tighten the relevant section with the decided behavior.
    • Add or sharpen acceptance criteria so the decision is now observable.
    • Append a ## Clarifications log to the spec: dated entries of Q → decision → why, so the reasoning survives, not just the result.
    • Move anything explicitly dropped into an ## Out of scope section so it's bounded, not dangling.

    Then re-read the spec once more: did resolving one gap open a new one? If so, one more short loop. Otherwise, the gate is passed.

Worked micro-example

Spec line: "Users can upload a profile photo."

Clarify's inventory against the taxonomy:

text
- Ambiguous term  : "photo" — which formats? (PNG/JPG/HEIC/SVG?)
- Boundary        : max file size? max dimensions? what if it's 50 MB?
- Edge case       : no photo uploaded — is there a default/placeholder?
- Failure         : upload fails mid-transfer — retry, or lose it?
- Data lifecycle  : replacing a photo — is the old file deleted or orphaned?
- Actors          : can an admin change another user's photo?
- Non-functional  : is the image resized/compressed server-side? stored where?
- Security        : is the file type validated, or can someone upload an .svg with script?

Resolved-internally (cite): constitution fixes storage to the project's object store → "stored where" is answered. Must-ask, ranked: formats + max size (changes validation and UX), security validation (changes the upload path), old-file deletion (changes data model). Cosmetic placeholder choice → propose a default, don't burn a question on it.

After baking back, the spec line becomes a bounded, testable behavior with acceptance criteria ("rejects files >5 MB with a clear message", "accepts PNG/JPG/HEIC only", "replacing a photo deletes the prior file") and a ## Clarifications entry recording why.

Anti-patterns

Anti-patternWhy it breaks the gate / do instead
Skipping clarify because the spec "reads clear enough"Clear to the author ≠ unambiguous. Run the taxonomy; the gaps you can't see are exactly the expensive ones.
Assuming the sensible default and moving onAn assumption is an unrecorded decision. Either it's resolvable from the constitution (cite it) or it's a must-ask. Silent defaults resurface as bugs — and "the user is busy" is not an exception; a wrong guess costs more than a one-tap question.
Sketching how you'd build it while you're in thereThat's plan. Clarify decides what, not how. Proposing architecture means you left the gate.
Asking everything you can think of, to be safeThirty questions is noise that buries the three that matter. Rank by leverage, ask the few, default the rest — and batch them once instead of dripping one per turn.
Answering the questions in your head and leaving the spec as-isThe deliverable is the edited spec plus the Clarifications log, not a clean conscience.
Leaving edge cases "to the implementer"Edge cases are spec problems. Resolving them now is the whole point of the gate.

Exit gate

The gate is passed when the spec, constitution and profile were all read (settled questions cited, not re-asked); every typed point in the handoff has its declared outcome (asked / validated / left deferred / graduated), with nothing silently dropped; all ten taxonomy categories were considered; only the build-changing gaps were put to the user, as one ranked batch of decisions with recommendations; every answer is baked into the spec body with observable acceptance criteria, logged under ## Clarifications and bounded under ## Out of scope; and the final re-read opened no new gap.

Result envelope

End with the parseable block every SDD phase shares, so the dispatcher can chain without interpreting prose (contract: ../sdd/SKILL.md):

json
{
  "status": "complete|blocked|failed",
  "executive_summary": "Open points resolved; the spec is de-risked and ready to plan against.",
  "artifact": "02-DOCS/wiki/sdd/specs/<slug>.md",
  "next_recommended": "plan",
  "risk": "low|medium|high",
  "skill_resolution": {
    "used": ["clarify"],
    "missing": [],
    "fallback": [],
    "compact_rules": ["Ask only what changes the spec.", "An unanswered question is recorded, never invented."]
  },
  "evidence": ["answers folded back into the spec", "remaining open points listed with their owner"]
}

Next in the chain

Hand off to plan — turn the now-sharp spec into a technical implementation plan (architecture, interfaces, data flow, testing strategy, risks), deferring stack specifics to the relevant stack skill. The chain continues: clarify → plan → tasks → analyze → implement → verify → review → ship. debug is callable any time if what you are "clarifying" turns out to be a runtime fault, not a spec gap.

Orientación (siempre)

Habla con la voz de orient: frases cortas, una idea por frase, y cada respuesta se entiende sola. Registro técnico o con analogías según technical_level en 02-DOCS/wiki/harness/user-profile.md. Cierra cada turno con el bloque-brújula (📍 dónde estás · ➡️ siguiente, terminando en pregunta; ✅ y 🧭 cuando hay algo hecho o decidido). Nunca termines en seco. Protocolo completo: skill orient → skills/orient/references/orientation-contract.md. (Defiere a suggest el "¿instalo la skill que falta?".)

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 Clarify AI skill do?

Use when a spec exists and must be de-risked before planning — hunt its ambiguities, unstated assumptions and edge cases, ask the few build-changing questions, bake the answers back into the spec. The rsc SDD gate between `specify` (writes the spec) and `plan` (designs the build). NOT the cross-artifact consistency check (that is `analyze`).

Why use Clarify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/clarify. 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 Clarify?

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 Clarify?

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

Is the Clarify AI skill free?

Yes. It is published on GitHub by ericrisco under the MIT 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.

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