skill-domain-discovery

name	skill-domain-discovery
description	Analyze library documentation and source code, then interview maintainers to discover capability domains and task-focused skills for AI coding agents. Activate when creating skills for a new library, organizing existing documentation into skill categories, or when a maintainer wants help deciding how to structure their library's agent-facing knowledge. Produces a domain_map.yaml and skill_spec.md that feed directly into the skill-tree-generator skill.
metadata	{"version":"3.0","category":"meta-tooling","output_artifacts":["skills/_artifacts/domain_map.yaml","skills/_artifacts/skill_spec.md"],"skills":["tree-generator"]}

Domain Discovery & Maintainer Interview

You are extracting domain knowledge for a library to produce a structured domain map. Your job is not to summarize documentation — it is to build a deep understanding of the library first, then use that understanding to surface the implicit knowledge that maintainers carry but docs miss.

The output is a set of task-focused skills — each one matching a specific developer moment ("implement a proxy", "set up auth", "audit before launch"). Domains are an intermediate conceptual grouping you use during analysis; the final skills emerge from the intersection of domains and developer tasks.

There are five phases. Always run them in order — unless the lightweight path applies (see below).

1. Quick scan — orient yourself (autonomous)
2. High-level interview — extract the maintainer's task map
3. Deep read — fill in failure modes and detail (autonomous)
4. Detail interview — gap-targeted questions, AI-agent failures
5. Finalize artifacts

Lightweight path (small libraries)

After Phase 1, decide whether the library warrants the full five-phase flow or the compressed flow below. This is a judgment call — lean toward full discovery unless the library is obviously small (single-purpose utility, 2–3 distinct developer tasks max). Use a compressed flow when the skill surface is small enough that two interview rounds would be redundant:

1. Phase 1 — Quick scan (same as full flow)
2. Phase 2+4 combined — Single interview round. Combine the high-level task map questions (Phase 2) with gap-targeted and AI-agent-specific questions (Phase 4) into one interview session of 4–8 questions total. Skip the draft-review step since the skill set is small enough to confirm in one pass.
3. Phase 3 — Deep read (same as full flow, but scope is smaller)
4. Phase 5 — Finalize artifacts (same as full flow)

The lightweight path produces identical output artifacts (domain_map.yaml and skill_spec.md). It just avoids two separate interview rounds when the library is small enough that one round covers everything.

Hard rules — interview phases are mandatory and interactive

These rules override any other reasoning. No exceptions.

1. Phases 2 and 4 are interactive interviews conducted with the maintainer. You must ask the questions specified in each sub-section and wait for the maintainer's response before continuing. Documentation, source code, and other automated analysis are NOT substitutes for the maintainer's answers.
2. Every question in Phases 2 and 4 must be asked as an open-ended question and sent as a message to the maintainer. You must then STOP and WAIT for their reply. Do not answer your own questions. Do not infer answers from documentation. Do not skip questions because you believe you already know the answer.
3. Never ask factual questions you can answer by searching the codebase. Before asking any question, determine whether the answer is a deterministic fact (how many X exist, what versions are supported, which files implement Y) or a judgment call (which ones matter, what should we prioritize, what do developers struggle with). Factual questions must be answered by searching the code — grep, glob, read files. Only ask the maintainer for priorities, opinions, trade-offs, and implicit knowledge that cannot be found in code or docs. Asking the maintainer a question whose answer is sitting in the codebase wastes their time and erodes trust in the process.
4. Do not convert open-ended questions into multiple-choice, yes/no, or confirmation prompts. The question templates in each sub-section are open-ended by design. Present them as open-ended questions. The maintainer's unprompted answers surface knowledge that pre-structured options suppress.
5. Minimum question counts are enforced. Each sub-section specifies a question count range (e.g. "2–4 questions"). You must ask at least the minimum number. Asking zero questions in any sub-section is a protocol violation.
6. STOP gates are mandatory. At the boundaries marked ── STOP ── below, you must halt execution and wait for the maintainer's response or acknowledgment before proceeding. Do not continue past a STOP gate in the same message.
7. If the maintainer asks to skip an interview phase, explain the value of the phase and what will be lost. Proceed with skipping only if they confirm a second time.
8. Rich documentation makes interviews MORE valuable, not less. When docs are comprehensive, the interview surfaces what docs miss: implicit knowledge, AI-specific failure modes, undocumented tradeoffs, and the maintainer's prioritization of what matters most. Never rationalize skipping interviews because documentation is thorough.

---

Phase 1 — Quick scan (autonomous, ~10 minutes)

Orient yourself in the library. You are building a structural map, not reading exhaustively yet.

1a — Read orientation material

1. README — vocabulary, mental model, what the library does
2. Getting started / quickstart — the happy path
3. Package structure — if monorepo, identify which packages are client-facing vs internal. Focus on the 2–3 packages most relevant to skill consumers (usually client SDKs and primary framework adapters)
4. AGENTS.md or .cursorrules — if the library already has agent guidance, read it. This is high-signal for what the maintainer considers important
5. All in-repo documentation — list every .md file in the docs/ directory (and any other documentation directories like guides/, reference/, wiki/). Read every file. This is NOT the exhaustive external doc reading from Phase 3 — this is reading what the maintainer committed to the repository, which is fast and high-signal. In-repo docs often contain migration guides, backward compatibility notes, architecture decisions, and other context that prevents you from asking factual questions the docs already answer. Do not sample a subset — read them all before the first interview.

1b — Read peer dependency constraints

Check package.json for peerDependencies and peerDependenciesMeta. For each major peer dependency (React, Vue, Svelte, Next.js, etc.):

1. Note the version range required
2. Read the peer's docs for integration constraints that affect this library: SSR/hydration rules, component lifecycle boundaries, browser-only APIs, singleton patterns, connection limits
3. Log framework-specific failure modes — these are the highest-impact failure modes and cannot be discovered from the library's own source

Examples of peer-dependency-driven failure modes:

• SSR: calling browser-only APIs during server render
• React: breaking hook rules in library wrapper components
• Connection limits: opening multiple WebSocket connections per tab
• Singleton patterns: creating multiple client instances in dev mode

1c — Note initial impressions

Log (but do not group yet):

• What the library does in one sentence
• The core abstractions a developer interacts with
• Which frameworks it supports
• Any existing skill files, agent configs, or intents
• Whether the library is a monorepo and which packages matter
• Peer dependency constraints — read peerDependencies and peerDependenciesMeta from each client-facing package.json to understand version ranges and optional integrations early

Present your initial impressions to the maintainer as a brief summary (3–5 bullets). This orients them on what you found and primes them for the interview.

── STOP ── Do not proceed to Phase 2 until the maintainer has acknowledged your summary or responded.

---

Phase 2 — High-level interview (interactive — requires maintainer)

The maintainer's mental model of developer tasks IS the skill map. Your job in this phase is to extract it — not to propose your own structure.

You must ask the questions below to the maintainer and wait for their responses. Do not infer answers from documentation or source code.

Rules for Phase 2

1. One topic per message for open-ended questions. You may batch 2–3 yes/no or short-confirmation questions together.
2. Ask each question as written (you may adapt phrasing to context, but keep questions open-ended — never convert to multiple-choice).
3. Wait for the maintainer's response after each question before asking the next.
4. Take notes silently. Do not summarize back unless asked.
5. If the maintainer gives a short answer, probe deeper before moving on.

2a — Developer tasks (2–4 questions)

Start with the maintainer's view of what developers do:

"Walk me through what a developer actually does with your library — not the elevator pitch, but the tasks they come to you for help with, from first install through production."

Follow up to enumerate distinct tasks:

"If you listed every distinct thing a developer asks an agent to help with using your library, what would that list look like? I'm thinking things like 'set up the client', 'implement auth', 'debug sync issues' — each one a separate moment where they'd want focused guidance."

For monorepo libraries, also ask about cross-package tasks:

"Are there tasks that touch multiple packages in your monorepo? For example, a getting-started flow that requires imports from both the client and server packages? I want to make sure skills that span package boundaries are captured correctly."

2b — Developer journeys (1–2 questions)

Surface lifecycle/journey skills that cross-cut task areas:

"Are there developer journeys that cut across multiple features? For example: a getting-started guide, a go-to-production checklist, a migrate-from-v4 walkthrough. Which of these exist in your docs or would be valuable as standalone skills?"

2c — Composition and ecosystem (1–3 questions)

"Which other libraries does yours compose with most often? Are there integration patterns important enough to warrant their own skill — for example, using your library with [framework/ORM/router]?"

"Are there tasks that developers might expect your library to handle, but that are actually handled by a companion library? Which tasks should we explicitly exclude from your library's skills?"

2d — Exclude experimental features (1 question)

"Are there any features that are experimental, unstable, or not yet ready to document for agents? We'll exclude these from the skill set."

2e — Confirm initial skill map

Synthesize what you heard into a proposed skill list and present it:

"Based on what you've told me, here's my proposed skill list: [enumerate skills with one-line descriptions]. Does this match how you think about your library? What would you add, remove, or rename?"

── STOP ── Do not proceed to Phase 3 until the maintainer has reviewed and confirmed (or corrected) the skill list.

---

Phase 3 — Deep read (autonomous)

You now have the maintainer's task map. Read docs and source to fill each skill area with concrete content — failure modes, code patterns, gotchas.

Reading order

Read in this order. Each step builds context for the next.

Before starting, list every file in the docs directory (and subdirectories). Use this list as a checklist — every narrative file must be read. Do not sample a subset and extrapolate.

1. Narrative guides — read as many as needed to build confidence in your understanding. Prioritize getting-started, migration, and guides covering the skill areas from Phase 2. Skip exhaustive reading of large online-only doc sets.

2. Migration guides — highest-yield source for failure modes; every breaking change is exactly what agents trained on older versions produce

3. API reference — scan for exports, type signatures, option shapes

4. Changelog for major versions — API renames, removed exports, behavioral changes

5. GitHub issues and discussions — this is one of the highest-yield sources for failure modes and skill content. Docs describe intended behavior; issues reveal actual behavior and real developer confusion.

   How to search. Use gh search issues and gh search prs (or the GitHub web search UI) against the library's repo. Run multiple passes:

   • High-engagement issues: sort by reactions or comments to find the problems that affect the most developers. These are skill-worthy even if already fixed — agents trained on older data still hit them.
   • Label-based scans: look for labels like bug, question, documentation, breaking-change, good first issue, FAQ, help wanted. Each label category yields different signal:
     • bug + closed → failure modes with known fixes (wrong/correct pairs)
     • question → developer confusion that skills should preempt
     • breaking-change → migration-boundary mistakes
   • Keyword searches: search for the skill's primary APIs, hooks, and config options by name. E.g. useQuery stale or hydration SSR.
   • Recent vs. historical: scan the last 6–12 months of open issues for current pain points. Then scan older closed issues for patterns that are now fixed but still appear in agent training data.

   GitHub Discussions are equally important when the repo uses them. Discussions surface "how do I..." patterns and architectural questions that issues don't capture. Search the Discussions tab (or use gh api to query discussions) for:

What to log

Produce a flat concept inventory. One item per line. No grouping yet.

Log every:

• Named concept, abstraction, or lifecycle stage
• Public export: function, hook, class, type, constant
• Configuration key, its type, and its default value
• Constraint or invariant (especially any enforced by throw or assertion)
• Doc callout: any "note", "warning", "caution", "important", "avoid", "do not"
• Dual API: any place the library has two ways to do the same thing (old/new, verbose/shorthand, lower-level/higher-level)
• Environment branch: any place behavior depends on SSR/CSR, dev/prod, framework, bundler, or config flag
• Type gap: any type documented as accepting X but source shows X | Y or rejects a subtype of X
• Source assertion: any if (!x) throw, invariant(), or assert() with the error message text
• Issue/discussion pattern: any recurring confusion, workaround, or misunderstanding surfaced from GitHub issues or discussions — note the issue/discussion URL, the core misunderstanding, and whether it's resolved or still active

What to extract from migration guides specifically

For each breaking change between major versions:

Old pattern: [code that agents trained on older versions will produce]
New pattern: [current correct code]
What changed: [one sentence — the specific mechanism]
Version boundary: [e.g. "v4 → v5"]

These become high-priority failure modes.

3a — Group concepts into domains

Move concept inventory items into groups. Two items belong together when:

• A developer reasons about them together when solving a problem
• Solving one correctly requires understanding how the other works
• They share a lifecycle, configuration scope, or architectural tradeoff
• Getting one wrong tends to produce bugs in the other

Let library complexity drive the domain count — a focused library may need only 2–3 domains, while a large framework may need 7+. Validate by asking: "Would a developer working on a single feature need to load skills from multiple domains? If so, merge those domains." These are conceptual groupings, not the final skills.

Do not create a group for:

• A single hook, function, or class
• A single doc or reference page
• "Miscellaneous", "Advanced", or "Other"
• Configuration knobs that only affect another group's behavior

Name each domain as work being performed, not what the library provides.

Validation step: After grouping, check each domain by asking: "Would a developer working on a single feature need to load skills from multiple domains?" If yes, merge those domains. Group by developer tasks (what they're trying to accomplish), not by architecture (how the library is organized internally). For example, prefer "writing data" over "producer lifecycle" — the former matches a developer's intent, the latter matches the codebase structure.

3b — Map domains × tasks → skills

Merge your conceptual domains with the maintainer's task list from Phase 2. Each skill should match a specific developer moment while carrying the conceptual depth of its parent domain(s).

A skill is well-shaped when:

• A developer would ask for it by name ("help me set up sync")
• It covers enough for the agent to complete the task end-to-end
• It doesn't require loading 3 other skills to be useful

Some domains produce multiple skills (a broad domain like "data access" might yield "live-queries", "mutations", "offline-sync"). Some tasks span domains (a "go-live" checklist touches security, performance, and configuration). Both are fine.

Also consider:

• Lifecycle/journey skills — if the library's docs include a quickstart guide, go-to-production checklist, or migration path, suggest these as standalone skills. Don't force them if the docs don't have the material.
• Composition skills — when peer deps or examples show consistent co-usage with another library, output a full skill for the integration, not a footnote on a domain.

3c — Flag subsystems within skills

Check each skill area for internal diversity. A skill may be conceptually unified but contain multiple independent subsystems with distinct config interfaces — for example, 5 sync adapters that all solve "connectivity" but each with unique setup, options, and failure modes.

For each skill, ask: "Does this cover 3+ backends, adapters, drivers, or providers with distinct configuration surfaces?" If yes, list them as subsystems. These tell the skill-tree-generator to produce per-subsystem reference files.

Also flag dense API surfaces — if a topic has >10 distinct operators, option shapes, or patterns (e.g. query operators, schema validation rules), note it as a reference_candidates entry.

3d — Extract failure modes

For each skill, extract failure modes that pass all three tests:

• Plausible — An agent would generate this because it looks correct based on the library's design, a similar API, or an older version
• Silent — No immediate crash; fails at runtime or under specific conditions
• Grounded — Traceable to a specific doc page, source location, or issue

Where to find them:

Source	What to extract
Migration guides	Every breaking change → old pattern is the wrong code
Doc callouts	Any "note", "warning", "avoid" with surrounding context
Source assertions	throw and invariant() messages describe the failure
Default values	Undocumented or surprising defaults that cause wrong behavior
Type precision	Source type more restrictive than docs imply
Environment branches	typeof window, SSR flags, NODE_ENV — behavior differs silently
GitHub issues	Recurring bug reports with workarounds → wrong/correct code pairs
GitHub discussions	"How do I…" threads with non-obvious answers → missing skill content

Target 3 failure modes per skill minimum. Complex skills target 5–6.

Code patterns. Every failure mode should include wrong_pattern and correct_pattern fields with short code snippets (3–10 lines each). The wrong pattern is what an agent would generate; the correct pattern is the fix. These feed directly into SKILL.md Common Mistakes sections as wrong/correct code pairs. If the failure mode is purely conceptual (e.g. an architectural choice) rather than a code pattern, omit both fields and explain in mechanism instead.

Cross-skill failure modes. Some failure modes belong to multiple skills. A developer doing SSR work and a developer doing state management both need to know about "stale state during hydration" — they load different skills but need the same advice. When a failure mode spans skills, list all relevant skill slugs in its skills field. The skill-tree-generator will write it into every corresponding SKILL file.

List a cross-skill failure mode once, under its primary skill. Set the skills field to all skill slugs it applies to. Do not duplicate the entry in the YAML — the skill-tree-generator handles duplication into multiple SKILL files at generation time.

3e — Identify cross-skill tensions

Look for places where design forces between skills conflict. A tension is not a failure mode — it's a structural pull where optimizing for one task makes another harder. Examples:

• "Getting-started simplicity conflicts with production operational safety"
• "Type-safety strictness conflicts with rapid prototyping flexibility"
• "SSR correctness requires patterns that hurt client-side performance"

Tensions are where agents fail most because they optimize for one task without seeing the tradeoff. Each tension should name the skills in conflict, describe the pull, and state what an agent gets wrong when it only considers one side.

Target 2–4 tensions. If you find none, the skills may be too isolated — revisit whether you're missing cross-connections.

3f — Map cross-references

Beyond tensions (conflicts) and shared failure modes, identify skills that illuminate each other without conflicting. A cross-reference means: "an agent loading skill A would produce better code if it knew about skill B." These become "See also" pointers in the generated SKILL.md files.

For each pair, note:

• Which skill references which (can be bidirectional)
• Why awareness of the other skill improves output

Examples:

• A quickstart skill references the security checklist ("after setup, audit")
• A state management skill references an SSR skill ("state hydration requires understanding SSR lifecycle")
• A data writing skill references a data reading skill ("writes affect how queries invalidate")

Output these in the cross_references section of domain_map.yaml.

3g — Identify gaps

For each skill, explicitly list what you could NOT determine from docs and source alone. These become interview questions in Phase 4.

Common gaps:

• "Docs describe X but don't explain when you'd choose X over Y"
• "Migration guide mentions this changed but doesn't say what the old behavior was"
• "Source has an assertion here but no doc explains what triggers it"
• "GitHub issues show confusion about X but docs don't address it"
• "I found two patterns for doing X — unclear which is current/preferred"

3h — Discover composition targets

Scan package.json for peer dependencies, optional dependencies, and peerDependenciesMeta. Scan example directories and integration tests for import patterns. For each frequently co-used library, log:

• Library name and which features interact
• Whether it's a required or optional integration
• Any example code showing the integration pattern

These become targeted composition questions in Phase 4e.

3i — Produce the draft

Write the full domain_map.yaml (format in Output Artifacts below) with a status: draft field. Flag every gap in the gaps section.

Present the draft to the maintainer before starting Phase 4:

"I've read the docs and source for [library] and produced a draft with [N] skills and [M] failure modes. I've flagged [K] specific gaps where I need your input."

Include the full draft domain_map.yaml in your message so the maintainer can review it. Also include a checklist of all docs files you read.

── STOP ── Do not proceed to Phase 4 until the maintainer has reviewed the draft and responded. Their response to the draft informs the detail interview questions.

---

Phase 4 — Detail interview (interactive — requires maintainer)

You have the maintainer's task map and a deep read. The interview now fills gaps, validates your understanding, and surfaces implicit knowledge.

You must ask the questions below to the maintainer and wait for their responses. Do not infer answers from documentation or source code — even for gaps you think you can answer from your reading.

Rules for Phase 4

1. One topic per message for open-ended questions. You may batch 2–3 yes/no or short-confirmation questions together.
2. Ask each question as written (you may adapt phrasing to context, but keep questions open-ended — never convert to multiple-choice).
3. Each question must reference something specific from your reading.
4. Wait for the maintainer's response after each question before asking the next.
5. If the maintainer gives a short answer, probe deeper before moving on.
6. Take notes silently. Do not summarize back unless asked.

4a — Draft review (2–3 questions)

Start by confirming or correcting your skill list and failure modes:

"Here's the skill list I've built from our earlier conversation plus the deep read: [list skills with brief descriptions]. Does this still match your thinking? Anything to add, remove, or rename?"

Follow up on any corrections. Then:

"I identified [M] failure modes from the docs and migration guides. Are there important ones I missed — especially patterns that look correct but fail silently?"

4b — Gap-targeted questions (3–8 questions)

For each gap flagged in Phase 3g, ask a specific question. These are not generic — they reference what you found:

Instead of: "What do developers get wrong?" Ask: "I noticed the migration guide from v4 to v5 changed how [X] works, but the docs don't show the old pattern. Do agents still commonly generate the v4 pattern? What does it look like?"

Instead of: "Are there surprising interactions?" Ask: "The source throws an invariant error if [X] is called before [Y], but the docs don't mention ordering. How often do developers hit this?"

Instead of: "What's different in SSR vs client?" Ask: "I found a typeof window check in [file] that changes behavior for [feature]. What goes wrong when developers test only in the browser and deploy with SSR?"

Adapt from this bank of gap-targeted question templates:

• "I found two patterns for [X] in the docs — [pattern A] and [pattern B]. Which is current, and does the old one still work?"
• "The source defaults [config option] to [value], which seems surprising for [reason]. Is this intentional? Do developers need to override it?"
• "GitHub issues show [N] reports of confusion about [X]. What's the underlying misunderstanding?"
• "I couldn't find docs for how [feature A] interacts with [feature B]. What should an agent know about using them together?"
• "The API reference shows [type signature], but the guide examples use a different shape. Which is accurate?"
• "I found [N] GitHub issues/discussions where developers struggled with [X]. The common workaround seems to be [Y] — is that the recommended approach, or is there a better pattern that should be documented?"
• "GitHub discussions show developers repeatedly asking how to combine [feature A] with [feature B]. Is there an intended integration pattern, or is this a gap in the current API?"

4c — AI-agent-specific failure modes (2–4 questions)

These target mistakes that AI coding agents make but human developers typically don't. Agent-specific failures are often the highest-value findings — in testing, maintainer answers to these questions produced the most critical failure modes.

• "What mistakes would an AI coding agent make that a human developer wouldn't? Think about: hallucinating APIs that don't exist, defaulting to language primitives instead of library abstractions, choosing the wrong adapter or integration path."
• "When an agent generates code using your library, what's the first thing you'd check? What pattern would make you immediately say 'an AI wrote this'?"
• "Are there parts of your API where the naming or design is misleading enough that an agent with no prior context would pick the wrong approach? What would it pick, and what should it pick instead?"
• "Are there features where the docs are comprehensive for human developers but would still mislead an agent? For example, features that require understanding unstated context, or where the 'obvious' approach from reading the API surface is wrong."

4d — Implicit knowledge extraction (3–5 questions)

These surface knowledge that doesn't appear in any docs:

• "What does a senior developer using your library know that a mid-level developer doesn't — something that isn't written down anywhere?"
• "Are there patterns that work fine for prototyping but are dangerous in production? What makes them dangerous?"
• "What question do you answer most often in Discord or GitHub issues that the docs technically cover but people still miss?"
• "Is there anything you'd change about the API design if you could break backwards compatibility? What's the current workaround?"

4e — Composition questions (if library interacts with others)

Use what you discovered in Phase 3h. For each integration target identified from peer dependencies and example code, ask targeted questions:

• "I see [library] is a peer dependency and [N] examples import it alongside yours. What's the most common integration mistake?"
• "When developers use [your library] with [other library], are there patterns that only matter when both are present?"
• "I found [specific integration pattern] in the examples. Is this the recommended approach, or is there a better way that isn't documented?"

---

Phase 5 — Finalize artifacts

Merge interview findings into the draft. For each interview answer:

1. If it confirms a skill or failure mode — no action needed
2. If it corrects something — update the map
3. If it adds a new failure mode — add it with source "maintainer interview"
4. If it reveals a new skill — add it
5. If it fills a gap — remove from gaps section

Validate the domain_map.yaml by parsing it with a YAML parser. Check for duplicate keys, invalid syntax, and structural correctness. Fix any issues before presenting the final artifact.

Update status: draft to status: reviewed.

---

Output artifacts

If the maintainer uses a custom skills root, replace skills/ in the paths below with their chosen directory.

Monorepo layout: For monorepos, domain map artifacts go at the REPO ROOT (e.g. _artifacts/domain_map.yaml) since they describe the whole library. Skills are generated per-package later by the tree-generator and generate-skill steps.

1. skills/_artifacts/domain_map.yaml

# domain_map.yaml
# Generated by skill-domain-discovery
# Library: [name]
# Version: [version this map targets]
# Date: [ISO date]
# Status: [draft | reviewed]

library:
  name: '[package-name]'
  version: '[version]'
  repository: '[repo URL]'
  description: '[one line]'
  primary_framework: '[React | Vue | Svelte | framework-agnostic]'

domains:
  - name: '[work-oriented domain name]'
    slug: '[kebab-case]'
    description: '[conceptual grouping — what a developer is reasoning about]'

skills:
  - name: '[task-focused skill name]'
    slug: '[kebab-case]'
    domain: '[parent domain slug]'
    description: '[what a developer is doing — matches a specific task/moment]'
    type: '[core | framework | lifecycle | composition]'
    packages: # required for monorepo; omit for single-package libraries
      - '[primary package name]'
      - '[secondary package name, if skill spans multiple packages]'
    covers:
      - '[API/hook/concept 1]'
      - '[API/hook/concept 2]'
    
       
       
       
     
        
         
         
     
        
         
    
        
         
          
          [     ]
          
          [     ]
         
         
         
         
         [] 
    
        
         


    
     [, ]
     
     


    
     
     


    
     
     
     

2. skills/_artifacts/skill_spec.md

A human-readable companion document. Follow this structure:

# [Library Name] — Skill Spec

[2–3 sentences: what this library is, what problem it solves. Factual,
not promotional.]

## Domains

| Domain | Description           | Skills                  |
| ------ | --------------------- | ----------------------- |
| [name] | [conceptual grouping] | [skill-1, skill-2, ...] |

## Skill Inventory

| Skill  | Type                                   | Domain   | What it covers | Failure modes |
| ------ | -------------------------------------- | -------- | -------------- | ------------- |
| [name] | [core/framework/lifecycle/composition] | [domain] | [list]         | [count]       |

## Failure Mode Inventory

### [Skill name] ([count] failure modes)

| #   | Mistake  | Priority | Source                 | Cross-skill?             |
| --- | -------- | -------- | ---------------------- | ------------------------ |
| 1   | [phrase] | CRITICAL | [doc/source/interview] | [other skill slugs or —] |

[Repeat table for each skill.]

## Tensions

| Tension        | Skills              | Agent implication       |
| -------------- | ------------------- | ----------------------- |
| [short phrase] | [slug-a] ↔ [slug-b] | [what agents get wrong] |

## Cross-References

| From   | To     | Reason                                    |
| ------ | ------ | ----------------------------------------- |
| [slug] | [slug] | [why awareness of one improves the other] |

## Subsystems & Reference Candidates

| Skill  | Subsystems                     | Reference candidates       |
| ------ | ------------------------------ | -------------------------- |
| [slug] | [adapter1, adapter2, ...] or — | [topic needing depth] or — |

## Remaining Gaps

| Skill  | Question                 | Status |
| ------ | ------------------------ | ------ |
| [slug] | [what still needs input] | open   |

[Omit this section if all gaps were resolved in the interview.]

## Recommended Skill File Structure

- **Core skills:** [list which skills are framework-agnostic]
- **Framework skills:** [list per-framework skills needed]
- **Lifecycle skills:** [list journey/lifecycle skills if applicable]
- **Composition skills:** [list integration seams needing composition skills]
- **Reference files:** [list skills needing references/ based on subsystems
  or dense API surfaces]



| Library | Integration points | Composition skill needed?     |
| ------- | ------------------ | ----------------------------- |
| [name]  | [what interacts]   | [yes/no — if yes, skill name] |

---

Constraints

Check	Rule
Quick scan before interview	Never interview without at least reading README and package structure
High-level interview before deep read	The maintainer's task map informs what you read deeply
Interview phases are interactive	Phases 2 and 4 require sending questions to the maintainer and waiting
Docs are not a substitute	Documentation cannot replace maintainer answers — even comprehensive docs
Open-ended questions stay open	Never convert interview questions to multiple-choice or yes/no
Minimum question counts enforced	Each sub-section's minimum count must be met; zero questions = violation
STOP gates are mandatory	Do not proceed past a STOP gate without maintainer response
Batch only confirmations	Yes/no questions may batch 2–3; open-ended questions get their own message
Questions reference findings	No generic questions — cite what you found
Skills are task-focused	Each skill matches a developer moment, not a conceptual area
3+ failure modes per skill	Complex skills target 5–6
Every failure mode sourced	Doc page, source file, issue link, or maintainer interview
Gaps are explicit	Unknown areas flagged, not guessed
No marketing prose	Library description is factual, not promotional
domain_map.yaml is valid YAML	Parseable by any YAML parser
Draft before detail interview	Present draft for review before Phase 4
Draft reviewed before Phase 4	Maintainer must acknowledge or respond to draft before detail interview
Agent-specific failures probed	Always ask AI-agent-specific questions in Phase 4c
Compositions discovered from code	Scan peer deps and examples before asking composition questions
Cross-skill failure modes tagged	Failure modes spanning skills list all relevant slugs
Tensions identified	2–4 cross-skill tensions; if none found, revisit skill boundaries

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Cross-model compatibility notes

This skill is designed to produce consistent results across Claude, GPT-4+, Gemini, and open-source models. To achieve this:

• All instructions use imperative sentences, not suggestions
• Interview phases use explicit STOP gates to prevent models from continuing autonomously past interactive checkpoints
• Hard rules at the top override any model tendency to rationalize skipping interactive phases when documentation is available
• Open-ended questions are explicitly protected from conversion to multiple-choice or confirmation prompts, which models default to when they have enough context to pre-populate answers
• Output formats use YAML (universally parsed) and Markdown tables (universally rendered)
• Examples use concrete values, not placeholders like "[your value here]"
• Section boundaries use Markdown headers (##) for navigation and --- for phase separation
• No model-specific features (no XML tags in output, no tool_use assumptions)