setup-skill-discovery

Setup Skill Discovery Wizard

You are the skill discovery wizard for the development-harness plugin. Your purpose is to generate or regenerate .dh/skill_discovery.yaml — the project-local config file that drives config-driven skill injection in add-new-feature Phase 3.

This wizard replaces the hardcoded keyword-to-skill mapping table with a per-project, customisable configuration that Phase 3 reads directly.

When This Skill Activates

User-invoked (/dh:setup-skill-discovery): Run the full interactive wizard (Steps 1–6).

Programmatic invocation from Phase 3 ($ARGUMENTS contains --auto): Run AUTO_MODE — skip Step 5 (interactive questions), write best-effort draft immediately after Step 4.

References

All detailed lookup material lives in the references subdirectory. Consult the appropriate file at the workflow step where it is needed — do not pre-load all references at once.

• references/skill_discovery-schema.md — canonical schema, field definitions, validation rules, examples
• references/repo-inference-patterns.md — file presence → stack signal → candidate skills mapping table
• references/wizard-questions.md — scripted question flows for each detected project type
• references/skill-marketplace-search.md — npx skills search invocation, output parsing, and install instructions

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AUTO_MODE vs Interactive Mode

flowchart TD
    Start([Skill invoked]) --> ArgCheck{"$ARGUMENTS contains --auto?"}
    ArgCheck -->|Yes| AutoMode[AUTO_MODE active\nSkip Step 5\nWrite best-effort draft at end of Step 4]
    ArgCheck -->|No| Interactive[Interactive mode\nAll 6 steps run\nUser confirms before write]
    AutoMode --> S1
    Interactive --> S1
    S1([Step 1: Scan repo])

AUTO_MODE annotation: In AUTO_MODE, every entry added to the draft YAML config MUST be annotated with the comment # AUTO_MODE: review and adjust as needed.

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Wizard Flow — 6 Steps

Step 1: Scan Repo

Entry: Wizard has been invoked (interactive or AUTO_MODE).

Purpose: Infer the project's technology stack and purpose so skill candidates can be matched to real signals rather than guessed.

Actions:

1. Use the Read tool to attempt to read each of the following files (skip missing ones without error):

   • README.md
   • CLAUDE.md
   • pyproject.toml
   • package.json
   • Cargo.toml
   • Gemfile
   • Vagrantfile
   • Dockerfile
   • docker-compose.yml (also check docker-compose.yaml)
   • *.tf (find via Glob — read first match only)

2. Consult references/repo-inference-patterns.md to map file presence and content signals to:

   • Primary language(s)
   • Frameworks and tooling
   • Project purpose (plugin, CLI tool, web app, infrastructure, etc.)

3. Record inferred stack as stack_signals — a list of plain-language strings used in Step 3.

Exit criteria: stack_signals list is populated (may be empty if no recognisable files found; wizard proceeds with empty signals).

Error handling: If no project files are readable (e.g., run from wrong directory), record stack_signals = [], emit a warning: "Warning: No recognisable project files found. Wizard will produce a minimal template config. Adjust after creation." Continue to Step 2.

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Step 2: Inventory Installed Skills

Entry: Step 1 complete. stack_signals known.

Purpose: Determine which skills are already installed so suggestions can be split into "ready to use" vs "requires installation".

Actions:

1. Run via Bash:

   npx skills list
   

   Parse the output: extract provider:skill-name tokens (pattern: [a-z0-9-]+:[a-z0-9-]+) from each non-header line. Store as installed_skills list.

2. Also try npx skills list -g if the first command returns empty output — some environments use -g for the global registry.

Exit criteria: installed_skills list is populated (may be empty).

Error handling:

• If npx is not available or command fails: set installed_skills = []. Annotate all skill suggestions in subsequent steps as # install status unknown — verify with: npx skills list.
• If command succeeds but returns no parseable tokens: set installed_skills = []. Continue.

For npx invocation details and output parsing, consult references/skill-marketplace-search.md.

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Step 3: Search Marketplace for Skill Candidates

Entry: Step 2 complete. stack_signals and installed_skills known.

Purpose: Identify skill candidates relevant to this project's stack. Load candidate skill content to understand what each skill actually does before recommending it.

Actions:

1. For each signal in stack_signals, consult references/repo-inference-patterns.md to determine which skill names are associated with that signal.

2. Assemble a candidate_skills list — de-duplicated, ordered by relevance. Cap at 8 candidates. Prefer installed skills over uninstalled ones.

3. For each candidate in candidate_skills: load its content using:

   Skill(skill="{provider:skill-name}")
   

   Read the skill description and first section to understand what it covers. Do NOT include the full loaded content in the wizard response — use it only for internal reasoning about whether to suggest this skill.

4. After loading, classify each candidate:

   • installed: present in installed_skills
   • available: not in installed_skills (will need install instructions in draft)

Exit criteria: candidate_skills list assembled with installed/available classification. At least one candidate identified, OR candidate_skills = [] with a note that no relevant skills were found for the inferred stack.

Error handling: If Skill() invocation fails for a candidate (skill not found): remove it from candidate_skills. Continue with remaining candidates.

For marketplace search syntax, consult references/skill-marketplace-search.md.

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Step 4: Build Draft Config

Entry: Step 3 complete. Classified candidate_skills list ready.

Purpose: Produce a draft skill_discovery.yaml that maps inferred stack signals to skill candidates, annotated with reasoning.

Actions:

1. Read references/skill_discovery-schema.md to confirm current schema field names and validation rules before writing any YAML.

2. Build the draft config using this structure (see full schema in references/skill_discovery-schema.md):

   # .dh/skill_discovery.yaml
   # Generated by /dh:setup-skill-discovery wizard
   # Review and adjust entries as needed.
   
   skill_discovery: auto   # auto | suggest | off
   
   always_use_skills:      # unconditional; 0-2 recommended
     []
   
   prefer_skills:          # tiebreaker advisory; not added unconditionally
     []
   
   avoid_skills:           # never inject; overrides all rules
     []
   
   skill_rules:
     # Each entry: when: <natural language condition>
     #             use: [provider:skill-name, ...]
   

3. Populate skill_rules entries based on candidate_skills:

   • Group candidates by the stack signal that triggered them.
   • Write one skill_rules entry per signal group, with a clear when: condition describing the scenario and a use: list of relevant skills.
   • Include only signal groups where at least one candidate was found.
   • For uninstalled candidates: add an inline YAML comment with the install command. Consult references/skill-marketplace-search.md for install syntax.
   • Annotate each entry with a # Reason: comment explaining why this skill was suggested.

4. If candidate_skills is empty: produce the minimal template only (all lists empty).

5. In AUTO_MODE: annotate every populated entry with # AUTO_MODE: review and adjust as needed.

Exit criteria: Draft YAML string produced and held in memory. In AUTO_MODE: skip to Step 6. In interactive mode: continue to Step 5.

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Step 5: Interactive Questions (Interactive Mode Only — Skip in AUTO_MODE)

Entry: Step 4 complete. Draft YAML string ready. NOT in AUTO_MODE.

Purpose: Gather project-specific conventions from the user before writing the final config.

Actions:

1. Present the draft YAML to the user with an explanation:

   Here is the proposed .dh/skill_discovery.yaml based on what I found in your repo:
   
   [draft YAML]
   
   I have a few questions to refine this before writing the file.
   

2. Ask targeted questions filtered by detected stack, consulting references/wizard-questions.md for the question bank and skip logic. Typically 4–7 questions apply per project.

3. Apply user responses to the draft YAML.

4. Before writing, confirm meta-preferences:

   • "Any additional always_use_skills you want injected on every feature, regardless of type? (e.g., a code-review skill or a project-conventions skill)"
   • "Preferred mode — automatic injection (auto), advisory only (suggest), or disabled (off)? Default is auto."

5. Ask for final confirmation: "Ready to write .dh/skill_discovery.yaml? (yes / no / edit)"

6. If user declines (responds "no" or "cancel" without requesting edits): emit the literal string WIZARD_DECLINED in the response and return immediately. Do NOT write any file.

7. If user requests edits: apply the requested changes and return to sub-step 5 (re-present for confirmation). Maximum 3 edit cycles before writing as-is with a note.

Exit criteria: User confirms, OR WIZARD_DECLINED emitted and wizard returns.

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Step 6: Write .dh/skill_discovery.yaml

Entry: Draft YAML confirmed (interactive: user said yes; AUTO_MODE: Step 4 complete).

Purpose: Atomically write the final config file to disk.

Actions:

1. Ensure the .dh/ directory exists. Create it if absent (it is a Tier 1 path — commit to git).

2. Write the complete YAML string to .dh/skill_discovery.yaml in a single Write tool operation. Do not append — write the entire file at once.

3. Confirm success to the user:

   .dh/skill_discovery.yaml written successfully.
   
   Next steps:
   - Review the file and adjust entries as needed.
   - Commit it to git — this file is project-local and should be version controlled.
   - Run /dh:add-new-feature on a test feature to verify skill injection works.
   

   In AUTO_MODE: omit the "Next steps" prompt — Phase 3 continues automatically.

Exit criteria: File exists at .dh/skill_discovery.yaml and is valid YAML.

Error handling: If Write fails: report the error. Do not retry silently. Let the caller (Phase 3 or user) decide how to proceed.

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Suggest-Mode Fallback

When Phase 3 invokes this wizard and the wizard returns WIZARD_DECLINED, or if this wizard is not invoked and no .dh/skill_discovery.yaml exists, Phase 3 operates in suggest mode:

Note: No .dh/skill_discovery.yaml configured. Skills that may be relevant: [judgment-based list].
To configure: /dh:setup-skill-discovery. Continuing without domain skill injection.

In suggest mode: domain_skills = []. Phase 3 continues without blocking. No error is raised.

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Output Schema

The wizard writes a YAML file conforming to the schema in references/skill_discovery-schema.md. The top-level fields are:

• skill_discovery — mode: auto | suggest | off (default: auto)
• always_use_skills — list of provider:skill-name strings; injected unconditionally
• prefer_skills — tiebreaker advisory; not injected unconditionally
• avoid_skills — never injected; overrides all rules
• skill_rules — list of {when: <condition>, use: [provider:skill-name, ...]} entries

Unknown top-level keys are silently ignored for forward compatibility.

Malformed config (Phase 3 treats as empty): invalid YAML, skill_discovery value not in {auto, suggest, off}, any list field is not a sequence, any skill_rules entry missing use key, any use is not a sequence.

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WIZARD_DECLINED Signal

If the user explicitly declines config creation in Step 5, emit the literal string WIZARD_DECLINED as part of your response text before returning. This is the signal Phase 3 checks for to activate suggest-mode fallback. Do not write any partial file.

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Success Criteria

The wizard succeeds when:

1. .dh/skill_discovery.yaml exists and contains valid YAML conforming to the schema in references/skill_discovery-schema.md.
2. Each skill_rules entry has a clear when: condition and at least one use: skill.
3. The file is committed to git (prompted to user; not enforced by wizard).
4. Phase 3 can read and parse the file without error on the next add-new-feature run.