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delivery-bootstrap

Use when starting implementation on a new or unfamiliar codebase. Auto-detects tech stack and sets up development context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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  • SKILL.md8.9 KB

SKILL.md(원문)

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Delivery Bootstrap Skill

Just-in-Time tech stack detection and setup.

Workflow

  1. Check product_type from .claude/diamonds/active.yml:
    • If product_type is set (from /mycelium:interview), use it to determine the delivery profile.
    • If not set, scan for indicators per ${CLAUDE_PLUGIN_ROOT}/jit-tooling/detector.md Step 1b:
      • Curriculum/lesson plans, LMS config -> content_course
      • Manuscript/chapters, editorial calendar -> content_publication
      • Video scripts, subtitle files, podcast RSS -> content_media
      • Prompt templates, model configs, agent definitions -> ai_tool
      • Service blueprints, pricing docs -> service_offering
    • If non-software product_type detected: skip software tooling detection (Steps 2-3 below), configure product-type-appropriate validation instead, and proceed to Step 4.

1b. Scan project root for technology indicators (software and ai_tool with code):

  • Package files: package.json, Cargo.toml, go.mod, requirements.txt, pyproject.toml, Gemfile, pom.xml, build.gradle
  • Config files: tsconfig.json, .eslintrc, .prettierrc, rustfmt.toml, .editorconfig
  • CI/CD: .github/workflows, .gitlab-ci.yml, Jenkinsfile, Dockerfile
  • Framework indicators: next.config.js, nuxt.config.ts, angular.json, etc.
  1. Identify stack components (software/ai_tool with code):

    • Language(s) and version(s)
    • Framework(s)
    • Package manager
    • Test runner and framework
    • Linter and formatter
    • Build tool
    • CI/CD platform
    • Database (if detectable)
    • Deployment target
  2. Verify tooling works:

    • Run build command
    • Run test command
    • Run lint command
    • Note any failures or warnings

3a. Offer the feedback-loop tooling menu (OFFER-MENU layer):

  • For the detected stack, present the best-practice menu from ${CLAUDE_PLUGIN_ROOT}/jit-tooling/security-scanning.md (SAST, dep audit, secrets, test runner, linter), ordered smallest-friction first (typically: secrets scan → linter → SAST → dep audit → container scan).
  • Frame as: "Best practice is to have tools help you shorten the feedback loop. For your detected stack ({lang}), the menu is: {list}. Want help finding and configuring any of these?"
  • Never auto-install. Never pick rulesets on the user's behalf. Adoption is per-tool, per-consent. The user owns the choice. See feedback-jit-nudge-not-push (founder principle, 2026-05-26).
  • If user declines or defers, record the offer in active-stack.yml under tooling_offers_declined with timestamp — later shape-triggers (Step 3b) can re-surface the relevant subset.

3b. Risk-shape re-offer (RISK-TRIGGERED layer):

  • Scan code for risk-shape patterns. If any fires AND the corresponding tool was declined in 3a, re-offer the relevant subset with the risk as citation. Patterns:
    • AUTH shape: /login, /auth, /register, password, session, token, jwt, oauth, x-user-id and similar trust-bearing headers → re-offer SAST + suggest /mycelium:threat-model and /mycelium:security-review. SAST blind spot: identity-trust design (e.g., x-user-id header trusted as auth) is NOT catchable by SAST tools — they don't model intent. Route to /mycelium:security-review regardless of whether SAST consent was given.
    • AI shape: imports from ai_components categories per detector.md Step 1c → suggest /mycelium:xai-check
    • PII / data shape: email, ssn, phone, address, payment, card, schema fields matching PII patterns → suggest /mycelium:privacy-check + secrets scanner
    • Public endpoint shape: routes without auth middleware → suggest SAST + DAST. SAST blind spot: "no authorization check" is design-level; SAST can flag suspicious patterns but cannot confirm absence-of-intent. Route to /mycelium:security-review.
    • File-upload shape: upload, multipart, FormFile, ServeFile, ServeContent, Content-Disposition, file-system write from request → re-offer SAST + suggest /mycelium:threat-model with explicit MIME-allowlist + filename-sanitization + size-cap + overwrite-policy review. SAST partial coverage: gosec/semgrep catch path-traversal (filepath.Join without Base), but cannot catch design flaws (MIME confusion via stored files, public-list disclosure, missing auth).
  • Cite the trigger explicitly per the operating contract's attribution rule (Communication Rule 4): (per: AUTH shape detected at app.py:42 → SAST recommended).
  • The 4-layer composition (OFFER-MENU + RISK-TRIGGERED + NUDGE-AT-FAILURE + PR-TIME) is derived from a deep-study comparison of 10 adoption approaches (2026-05-26). Strongest single-finding: contextual nudges at decision moments produce ~8× higher detection vs no-nudge baseline (Less is More, arxiv 2202.04586; consistency_only — single experimental study). Other layers live in /mycelium:security-review, /mycelium:threat-model, /mycelium:reflexion (NUDGE-AT-FAILURE) and /mycelium:definition-of-done (PR-TIME).
  1. Document existing patterns:

    • Code organization (monorepo, src layout, etc.)
    • Naming conventions
    • Test patterns
    • Error handling patterns
    • API patterns
  2. Scaffold Architecture Decision Records (if applicable):

    • Check: Does the project have significant architecture decisions ahead? Indicators:
      • Multiple competing implementation approaches (e.g., REST vs GraphQL, monolith vs microservices)
      • New infrastructure choices (database, hosting, auth provider)
      • Framework selection or migration
      • Integration with external systems
    • If yes: create docs/adr/ directory and a template file docs/adr/0000-template.md:
      # [NUMBER]. [TITLE]
      
      Date: [DATE]
      
      ## Status
      Proposed | Accepted | Deprecated | Superseded by [ADR-XXXX]
      
      ## Context
      What is the issue that we're seeing that is motivating this decision?
      
      ## Decision
      What is the change that we're proposing and/or doing?
      
      ## Consequences
      What becomes easier or more difficult to do because of this change?
      
    • If no significant architecture decisions are foreseeable: skip. Don't scaffold ceremony for a weekend project.
    • ADR format: Nygard (Context/Decision/Consequences). Lightweight by design — each ADR should be readable in under 2 minutes.
  3. Output:

    ## Stack Profile
    - Language: [x] v[y]
    - Framework: [x]
    - Package manager: [x]
    - Test: [command] ([framework])
    - Lint: [command]
    - Build: [command]
    - CI/CD: [platform]
    
    ## Commands
    - Install: [command]
    - Dev server: [command]
    - Test: [command]
    - Build: [command]
    - Lint: [command]
    
    ## Observed Patterns
    - [list of patterns detected]
    
    ## Issues Found
    - [any broken tooling or warnings]
    
    ## Architecture Decision Records
    - Scaffolded: yes/no
    - Location: docs/adr/
    - Pending decisions: [list if any identified during bootstrap]
    
    ## Risk Shapes Detected
    - [list of risk shapes fired in Step 3b, with citations]
    
    ## Recommended Next Skills
    - [if AUTH / public-endpoint / file-upload / PII shape fired] → run `/mycelium:security-review` before merging (per: SAST blind spot — identity/authorization/business-logic flaws aren't tool-catchable)
    - [if AI shape fired] → run `/mycelium:xai-check`
    - [if PII shape fired] → run `/mycelium:privacy-check`
    

Rules

  • Use the project's established patterns. Don't impose external preferences.
  • Be language-agnostic in principles, language-specific in implementation.
  • If tooling is broken, flag it rather than silently working around it.

Canvas Output

Create/update .claude/jit-tooling/active-stack.yml with detected stack configuration — project state, in the consumer's .claude/, never the shared plugin cache (which is read-only in practice and wiped on /plugin update). See ${CLAUDE_PLUGIN_ROOT}/jit-tooling/active-stack.example.yml for the expected format.

This file is the trigger for the whole XAI/AI-Act gate family: ai_components.detected gates Theory Gate 13 and /mycelium:xai-check, plus the AI arms of /mycelium:threat-model, /mycelium:launch-tier, /mycelium:regulatory-review, and /mycelium:definition-of-done. If it is not written here, every one of those silently reports N/A.

Theory Citations

  • Forsgren: Accelerate (tooling and automation)
  • Smart: Sooner Safer Happier (remove friction)
  • Nygard: Architecture Decision Records (lightweight decision documentation)

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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