AI Assets

Guides creation, modification, and validation of Claude Code AI assets. Every asset is a prompt for an LLM — apply prompt engineering rigor throughout.

Apply Agent(prompt-engineer) role for all steps below.

1. Determine Operation and Scope

Clarify with the user:

• Operation: create | modify | validate | analyze
• Asset type: rule | workflow | skill | hook | agents-md
• Target: file path or asset name

If the user's intent is ambiguous, ask before proceeding.

2. Gather Context

Read and internalize before making any changes:

1. Target asset (if exists — for modify/validate/analyze)
2. Vendor specs: framework-summary.md — Section 2.x matching the asset type. This is the authoritative source for Claude Code capabilities, formats, and constraints
3. Authoring standards: rules/global_rules.md — language, file size limits, cross-reference rules
4. Project conventions: CLAUDE.md — naming, frontmatter, source management, interconnection patterns
5. Existing assets of the same type — scan the target directory to understand tone, structure, and naming patterns already in use
6. Context engineering guide: context-engineering skill — context stack model, production checklists, reference templates. Required reading for AI-facing assets

3. Map Dependency Chain

Search the entire asset tree for references to/from the target. Build a dependency map:

[target asset]
├─ OUT (this asset references):
│  ├─ /workflow-name  → file path [OK | MISSING]
│  ├─ @skill-name    → file path [OK | MISSING]
│  └─ rule-name      → file path [OK | MISSING]
├─ IN (referenced by):
│  ├─ asset-a.md (line N)
│  └─ asset-b.md (step N)
└─ HOOKS (enforced by):
   └─ script.py → hook event

Search patterns: /asset-name, @asset-name, ../type/path.md, prose mentions in integration sections.

Flag issues:

• Broken refs: outgoing reference target doesn't exist
• Orphaned: zero incoming references (nothing calls or mentions this asset)
• Missing links: asset logically belongs in a chain but isn't connected
• Circular deps: A → B → A (acceptable for rules↔workflows, flag for review otherwise)

For analyze operations — present the full map and stop here. For other operations — use the map to inform subsequent steps.

4. Companion Asset Analysis

Before authoring, evaluate whether the target asset needs companion assets to be effective:

• Rule that defines a multi-step procedure → extract to a skill with checklists as resource files
• Rule that needs automated enforcement → propose a hook script
• Workflow with reusable sub-sequences → extract sub-workflows (/sub-workflow)
• Workflow that bundles complex knowledge → extract a skill for the knowledge, reference via @skill-name
• Skill that operationalizes a rule → verify the rule exists and cross-references the skill
• Any asset approaching 12K chars → split: offload detail to skill resources or sub-workflows

If companion assets are needed, create them as part of the same operation. Apply this entire workflow to each companion asset recursively (steps 2–8).

5. Author or Edit the Asset

Apply the asset-type spec from framework-summary.md Section 2.x:

<rule_spec>

• Frontmatter: trigger ((auto-loaded) | (agent) | glob | manual), description
• description is the activation signal for (agent) — write it as a keyword-rich, specific summary. Too broad = false positives. Too narrow = missed activations
• Trigger mode: (auto-loaded) for foundational constraints; (agent) for contextual guidance; glob for file-type-specific; manual for on-demand roles/expertise
• Structure: imperative instructions, bullet points, numbered lists, XML tags for sections
• Max 12,000 chars </rule_spec>

<workflow_spec>

• Frontmatter: description
• Numbered steps — each step is one concrete action with a clear completion criterion
• Compose with /sub-workflow to avoid duplication across workflows
• Include decision points (if/then) for branching logic
• Max 12,000 chars </workflow_spec>

<skill_spec>

• Folder: skill-name/SKILL.md + resource files
• Frontmatter: name, description
• description drives progressive disclosure — optimize for automatic activation by Claude Code
• Offload checklists, templates, guides to resource files (no size limit on resources)
• Max 12,000 chars for SKILL.md only </skill_spec>

<hook_spec>

• Config: JSON with hook event name, command, working directory
• Script: Python 3; stdin JSON; exit 0 (pass) or 2 (block)
• 11 events: pre/PostToolUse (Read), pre/PostToolUse (Write|Edit), pre/PostToolUse (Bash), pre/PostToolUse, UserPromptSubmit, Stop, WorktreeCreate
• Merge order: System → User → Workspace (all execute) </hook_spec>

<agents_md_spec>

• No frontmatter — auto-scoped by directory placement
• Root = always on; subdirectory = scoped to that dir tree
• Focus: what this directory does, which rules/workflows/skills apply
• Be specific, avoid redundancy with parent CLAUDE.md </agents_md_spec>

Role Hierarchy and Routing

Design role rules for composable specialization via Claude Code's agent/skill system:

• Layer 1 — Base role ((agent)): Universal principles, reasoning protocol, hard rules. Example: software-engineer.role.md
• Layer 2 — Specialization ((agent) or glob): Stack-specific patterns. (agent) for project context matching; glob for file-type binding
• Layer 3 — Explicit override (manual): User forces via @role-name

Routing: CLAUDE.md declares tech stack → (agent) roles with matching description keywords activate automatically → glob activates on file patterns → multiple roles compose (base + specialization)

Composability rules:

• Base MUST NOT duplicate specializations; specializations MUST NOT duplicate base
• Each role's description must clearly state scope to avoid activation overlap

6. Prompt Engineering Review

Every asset is a prompt for an LLM. Apply Agent(prompt-engineer) Reasoning Protocol to evaluate each asset:

1. Assess — identify the prompt surface: what will Claude Code read and how will it shape behavior?
2. Diagnose — what could go wrong? (wrong activation, vague instructions, conflicting rules, token waste, missed edge cases)
3. Design review — verify the asset follows prompt engineering principles below
4. Eval — define ≥1 scenario proving the asset works as intended + ≥1 scenario where it should NOT activate

Universal Checks (all asset types)

1. Instruction clarity: imperative, unambiguous. No hedging ("try to", "consider") — use "do X", "never Y", "always Z"
2. Structure: sections separated via headings, XML tags, bullet lists. Instructions never buried in prose
3. Token efficiency: every token earns its place. Remove filler, redundancy, obvious statements
4. Instruction hierarchy: system constraints > developer guidelines > user preferences. Explicit priority on conflicts
5. Security: no secrets, PII, API keys. Instructions separated from data. No injection vectors
6. Conflict check: no contradictions with other active assets (rules, CLAUDE.md, other workflows)

Context Engineering Checks (all asset types)

Apply context-engineering skill principles — every asset is context that enters Claude Code's window:

7. Context stack awareness: Which context stack layer does this asset operate in? (L1: policy, L2: developer instructions, L3: tool contracts, L4: runtime state, etc.)
8. Position effects: Critical constraints (Hard Rules, NEVER items) placed at the beginning. Output contracts at the end. No critical info buried in the middle of long sections
9. Layer separation: Policy ("never do X") is separate from knowledge ("how to do Y"). No mixed sections
10. Token budget impact: Does this asset justify its token cost? Can any section be compressed without losing signal?
11. Cacheable prefix: For roles/rules — static content that rarely changes grouped to enable KV cache reuse

Asset-Type-Specific Checks

Rules ((agent)):

• Does description contain keywords users naturally use when the rule should activate?
• Is it specific enough to avoid false positives, broad enough to not miss real cases?
• Does it differentiate clearly from other rules with similar scope?
• Are negative instructions ("do NOT", anti-patterns) sufficient to prevent wrong behavior?
• Will Claude Code consistently produce the intended behavior when this rule is active?

Workflows:

• Does each step have a clear completion criterion?
• Are decision points (if/then) explicit — no ambiguous branching?
• Could Claude Code execute this workflow autonomously with minimal user input?
• Are sub-workflow calls (/name) and skill refs (@name) correct?

Skills:

• Does description trigger progressive disclosure for the right tasks only?
• Are resource files referenced and accessible?
• Is knowledge actionable (checklists, patterns) vs purely informational?

Rules (roles):

• Are Hard Rules concrete and testable (not aspirational)?
• Is the role boundary clear — what is in scope vs delegated?
• Does it compose cleanly with Layer 1 base role without duplication?

7. Validate (mandatory on every create/modify)

This step is non-optional. Run the full checklist on every create or modify operation to prevent dependency corruption, broken cross-references, and size violations.

Format and structure:

• Correct YAML frontmatter for asset type
• File size ≤ 12,000 characters (rules, workflows, SKILL.md)
• All content in English
• File name follows naming convention (see CLAUDE.md)

References and dependencies:

• All /workflow references resolve to existing workflow files
• All @skill references resolve to existing skill folders
• All relative paths are valid and point to existing files
• No absolute or machine-specific paths (C:\Users\..., /home/...)
• Related assets updated with cross-references to this asset

Prompt quality (per Agent(prompt-engineer) review — Step 6):

• All checks from Step 6 passed (universal + asset-type-specific)
• description is keyword-rich and activation-appropriate
• No secrets, API keys, or PII
• Consistent with existing assets in tone and structure
• Behavioral test: Claude Code would produce correct behavior with this asset active

Context engineering (per context-engineering skill — Step 6):

• Asset maps to a context stack layer (or spans multiple with justification)
• No "lost-in-the-middle" — critical info not buried in middle of long asset
• Policy and knowledge sections distinct
• AI-facing assets reference context-engineering skill where relevant

Dependency chain integrity:

• No broken outgoing references
• Asset is reachable (referenced by at least one other asset or is top-level)
• No collateral damage — assets that reference this one still work after the change
• Companion assets (from step 4) are also validated
• SDLC lifecycle map in ARCHITECTURE.md updated if applicable

8. Finalize

1. Verify all assets (primary + companions) are saved in correct directories
2. If cross-references changed, verify all affected assets are updated
3. Present a summary to the user:
   • What was created/modified (list all assets including companions)
   • Dependency chain status (all refs OK / issues found)
   • Role routing status (for role assets: which layers are covered, CLAUDE.md tech stack alignment)
   • Validation checklist result (all passed / items requiring attention)
   • Any remaining action items