Cortex TMS: AI Prompt Library

This file contains project-aware prompt templates for streamlined AI collaboration. Use

cortex-tms prompt <name>

to quickly access these prompts.

init-session

Review

NEXT-TASKS.md

docs/core/ARCHITECTURE.md

, and

CLAUDE.md

. Summarize current priorities and propose a step-by-step plan for the next task.

feature

Implement

[FEATURE]

. First, check

ARCHITECTURE.md

for constraints, follow

PATTERNS.md

for style, and validate logic against

DOMAIN-LOGIC.md

debug

I'm seeing

[ISSUE]

. Check

docs/core/TROUBLESHOOTING.md

for known issues, then trace the logic through

ARCHITECTURE.md

to find the failure point.

review

Review the current changes against

PATTERNS.md

. Flag any violations of project conventions and suggest specific fixes.

refactor

Refactor

[COMPONENT]

. Improve structure per

PATTERNS.md

while ensuring behavior remains strictly compliant with

DOMAIN-LOGIC.md

decision

We need to decide on

[TOPIC]

. Draft a new Architecture Decision Record in

docs/core/DECISIONS.md

using the project's standard format.

finish

Task complete. Execute the Maintenance Protocol:

Run
```
pnpm run docs:sync
```
to ensure version integrity across documentation
Update
```
NEXT-TASKS.md
```
with task status and next priorities

Run

node bin/cortex-tms.js validate --strict

to verify project health

Follow Git Protocol: commit with conventional format and co-authorship
Suggest next priority from backlog

bootstrap

You have just been added to a project that uses Cortex TMS for AI governance. The documentation files exist but contain placeholder content that needs to be populated with real project information.

Your task: Analyze this codebase and populate the TMS documentation files as DRAFTS for human review.

Step 1 - Understand the project:

Read
```
package.json
```
(or equivalent) for tech stack and dependencies
Scan the directory structure to understand components
Read entry points (
```
src/index.*
```
,
```
src/app.*
```
,
```
bin/*
```
)
Read any existing README.md for project context

Step 2 - Populate ARCHITECTURE.md (

docs/core/ARCHITECTURE.md

Fill "Quick Context" with what the project does, who it's for, key constraint
Fill "System Overview" with 2-3 sentences about the system
Fill "Component Map" table with real components from the codebase
Fill "Core Data Flow" with the actual happy path

Mark all populated sections with:

<!-- AI-DRAFT: Review before treating as canonical -->

Step 3 - Customize CLAUDE.md:

Update CLI commands with the project's actual commands (from package.json scripts)
Add project-specific operational loop steps
Add any project-specific rules or conventions you observed

Step 4 - Customize copilot-instructions.md (

.github/copilot-instructions.md

Fill in the actual tech stack
Add real conventions observed in the code
Add project-specific prohibitions

Step 5 - Populate NEXT-TASKS.md:

If there are open TODOs, issues, or incomplete features, suggest initial tasks
Otherwise, suggest "Review AI-generated documentation" as first task

Rules:

Be SPECIFIC to this project - no generic advice
Reference actual file paths from the codebase
Mark every generated section with
```

```
comment
Ask the user to confirm before writing each file
If unsure about something, ASK rather than guess
Do NOT open or send content from
```
.env*
```
,
```
*.pem
```
,
```
id_rsa*
```
, or similar secret files
Skip
```
node_modules/
```
,
```
dist/
```
,
```
build/
```
,
```
coverage/
```
,
```
.git/
```

populate-architecture

Analyze this codebase and populate

docs/core/ARCHITECTURE.md

Read
```
package.json
```
for tech stack, then scan
```
src/
```
directory structure
Fill "Quick Context": what it does, who it's for, key constraint
Fill "Component Map": real components with responsibilities and tech stack
Fill "Core Data Flow": trace the main happy path from entry point to output
Fill "Deployment & Infrastructure" from config files and CI workflows

Be specific to THIS project. Reference actual file paths. Mark sections with

<!-- AI-DRAFT: Review before treating as canonical -->

. Present the draft and ask for confirmation before writing.

discover-patterns

Review this codebase and document patterns for

docs/core/PATTERNS.md

Scan representative files in
```
src/
```
for recurring conventions
Identify: naming conventions, error handling, component structure, testing patterns
For each pattern, find a canonical example (actual file path + code snippet)
Document as "Pattern N: Name" with Rule, Anti-Pattern, and Canonical Example

Extract ACTUAL patterns from code, not generic best practices. Mark all entries with

<!-- AI-DRAFT -->

. Present draft for review before writing.

extract-rules

Extract domain-specific rules for

docs/core/DOMAIN-LOGIC.md

Look for validation logic (schemas, validators, form rules)
Look for business rules (calculations, state machines, conditionals)
Look for integration constraints (API limits, timeouts, rate limits)
Look for security rules (auth checks, input sanitization)

For each rule: write a clear imperative statement, cite the source file, and mark severity (critical/important/guideline). Mark all entries with

<!-- AI-DRAFT -->

. Present draft for review before writing.

Usage Tips

Replace placeholders like
```
[FEATURE]
```
,
```
[ISSUE]
```
,
```
[COMPONENT]
```
,
```
[TOPIC]
```
with your specific context
Customize these prompts to match your team's vocabulary and workflow
Run
```
cortex-tms prompt --list
```
to see all available prompts

Cortex TMS: AI Prompt Library

This file contains project-aware prompt templates for streamlined AI collaboration. Use

cortex-tms prompt <name>

to quickly access these prompts.

init-session

Review

NEXT-TASKS.md

docs/core/ARCHITECTURE.md

, and

CLAUDE.md

. Summarize current priorities and propose a step-by-step plan for the next task.

feature

Implement

[FEATURE]

. First, check

ARCHITECTURE.md

for constraints, follow

PATTERNS.md

for style, and validate logic against

DOMAIN-LOGIC.md

debug

I'm seeing

[ISSUE]

. Check

docs/core/TROUBLESHOOTING.md

for known issues, then trace the logic through

ARCHITECTURE.md

to find the failure point.

review

Review the current changes against

PATTERNS.md

. Flag any violations of project conventions and suggest specific fixes.

refactor

Refactor

[COMPONENT]

. Improve structure per

PATTERNS.md

while ensuring behavior remains strictly compliant with

DOMAIN-LOGIC.md

decision

We need to decide on

[TOPIC]

. Draft a new Architecture Decision Record in

docs/core/DECISIONS.md

using the project's standard format.

finish

Task complete. Execute the Maintenance Protocol:

Run
```
pnpm run docs:sync
```
to ensure version integrity across documentation
Update
```
NEXT-TASKS.md
```
with task status and next priorities

Run

node bin/cortex-tms.js validate --strict

to verify project health

Follow Git Protocol: commit with conventional format and co-authorship
Suggest next priority from backlog

bootstrap

You have just been added to a project that uses Cortex TMS for AI governance. The documentation files exist but contain placeholder content that needs to be populated with real project information.

Your task: Analyze this codebase and populate the TMS documentation files as DRAFTS for human review.

Step 1 - Understand the project:

Read
```
package.json
```
(or equivalent) for tech stack and dependencies
Scan the directory structure to understand components
Read entry points (
```
src/index.*
```
,
```
src/app.*
```
,
```
bin/*
```
)
Read any existing README.md for project context

Step 2 - Populate ARCHITECTURE.md (

docs/core/ARCHITECTURE.md

Fill "Quick Context" with what the project does, who it's for, key constraint
Fill "System Overview" with 2-3 sentences about the system
Fill "Component Map" table with real components from the codebase
Fill "Core Data Flow" with the actual happy path

Mark all populated sections with:

<!-- AI-DRAFT: Review before treating as canonical -->

Step 3 - Customize CLAUDE.md:

Update CLI commands with the project's actual commands (from package.json scripts)
Add project-specific operational loop steps
Add any project-specific rules or conventions you observed

Step 4 - Customize copilot-instructions.md (

.github/copilot-instructions.md

Fill in the actual tech stack
Add real conventions observed in the code
Add project-specific prohibitions

Step 5 - Populate NEXT-TASKS.md:

If there are open TODOs, issues, or incomplete features, suggest initial tasks
Otherwise, suggest "Review AI-generated documentation" as first task

Rules:

Be SPECIFIC to this project - no generic advice
Reference actual file paths from the codebase
Mark every generated section with
```

```
comment
Ask the user to confirm before writing each file
If unsure about something, ASK rather than guess
Do NOT open or send content from
```
.env*
```
,
```
*.pem
```
,
```
id_rsa*
```
, or similar secret files
Skip
```
node_modules/
```
,
```
dist/
```
,
```
build/
```
,
```
coverage/
```
,
```
.git/
```

populate-architecture

Analyze this codebase and populate

docs/core/ARCHITECTURE.md

Read
```
package.json
```
for tech stack, then scan
```
src/
```
directory structure
Fill "Quick Context": what it does, who it's for, key constraint
Fill "Component Map": real components with responsibilities and tech stack
Fill "Core Data Flow": trace the main happy path from entry point to output
Fill "Deployment & Infrastructure" from config files and CI workflows

Be specific to THIS project. Reference actual file paths. Mark sections with

<!-- AI-DRAFT: Review before treating as canonical -->

. Present the draft and ask for confirmation before writing.

discover-patterns

Review this codebase and document patterns for

docs/core/PATTERNS.md

Scan representative files in
```
src/
```
for recurring conventions
Identify: naming conventions, error handling, component structure, testing patterns
For each pattern, find a canonical example (actual file path + code snippet)
Document as "Pattern N: Name" with Rule, Anti-Pattern, and Canonical Example

Extract ACTUAL patterns from code, not generic best practices. Mark all entries with

<!-- AI-DRAFT -->

. Present draft for review before writing.

extract-rules

Extract domain-specific rules for

docs/core/DOMAIN-LOGIC.md

Look for validation logic (schemas, validators, form rules)
Look for business rules (calculations, state machines, conditionals)
Look for integration constraints (API limits, timeouts, rate limits)
Look for security rules (auth checks, input sanitization)

For each rule: write a clear imperative statement, cite the source file, and mark severity (critical/important/guideline). Mark all entries with

<!-- AI-DRAFT -->

. Present draft for review before writing.

Usage Tips

Replace placeholders like
```
[FEATURE]
```
,
```
[ISSUE]
```
,
```
[COMPONENT]
```
,
```
[TOPIC]
```
with your specific context
Customize these prompts to match your team's vocabulary and workflow
Run
```
cortex-tms prompt --list
```
to see all available prompts

Cortex TMS: AI Prompt Library

Cortex TMS: AI Prompt Library

init-session

feature

debug

review

refactor

decision

finish

bootstrap

populate-architecture

discover-patterns

extract-rules

Usage Tips

Related Skills

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Cortex TMS: AI Prompt Library

init-session

feature

debug

review

refactor

decision

finish

bootstrap

populate-architecture

discover-patterns

extract-rules

Usage Tips