Skill Learn

Learn Skill (Direct Execution)

Direct execution skill for scanning files, presenting findings interactively, and creating user-selected tasks. Replaces the previous delegation-based approach with synchronous execution and AskUserQuestion prompts.

Key behavior: Users always see tag scan results BEFORE any tasks are created. Users select which task types to create via interactive prompts.

Context References

Reference (do not load eagerly):

• Path: @specs/TODO.md - Current task list
• Path: @specs/state.json - Machine state

Execution

Step 1: Parse Arguments

Extract paths from command input:

# Parse from command input
paths="$ARGUMENTS"

# Default to project root if no paths specified
if [ -z "$paths" ]; then
  paths="."
fi

Note: The --dry-run flag is no longer supported. The interactive flow is inherently "preview first" - users always see findings before any tasks are created.

Step 2: Generate Session ID

Generate session ID for tracking:

session_id="sess_$(date +%s)_$(od -An -N3 -tx1 /dev/urandom | tr -d ' ')"

Step 3: Execute Tag Extraction

Scan for tags using file-type-specific patterns. Use Bash with grep for consistent output parsing.

3.1: Extract FIX: Tags

Lua files (Neovim config):

grep -rn --include="*.lua" "-- FIX:" $paths 2>/dev/null || true

LaTeX files:

grep -rn --include="*.tex" "% FIX:" $paths 2>/dev/null || true

Markdown files:

grep -rn --include="*.md" "<!-- FIX:" $paths 2>/dev/null || true

Python/Shell/YAML files:

grep -rn --include="*.py" --include="*.sh" --include="*.yaml" --include="*.yml" "# FIX:" $paths 2>/dev/null || true

3.2: Extract NOTE: Tags

Same patterns as above, replacing FIX: with NOTE:.

3.3: Extract TODO: Tags

Same patterns as above, replacing FIX: with TODO:.

3.4: Extract QUESTION: Tags

Lua files (Neovim config):

grep -rn --include="*.lua" "-- QUESTION:" $paths 2>/dev/null || true

LaTeX files:

grep -rn --include="*.tex" "% QUESTION:" $paths 2>/dev/null || true

Markdown files:

grep -rn --include="*.md" "<!-- QUESTION:" $paths 2>/dev/null || true

Python/Shell/YAML files:

grep -rn --include="*.py" --include="*.sh" --include="*.yaml" --include="*.yml" "# QUESTION:" $paths 2>/dev/null || true

3.5: Parse Results

For each grep match, extract:

• File path
• Line number
• Tag type (FIX, NOTE, TODO, QUESTION)
• Tag content (text after the tag)

Example raw output:

nvim/lua/plugins/telescope.lua:67:-- TODO: Add custom picker for git worktrees
docs/KEYMAPS.md:89:<!-- FIX: Update keymap table with new bindings -->
nvim/lua/config/lsp.lua:45:-- QUESTION: What is the best way to configure LSP hover windows?

Categorize into four arrays:

• fix_tags[] - All FIX: tags
• note_tags[] - All NOTE: tags
• todo_tags[] - All TODO: tags
• question_tags[] - All QUESTION: tags

Step 4: Display Tag Summary

Present findings to user BEFORE any selection:

## Tag Scan Results

**Files Scanned**: {paths}
**Tags Found**: {total_count}

### FIX: Tags ({count})
- `{file}:{line}` - {content}
- ...

### NOTE: Tags ({count})
- `{file}:{line}` - {content}
- ...

### TODO: Tags ({count})
- `{file}:{line}` - {content}
- ...

### QUESTION: Tags ({count})
- `{file}:{line}` - {content}
- ...

Step 5: Handle Edge Cases

No Tags Found

If no tags found:

## No Tags Found

Scanned files in: {paths}
No FIX:, NOTE:, TODO:, or QUESTION: tags detected.

Nothing to create.

Exit gracefully without prompts.

Only Certain Tag Types

Only show task type options for tag types that exist:

• FIX: tags exist -> offer "fix-it task"
• NOTE: tags exist -> offer "fix-it task" AND "learn-it task"
• TODO: tags exist -> offer "TODO tasks"
• QUESTION: tags exist -> offer "Research tasks"

Step 6: Task Type Selection

If tags were found, prompt user to select task types:

{
  "question": "Which task types should be created?",
  "header": "Task Types",
  "multiSelect": true,
  "options": [
    {
      "label": "fix-it task",
      "description": "Combine {N} FIX:/NOTE: tags into single task"
    },
    {
      "label": "learn-it task",
      "description": "Update context from {N} NOTE: tags"
    },
    {
      "label": "TODO tasks",
      "description": "Create tasks for {N} TODO: items"
    },
    {
      "label": "Research tasks",
      "description": "Create research tasks for {N} QUESTION: items"
    }
  ]
}

Important: Only include options where the tag type exists:

• Include "fix-it task" only if FIX: or NOTE: tags exist
• Include "learn-it task" only if NOTE: tags exist
• Include "TODO tasks" only if TODO: tags exist
• Include "Research tasks" only if QUESTION: tags exist

If user selects nothing, exit gracefully:

No task types selected. No tasks created.

Step 7: Individual TODO Selection

If "TODO tasks" was selected AND there are TODO: tags:

Standard Case (<=20 TODOs)

{
  "question": "Select TODO items to create as tasks:",
  "header": "TODO Selection",
  "multiSelect": true,
  "options": [
    {
      "label": "{content truncated to 50 chars}",
      "description": "{file}:{line}"
    },
    ...
  ]
}

Large Number of TODOs (>20)

Add a "Select all" option at the top:

{
  "question": "Select TODO items to create as tasks:",
  "header": "TODO Selection (many items)",
  "multiSelect": true,
  "options": [
    {
      "label": "Select all ({N} items)",
      "description": "Create a task for every TODO tag"
    },
    {
      "label": "{content truncated to 50 chars}",
      "description": "{file}:{line}"
    },
    ...
  ]
}

If "Select all" is chosen, include all TODOs. Otherwise, only selected items.

Step 7.5: Topic Grouping for TODO Items

Condition: User selected "TODO tasks" AND selected more than 1 TODO item

If only 1 TODO item was selected, skip to Step 8 (no grouping benefit).

7.5.1: Extract Topic Indicators

For each selected TODO item, extract topic indicators:

Key Terms: Extract significant words from the TODO content (nouns, verbs). Ignore stop words (the, a, is, to, for, etc.).

File Section: Group by file path prefix (e.g., Logos/Layer1/ vs Logos/Shared/).

Action Type: Identify common action patterns:

• "Add/Implement/Create" → implementation tasks
• "Fix/Handle/Correct" → fix tasks
• "Document/Update docs" → documentation tasks
• "Test/Verify" → testing tasks
• "Refactor/Optimize" → improvement tasks

Example extraction:

TODO: "Add custom picker for worktrees" at nvim/lua/plugins/telescope.lua:67
  → key_terms: ["picker", "worktrees", "telescope"]
  → file_section: "nvim/lua/plugins/"
  → action_type: "implementation"

TODO: "Add preview window for worktrees" at nvim/lua/plugins/telescope.lua:89
  → key_terms: ["preview", "worktrees", "telescope"]
  → file_section: "nvim/lua/plugins/"
  → action_type: "implementation"

TODO: "Optimize lazy loading" at nvim/lua/config/lazy.lua:23
  → key_terms: ["optimize", "lazy", "loading"]
  → file_section: "nvim/lua/config/"
  → action_type: "improvement"

7.5.2: Cluster TODOs by Shared Terms

Group TODOs that share 2 or more significant terms or share file section + action type.

Clustering algorithm:

1. Start with first TODO as initial group
2. For each remaining TODO:
   • If shares 2+ key terms with existing group → add to group
   • If shares file_section AND action_type with existing group → add to group
   • Otherwise → start new group
3. Generate topic label from most common shared terms in group

Example clustering:

Group 1: "Telescope Worktrees" (shared: worktrees, telescope, nvim/lua/plugins/, implementation)
  - Add custom picker for worktrees
  - Add preview window for worktrees

Group 2: "Config Optimization" (shared: nvim/lua/config/, improvement)
  - Optimize lazy loading

Single-item groups: If a TODO doesn't cluster with others, it becomes its own single-item group.

7.5.3: Store Grouped Topics

Store the topic groups for use in Step 7.5.4:

topic_groups = [
  {
    label: "Telescope Worktrees",
    items: [
      {file: "nvim/lua/plugins/telescope.lua", line: 67, content: "Add custom picker for worktrees"},
      {file: "nvim/lua/plugins/telescope.lua", line: 89, content: "Add preview window for worktrees"}
    ],
    shared_terms: ["worktrees", "telescope"],
    action_type: "implementation"
  },
  {
    label: "Config Optimization",
    items: [
      {file: "nvim/lua/config/lazy.lua", line: 23, content: "Optimize lazy loading"}
    ],
    shared_terms: [],
    action_type: "improvement"
  }
]

Step 7.5.4: Topic Group Confirmation

Condition: topic_groups contains at least one group with 2+ items

If all groups have only 1 item, skip to Step 8 (no grouping benefit).

Present topic groups via AskUserQuestion:

{
  "question": "How should TODO items be grouped into tasks?",
  "header": "TODO Topic Grouping",
  "multiSelect": false,
  "options": [
    {
      "label": "Accept suggested topic groups",
      "description": "Creates {N} grouped tasks: {group_summaries}"
    },
    {
      "label": "Keep as separate tasks",
      "description": "Creates {M} individual tasks (one per TODO item)"
    },
    {
      "label": "Create single combined task",
      "description": "Creates 1 task containing all {M} TODO items"
    }
  ]
}

Where:

• {N} = number of topic groups
• {M} = total number of selected TODO items
• {group_summaries} = comma-separated list like "S5 Theorems (2 items), Utility Optimization (1 item)"

Store user choice: grouping_mode = "grouped" | "separate" | "combined"

Step 7.6: Individual QUESTION Selection

Condition: User selected "Research tasks" in Step 6 AND QUESTION: tags exist

If "Research tasks" was selected AND there are QUESTION: tags:

Standard Case (<=20 QUESTIONs)

{
  "question": "Select QUESTION items to create as research tasks:",
  "header": "QUESTION Selection",
  "multiSelect": true,
  "options": [
    {
      "label": "{content truncated to 50 chars}",
      "description": "{file}:{line}"
    },
    ...
  ]
}

Large Number of QUESTIONs (>20)

Add a "Select all" option at the top:

{
  "question": "Select QUESTION items to create as research tasks:",
  "header": "QUESTION Selection (many items)",
  "multiSelect": true,
  "options": [
    {
      "label": "Select all ({N} items)",
      "description": "Create a research task for every QUESTION tag"
    },
    {
      "label": "{content truncated to 50 chars}",
      "description": "{file}:{line}"
    },
    ...
  ]
}

If "Select all" is chosen, include all QUESTIONs. Otherwise, only selected items.

Step 7.7: Topic Grouping for QUESTION Items

Condition: User selected "Research tasks" AND selected more than 1 QUESTION item

If only 1 QUESTION item was selected, skip to Step 8 (no grouping benefit).

7.7.1: Extract Topic Indicators

For each selected QUESTION item, extract topic indicators. Use the same algorithm as Step 7.5.1 (TODO topic extraction):

Key Terms: Extract significant words from the QUESTION content (nouns, verbs). Ignore stop words (the, a, is, to, for, etc.).

File Section: Group by file path prefix (e.g., nvim/lua/plugins/ vs nvim/lua/config/).

Action Type: For QUESTION tags, action_type defaults to "research" for all items (since all are questions to be researched).

Example extraction:

QUESTION: "What is the best way to configure LSP hover windows?" at nvim/lua/config/lsp.lua:45
  → key_terms: ["configure", "LSP", "hover", "windows"]
  → file_section: "nvim/lua/config/"
  → action_type: "research"

QUESTION: "How do I add custom LSP handlers?" at nvim/lua/config/lsp.lua:89
  → key_terms: ["custom", "LSP", "handlers"]
  → file_section: "nvim/lua/config/"
  → action_type: "research"

QUESTION: "What telescope extensions are available for git worktrees?" at nvim/lua/plugins/telescope.lua:23
  → key_terms: ["telescope", "extensions", "git", "worktrees"]
  → file_section: "nvim/lua/plugins/"
  → action_type: "research"

7.7.2: Cluster QUESTIONs by Shared Terms

Use the same clustering algorithm as Step 7.5.2 (TODO clustering).

Group QUESTIONs that share 2 or more significant terms or share file section (action_type is always "research" for questions, so only file_section matters for secondary matching).

Example clustering:

Group 1: "LSP Configuration" (shared: LSP, nvim/lua/config/)
  - What is the best way to configure LSP hover windows?
  - How do I add custom LSP handlers?

Group 2: "Telescope Extensions" (shared: nvim/lua/plugins/)
  - What telescope extensions are available for git worktrees?

Single-item groups: If a QUESTION doesn't cluster with others, it becomes its own single-item group.

7.7.3: Store Grouped Topics

Store the topic groups for use in Step 7.7.4:

question_topic_groups = [
  {
    label: "LSP Configuration",
    items: [
      {file: "nvim/lua/config/lsp.lua", line: 45, content: "What is the best way to configure LSP hover windows?"},
      {file: "nvim/lua/config/lsp.lua", line: 89, content: "How do I add custom LSP handlers?"}
    ],
    shared_terms: ["LSP"],
    action_type: "research"
  },
  {
    label: "Telescope Extensions",
    items: [
      {file: "nvim/lua/plugins/telescope.lua", line: 23, content: "What telescope extensions are available for git worktrees?"}
    ],
    shared_terms: [],
    action_type: "research"
  }
]

Step 7.7.4: QUESTION Topic Group Confirmation

Condition: question_topic_groups contains at least one group with 2+ items

If all groups have only 1 item, skip to Step 8 (no grouping benefit).

Present topic groups via AskUserQuestion:

{
  "question": "How should QUESTION items be grouped into research tasks?",
  "header": "QUESTION Topic Grouping",
  "multiSelect": false,
  "options": [
    {
      "label": "Accept suggested topic groups",
      "description": "Creates {N} grouped research tasks: {group_summaries}"
    },
    {
      "label": "Keep as separate tasks",
      "description": "Creates {M} individual research tasks (one per QUESTION item)"
    },
    {
      "label": "Create single combined task",
      "description": "Creates 1 research task containing all {M} QUESTION items"
    }
  ]
}

Where:

• {N} = number of topic groups
• {M} = total number of selected QUESTION items
• {group_summaries} = comma-separated list like "LSP Configuration (2 items), Telescope Extensions (1 item)"

Store user choice: question_grouping_mode = "grouped" | "separate" | "combined"

Step 8: Create Selected Tasks

For each selected task type, create the task. Important: When NOTE: tags exist and both fix-it and learn-it tasks are selected, create learn-it FIRST so fix-it can depend on it.

8.1: Get Next Task Number

next_num=$(jq -r '.next_project_number' specs/state.json)

8.2: Dependency-Aware Task Creation Order

Check for NOTE: dependency condition:

has_note_dependency = (NOTE: tags exist) AND (user selected both "fix-it task" AND "learn-it task")

If has_note_dependency is TRUE:

• Create learn-it task FIRST (Step 8.2a)
• Store learn-it task number as learn_it_task_num
• Create fix-it task SECOND with dependency (Step 8.2b)

If has_note_dependency is FALSE:

• Create fix-it task first (if selected)
• Create learn-it task second (if selected)
• No dependency relationship

8.2a: Learn-It Task (when created first for dependency)

Condition: has_note_dependency is TRUE

{
  "title": "Update context files from NOTE: tags",
  "description": "Update {N} context files based on learnings:\n\n{grouped by target context}",
  "language": "meta",
  "effort": "1-2 hours"
}

Store the task number: learn_it_task_num = next_num Increment: next_num = next_num + 1

8.2b: Fix-It Task (with dependency when has_note_dependency)

Condition: User selected "fix-it task" AND (FIX: or NOTE: tags exist)

When has_note_dependency is TRUE:

{
  "title": "Fix issues from FIX:/NOTE: tags",
  "description": "Address {N} items from embedded tags:\n\n{list of items with file:line references}\n\n**Important**: When making changes, remove the FIX: and NOTE: tags from the source files. Leave TODO: tags untouched (they create separate tasks).",
  "language": "{predominant language from source files}",
  "effort": "2-4 hours",
  "dependencies": [learn_it_task_num]
}

When has_note_dependency is FALSE:

{
  "title": "Fix issues from FIX:/NOTE: tags",
  "description": "Address {N} items from embedded tags:\n\n{list of items with file:line references}\n\n**Important**: When making changes, remove the FIX: and NOTE: tags from the source files. Leave TODO: tags untouched (they create separate tasks).",
  "language": "{predominant language from source files}",
  "effort": "2-4 hours"
}

Language Detection:

if majority of tags from .lean files -> "lean"
elif majority from .tex files -> "latex"
elif majority from .opencode/ files -> "meta"
else -> "general"

8.3: Learn-It Task (when created without dependency)

Condition: User selected "learn-it task" AND NOTE: tags exist AND has_note_dependency is FALSE

{
  "title": "Update context files from NOTE: tags",
  "description": "Update {N} context files based on learnings:\n\n{grouped by target context}",
  "language": "meta",
  "effort": "1-2 hours"
}

8.4: Todo-Tasks (if selected)

Condition: User selected "TODO tasks" AND user selected specific TODO items

Check grouping_mode (from Step 7.5.4, defaults to "separate" if Step 7.5.4 was skipped):

8.4.1: Grouped Mode (grouping_mode == "grouped")

For each topic group in topic_groups:

{
  "title": "{topic_label}: {item_count} TODO items",
  "description": "Address TODO items related to {topic_label}:\n\n{item_list}\n\n---\n\nShared context: {shared_terms_description}",
  "language": "{detected from majority file type in group}",
  "effort": "{scaled_effort}"
}

Where:

• {topic_label} = generated label (e.g., "Telescope Worktrees")
• {item_count} = number of items in group
• {item_list} = formatted list of items:

  - [ ] {content} (`{file}:{line}`)
  - [ ] {content} (`{file}:{line}`)
  

• {shared_terms_description} = brief description of why items are grouped (e.g., "Related to telescope worktree functionality")

Effort Scaling Formula:

base_effort = 1 hour
scaled_effort = base_effort + (30 min * (item_count - 1))

Examples:
  1 item  → 1 hour
  2 items → 1.5 hours (1h + 30min)
  3 items → 2 hours (1h + 60min)
  4 items → 2.5 hours (1h + 90min)

8.4.2: Combined Mode (grouping_mode == "combined")

Create single task containing all selected TODO items:

{
  "title": "Address {item_count} TODO items",
  "description": "Combined TODO items from scan:\n\n{all_items_list}\n\n---\n\nFiles: {unique_files_list}",
  "language": "{detected from majority file type}",
  "effort": "{scaled_effort}"
}

Where:

• {item_count} = total number of selected TODO items
• {all_items_list} = formatted list of all items with checkboxes
• {unique_files_list} = comma-separated list of unique files involved

Effort Scaling: Same formula as grouped mode.

8.4.3: Separate Mode (grouping_mode == "separate" or default)

For each selected TODO item individually:

{
  "title": "{tag content, truncated to 60 chars}",
  "description": "{full tag content}\n\nSource: {file}:{line}",
  "language": "{detected from file type}",
  "effort": "1 hour"
}

Language Detection for Todo-Task (all modes):

.lua (nvim/) -> "neovim"
.tex  -> "latex"
.md   -> "markdown"
.py/.sh -> "general"
.opencode/* -> "meta"

8.5: Research-Tasks (if selected)

Condition: User selected "Research tasks" AND user selected specific QUESTION items

Check question_grouping_mode (from Step 7.7.4, defaults to "separate" if Step 7.7.4 was skipped):

8.5.1: Content-Based Language Detection for Research Tasks

IMPORTANT: Research task language is detected from the content of the question, NOT the source file type. This ensures questions are routed to the appropriate research agent based on what is being asked.

Keyword-to-Language Mapping:

neovim_keywords = ["nvim", "neovim", "plugin", "lazy", "telescope", "treesitter", "lsp", "buffer", "window", "keymap", "autocmd", "filetype", "lua"]
latex_keywords = ["theorem", "proof", "lemma", "axiom", "logic", "formula", "derivation", "proposition", "corollary", "latex", "tex"]
meta_keywords = [".claude", "command", "agent", "skill", "workflow", "state.json", "TODO.md", "specs/"]

function detect_research_language(question_content):
    content_lower = question_content.lower()

    # Check for neovim keywords
    for keyword in neovim_keywords:
        if keyword in content_lower:
            return "neovim"

    # Check for latex keywords
    for keyword in latex_keywords:
        if keyword in content_lower:
            return "latex"

    # Check for meta keywords
    for keyword in meta_keywords:
        if keyword in content_lower:
            return "meta"

    # Default to general for all other cases
    return "general"

Examples:

• "What is the best way to configure LSP hover windows?" → neovim (contains "LSP")
• "How do I prove this theorem about completeness?" → latex (contains "theorem")
• "What is the difference between a skill and an agent?" → meta (contains "skill", "agent")
• "What are the best practices for API design?" → general (no matching keywords)

8.5.2: Grouped Mode (question_grouping_mode == "grouped")

For each topic group in question_topic_groups:

{
  "title": "{topic_label}: {item_count} research questions",
  "description": "Research questions related to {topic_label}:\n\n{question_list}\n\n---\n\nShared context: {shared_terms_description}",
  "language": "{detected from majority question content in group}",
  "effort": "{scaled_effort}"
}

Where:

• {topic_label} = generated label (e.g., "LSP Configuration")
• {item_count} = number of items in group
• {question_list} = formatted list of questions using blockquote syntax:

  > {question text}
  > Source: `{file}:{line}`
  
  > {question text}
  > Source: `{file}:{line}`
  

• {shared_terms_description} = brief description of why questions are grouped

Effort Scaling Formula (research tasks use slightly higher base):

base_effort = 1.5 hours (research requires more exploration)
scaled_effort = base_effort + (30 min * (item_count - 1))

Examples:
  1 item  → 1-2 hours
  2 items → 2 hours (1.5h + 30min)
  3 items → 2.5 hours (1.5h + 60min)
  4 items → 3 hours (1.5h + 90min)

Language Detection for Grouped Mode: Analyze all question content in the group, use the most frequently detected language. If tie, default to "general".

8.5.3: Combined Mode (question_grouping_mode == "combined")

Create single task containing all selected QUESTION items:

{
  "title": "Research: {item_count} questions",
  "description": "Research questions from scan:\n\n{all_questions_list}\n\n---\n\nFiles: {unique_files_list}",
  "language": "{detected from majority question content}",
  "effort": "{scaled_effort}"
}

Where:

• {item_count} = total number of selected QUESTION items
• {all_questions_list} = formatted list of all questions with blockquotes
• {unique_files_list} = comma-separated list of unique source files

Effort Scaling: Same formula as grouped mode.

8.5.4: Separate Mode (question_grouping_mode == "separate" or default)

For each selected QUESTION item individually:

{
  "title": "Research: {question content, truncated to 60 chars}",
  "description": "> {full question text}\n\nSource: `{file}:{line}`",
  "language": "{detected from question content}",
  "effort": "1-2 hours"
}

Language Detection: Apply content-based detection (Step 8.5.1) to the individual question.

Step 9: Update State Files

For each task created:

9.1: Update state.json

Read current state, add new task entry, increment next_project_number:

# Create slug from title
slug=$(echo "$title" | tr '[:upper:]' '[:lower:]' | tr ' ' '_' | tr -cd 'a-z0-9_' | cut -c1-50)

# Read current state
current=$(cat specs/state.json)

# Add task using jq (use two-step pattern to avoid escaping issues)
# Step 1: Write task data to temp file
# Step 2: Use jq with slurpfile

For fix-it task when has_note_dependency is TRUE, include dependencies array:

{
  "project_number": {N},
  "project_name": "{slug}",
  "status": "not_started",
  "language": "{language}",
  "dependencies": [learn_it_task_num]
}

For all other tasks, no dependencies field needed.

9.2: Update TODO.md

CRITICAL — Batch Insertion: Learn may create multiple tasks (learn-it, fix-it, TODO, research). Build a single batch_markdown string by joining all task entries with \n\n, then use ONE Edit tool call:

oldString: "## Tasks\n"
newString: "## Tasks\n\n{batch_markdown}\n"

WARNING: DO NOT search for the last --- separator and append text after it. DO NOT insert at the bottom of the file. DO NOT prepend each task individually — individual prepending reverses task order (last task becomes first). ALWAYS use the heading-anchored Edit tool pattern with oldString: "## Tasks\n". The heading ## Tasks is unique in TODO.md and is the only reliable insertion anchor.

After inserting, re-read the first few lines after ## Tasks:

• Confirm the first task after ## Tasks has the expected task number
• If it doesn't match, the insertion went wrong — fix and re-verify

Task entry format (no dependency):

### {N}. {Title}
- **Effort**: {estimate}
- **Status**: [NOT STARTED]
- **Language**: {language}
- **Started**: {timestamp}

**Description**: {description}

---

Task entry format when has_note_dependency is TRUE:

### {N}. {Title}
- **Effort**: {estimate}
- **Status**: [NOT STARTED]
- **Language**: {language}
- **Dependencies**: {learn_it_task_num}
- **Started**: {timestamp}

**Description**: {description}

---

Step 10: Display Results

Show summary of created tasks:

## Tasks Created from Tags

**Tags Processed**: {N} across scanned files

### Created Tasks

| # | Type | Title | Language |
|---|------|-------|----------|
| {N} | fix-it | Fix issues from FIX:/NOTE: tags | {lang} |
| {N+1} | learn-it | Update context files from NOTE: tags | meta |
| {N+2} | todo | {title} | {lang} |
| {N+3} | research | Research: {question title} | {lang} |

---

**Next Steps**:
1. Review tasks in TODO.md
2. Run `/research {first_task}` to begin
3. Progress through /research -> /plan -> /implement cycle

Step 11: Git Commit (Postflight)

If tasks were created, commit changes:

task_count={number of tasks created}
git add specs/TODO.md specs/state.json
git commit -m "learn: create $task_count tasks from tags

Session: $session_id

Error Handling

Path Access Errors

When paths don't exist or can't be accessed:

1. Log warning for each invalid path
2. Continue with valid paths
3. If no valid paths remain, report and exit

No Tags Found

This is NOT an error condition:

• Report informatively
• Exit without prompts

state.json Update Failure

If jq fails:

1. Log error with command and output
2. Try two-step jq pattern
3. If still failing, report partial success (tags found but tasks not created)

TODO.md Parse Error

If TODO.md format is corrupted:

1. Log error
2. Skip TODO.md update
3. State.json update may still succeed
4. Report partial success

Git Commit Failure

Non-blocking:

1. Log the failure
2. Tasks are still created successfully
3. Report that commit failed but tasks exist

Standards Reference

This skill implements the multi-task creation pattern. See .opencode/docs/reference/standards/multi-task-creation-standard.md for the complete standard.

Compliance Level: Full (all required components)

Limitation: External dependencies (TODO tasks depending on existing tasks) not implemented. Consider as future enhancement.