Agent skill

Nav Task

by qf-studio in qf-studio/navigator

Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index.

MITAuto-check: notesAgent Workflows

Install Nav Task

skills CLI
$ npx skills add qf-studio/navigator --skill nav-task -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install qf-studio/navigator nav-task --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nav-task .claude/skills/nav-task && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nav-task
GitHub stars
355
Token cost
~3.9k tokens
SKILL.md length
1,031 words
Files
6
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index.

  • Works in 7 steps: Determine Task ID → Determine Action (Create vs Archive) → 5: Recall Prior Knowledge (CREATE flow… → …
  • User starts new feature
  • SKILL.md covers When to Invoke, Execution Steps, Done and Refs, plus 12 more sections
  • Runs Python scripts from its folder; calls python3, npm and gh

What it does

Nav Task is an agent skill from qf-studio/navigator. Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index. Use when user starts new feature, completes work, or says "document this feature".

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `functions/index_updater.py`, `functions/task_formatter.py` and `functions/task_id_generator.py`).

It sits in Agent Workflows, covering Planning. It works with GitHub. The repository describes itself as: Finish What You Start — Context engineering for Claude Code. Sessions last 20+ exchanges instead of crashing at 7. The licence is MIT.

When your agent uses it

  • User starts new feature
  • Says document this feature

Example prompts

  • “document this feature”
  • “/nav-task”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Determine Task ID
  2. Determine Action (Create vs Archive)
  3. 5: Recall Prior Knowledge (CREATE flow only, v6.17.0+)
  4. {Next phase}
  5. 5: Sync to Knowledge Graph (v6.0.0+)
  6. Update PM Tool (If Configured)
  7. Confirm Success

What it can do on your machine

Read from SKILL.md and the folder at commit 3bb9eac. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • npm
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nav Task loads about 3.9k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 1,031 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from qf-studio/navigator at commit 3bb9eac, republished under its MIT licence (© qf-studio). 1,031 words, ~3,943 tokens.

Download SKILL.mdSave it as .claude/skills/nav-task/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
nav-task
description
Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index. Use when user starts new feature, completes work, or says "document this feature".
allowed-tools
Read, Write, Edit, Bash
version
1.0.0

Navigator Task Manager Skill

Create and manage task documentation - implementation plans that capture what was built, how, and why.

When to Invoke

Invoke this skill when the user:

  • Says "document this feature", "archive this task"
  • Says "create task doc for...", "document what I built"
  • Completes a feature and mentions "done", "finished", "complete"
  • Starts new feature and says "create implementation plan"

DO NOT invoke if:

  • User is asking about existing tasks (use Read, not creation)
  • Creating SOPs (that's nav-sop skill)
  • Updating system docs (different skill)

Execution Steps

Step 1: Determine Task ID

If user provided task ID (e.g., "TASK-01", "GH-123"):

  • Use their ID directly

If no ID provided, run the generator — it reads task_id_source from .agent/.nav-config.json (default local):

bash
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$(cat "${NAVIGATOR_CONFIG_HOME:-${XDG_CONFIG_HOME:-$HOME/.config}/navigator}/plugin-root" 2>/dev/null)}"
[ -d "$PLUGIN_DIR/skills" ] || PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/navigator-marketplace"
python3 "$PLUGIN_DIR/skills/nav-task/functions/task_id_generator.py" \
  --title "{feature name}" --body "{one-line summary}" --json
  • task_id_source: local (solo repos) — next sequential {task_prefix}-NN from .agent/tasks/ (last is TASK-05 → TASK-06). --title is ignored.
  • task_id_source: github (team repos, GH-32) — creates the GitHub issue first via gh issue create and returns GH-<n>; the doc is .agent/tasks/GH-<n>-{slug}.md. Issue numbers are allocated by GitHub, so two contributors branching at once can never mint the same ID, and Pilot already addresses tasks as GH-<n>. Add --label pilot (or any label) when the issue should be picked up. A failing gh (no auth, no network) is an error — do not fall back to a local number; tell the user and stop.
  • Existing TASK-NN docs keep working either way; the index, graph sync and session-start listing accept any PREFIX-<n> filename.
  • In the doc template below, TASK-{XX} stands for whichever ID came back.
Step 2: Determine Action (Create vs Archive)

Creating new task (starting feature):

User: "Create task doc for OAuth implementation"
→ Action: CREATE
→ Generate empty implementation plan template

Archiving completed task (feature done):

User: "Document this OAuth feature I just built"
→ Action: ARCHIVE
→ Generate implementation plan from conversation
Step 2.5: Recall Prior Knowledge (CREATE flow only, v6.17.0+)

Before writing the plan, query the knowledge graph for memories relevant to this feature — pitfalls and patterns the project has already paid for:

bash
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$(cat "${NAVIGATOR_CONFIG_HOME:-${XDG_CONFIG_HOME:-$HOME/.config}/navigator}/plugin-root" 2>/dev/null)}"
[ -d "$PLUGIN_DIR/skills" ] || PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/navigator-marketplace"

if [ -f ".agent/knowledge/graph.json" ]; then
  python3 "$PLUGIN_DIR/skills/nav-graph/functions/memory_recall.py" \
    --concepts "{comma-separated concepts inferred from the feature description}" \
    --format markdown --limit 5
fi
  • Infer concepts from the feature description using the same keyword families as task_to_graph.extract_concepts_from_task (auth, database, api, frontend, testing, …) — alias resolution absorbs abbreviations.
  • Empty output → skip silently. Do not add the section; do not mention the absence.
  • Non-empty output → include it as the ## Known Pitfalls & Patterns section in the template below, and reflect each recalled pitfall in the Implementation phases (a recalled pitfall that doesn't change the plan wasn't really factored in).
Step 3A: Create New Task (If Starting Feature)

Generate task document from template:

markdown
# TASK-{XX}: {Feature Name}

**Status**: 🚧 In Progress
**Created**: {YYYY-MM-DD}
**Assignee**: {from PM tool or "Manual"}

---

## Context

**Problem**:
[What problem does this solve?]

**Goal**:
[What are we building?]

---

## Known Pitfalls & Patterns

<!-- From knowledge graph (Step 2.5). OMIT this section entirely if recall
     returned nothing. Each recalled pitfall must be reflected in the
     Implementation phases below. -->

- **PITFALL** (90%, mem-XXX): [recalled summary]

---

## Acceptance Criteria

Concrete, checkable outcomes — written so anyone (human or AI) can verify them.

- [ ] [Specific, observable outcome]
- [ ] [Another outcome]
- [ ] [Edge case handled]

---

## Implementation

### Phase 1: {Name}
**Goal**: [What this phase accomplishes]

**Tasks**:
- [ ] [Specific task]
- [ ] [Another task]

**Files**:
- `path/to/file.ts` - [Purpose]

### Phase 2: {Name}
...

---

## Out of Scope

Explicit non-goals — what this task deliberately does not address.

- [What's deferred to a future task]
- [Adjacent change being avoided]

---

## Technical Decisions

| Decision | Options Considered | Chosen | Reasoning |
|----------|-------------------|--------|-----------|
| [What] | [Option A, B, C] | [Chosen] | [Why] |

---

## Verify

Run these commands to validate the implementation:

```bash
# Run tests
[test command for this feature]

# Type check
[type check command]

# Build
[build command]

Done

Observable outcomes that prove completion:

  • [Specific file/API exists and exports expected interface]
  • [Tests pass - specify count or coverage target]
  • [Build succeeds without errors]
  • [User-observable behavior works as specified]

Refs

  • [Source plan / design doc / parent issue]
  • [Related ticket]

Notes

[Any additional context, links, references]


Last Updated: {YYYY-MM-DD}


Save to: `.agent/tasks/TASK-{XX}-{slug}.md`

### Step 3B: Archive Completed Task (If Feature Done)

Generate task document from conversation:

1. **Analyze conversation** (last 30-50 messages):
   - What was built?
   - How was it implemented?
   - What decisions were made?
   - What files were modified?

2. **Generate implementation plan**:

```markdown
# TASK-{XX}: {Feature Name}

**Status**: ✅ Completed
**Created**: {YYYY-MM-DD}
**Completed**: {YYYY-MM-DD}

---

## What Was Built

[1-2 paragraph summary of the feature]

---

## Implementation

### Phase 1: {Actual phase completed}
**Completed**: {Date}

**Changes**:
- Created `src/auth/oauth.ts` - OAuth provider integration
- Modified `src/routes/auth.ts` - Added login/logout endpoints
- Updated `src/config/passport.ts` - Passport configuration

**Key Code**:
```typescript
// Example of key implementation
export const oauthLogin = async (req, res) => {
  // Implementation details
};
Phase 2: {Next phase}

...


Technical Decisions

DecisionOptionsChosenReasoning
Auth librarynext-auth, passport.js, auth0passport.jsBetter control over OAuth flow, smaller bundle
Token storagelocalStorage, cookies, sessionStoragehttpOnly cookiesXSS protection, automatic transmission
Session storememory, Redis, PostgreSQLRedisFast, scalable, separate from DB

Files Modified

  • src/auth/oauth.ts (created) - OAuth integration
  • src/routes/auth.ts (modified) - Added auth endpoints
  • src/config/passport.ts (created) - Passport setup
  • tests/auth.test.ts (created) - Auth tests
  • README.md (updated) - OAuth setup instructions

Challenges & Solutions

Challenge: OAuth callback URL mismatch

  • Problem: Redirects failed in production
  • Solution: Added environment-specific callback URLs
  • Commit: abc1234

Challenge: Session persistence across restarts

  • Problem: Users logged out on server restart
  • Solution: Redis session store
  • Commit: def5678

Testing

  • ✅ Unit tests: src/auth/*.test.ts (15 tests, 100% coverage)
  • ✅ Integration tests: OAuth flow end-to-end
  • ✅ Manual testing: Tested with Google, GitHub providers

Documentation

  • ✅ README updated with OAuth setup instructions
  • ✅ Environment variables documented in .env.example
  • ✅ API endpoints documented in docs/api.md

Verify

Commands executed to validate:

bash
# Actual commands run during verification
npm test src/auth
npm run type-check
npm run build

Results: All passed ✅


Done

Outcomes confirmed:

  • src/auth/oauth.ts exports OAuth provider integration
  • All tests pass (15 tests, 100% coverage)
  • Build succeeds without errors
  • OAuth login/logout flows work correctly

Show full SKILL.md (427 more words)Show less

SOPs Created:

  • .agent/sops/integrations/oauth-setup.md

System Docs Updated:

  • .agent/system/project-architecture.md (added auth section)

Completed: {YYYY-MM-DD} Implementation Time: {X hours/days}


Save to: `.agent/tasks/TASK-{XX}-{slug}.md`

### Step 3.5: Verify Interpretation (ToM Checkpoint - Archive Mode Only) [EXECUTE]

**IMPORTANT**: This step MUST be executed when archiving tasks (not creating new ones).

**Before committing archive documentation, confirm interpretation with user**.

**Display verification** (only for ARCHIVE action):

I extracted this from our session: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

What was built:

  • {FEATURE_SUMMARY}

Key decisions captured:

  • {DECISION_1}: {REASONING_1}
  • {DECISION_2}: {REASONING_2}

Files changed: {COUNT} total

  • {FILE_1} ({ACTION}: {PURPOSE})
  • {FILE_2} ({ACTION}: {PURPOSE})

Challenges solved:

  • {CHALLENGE_1}: {SOLUTION_1} ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Corrections needed? [Enter to proceed / type corrections]


**Always verify for ARCHIVE** because:
- Extracting from conversation is inference-based
- User may have made decisions not explicitly stated
- Some files may have been modified outside conversation
- Ensures accurate historical record

**Skip verification for CREATE** because:
- Template is mostly empty
- User fills in details themselves
- No inference risk

### Step 4: Update Navigator Index

Edit `.agent/DEVELOPMENT-README.md` to add task to index:

```markdown
## Active Tasks

- **TASK-{XX}**: {Feature Name} (Status: In Progress/Completed)
  - File: `.agent/tasks/TASK-{XX}-{slug}.md`
  - Started: {Date}
  - [Completed: {Date}]

Keep index organized (active tasks first, completed below).

Step 4.5: Sync to Knowledge Graph (v6.0.0+)

If knowledge graph exists, sync task to graph:

bash
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$(cat "${NAVIGATOR_CONFIG_HOME:-${XDG_CONFIG_HOME:-$HOME/.config}/navigator}/plugin-root" 2>/dev/null)}"
[ -d "$PLUGIN_DIR/skills" ] || PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/navigator-marketplace"
if [ -f ".agent/knowledge/graph.json" ]; then
  python3 "$PLUGIN_DIR/skills/nav-graph/functions/task_to_graph.py" \
    --action add \
    --task-path ".agent/tasks/TASK-{XX}-{slug}.md" \
    --graph-path .agent/knowledge/graph.json
fi

What this does:

  • Extracts concepts from task content (auth, api, testing, etc.)
  • Adds task node to graph with status and concepts
  • Creates implements edges from task to concepts
  • For completed tasks: Extracts Technical Decisions as decision memories

Output:

Added task: TASK-XX
Title: {Feature Name}
Status: completed
Concepts: auth, api, testing
Decisions extracted: 2

This makes the task queryable via "What do we know about auth?" and preserves architectural decisions as persistent memories.

Step 5: Update PM Tool (If Configured)

If PM tool is Linear:

typescript
create_comment({
  issueId: "TASK-XX",
  body: "📚 Implementation plan documented: .agent/tasks/TASK-XX-feature.md"
})

If PM tool is GitHub:

bash
gh issue comment {ISSUE-NUMBER} -b "📚 Implementation plan: .agent/tasks/TASK-XX-feature.md"

If PM tool is none: Skip PM update.

Step 6: Confirm Success

Show completion message:

✅ Task documentation created!

Task: TASK-{XX} - {Feature Name}
File: .agent/tasks/TASK-{XX}-{slug}.md
Size: {X} KB (~{Y} tokens)

📋 Contains:
- Implementation phases
- Technical decisions
- Files modified
- [If archived: Challenges & solutions]
- [If archived: Testing & documentation]

🔗 Navigator index updated
[If PM tool: PM tool comment added]

To reference later:
Read .agent/tasks/TASK-{XX}-{slug}.md

Task Document Template Structure

For New Tasks (Planning)
  1. Context (problem/goal)
  2. Acceptance criteria (- [ ] checkable outcomes)
  3. Implementation (phases)
  4. Out of scope (explicit non-goals)
  5. Technical decisions (to be made)
  6. Verify + Done (validation + completion)
  7. Refs (sources/related)
For Completed Tasks (Archive)
  1. What was built (summary)
  2. Implementation (actual phases)
  3. Technical decisions (what was chosen)
  4. Files modified
  5. Challenges & solutions
  6. Testing & documentation

Common Use Cases

Starting New Feature
User: "Create task doc for payments integration"
→ Generates TASK-07-payments.md
→ Empty template for planning
→ User fills in as they work
Completing Feature
User: "Document the auth feature I just finished"
→ Analyzes conversation
→ Generates TASK-06-auth.md
→ Complete implementation record
→ Archives for future reference
Mid-Feature Update
User: "Update TASK-05 with OAuth decision"
→ Reads existing TASK-05-auth.md
→ Adds to Technical Decisions section
→ Preserves rest of document

Error Handling

Navigator not initialized:

❌ .agent/tasks/ directory not found

Run /nav:init to set up Navigator structure first.

Task ID already exists (for creation):

⚠️  TASK-{XX} already exists

Options:
1. Read existing task
2. Use different ID
3. Archive/overwrite existing

Your choice [1-3]:

Insufficient context to archive:

⚠️  Not enough conversation context to generate implementation plan

Consider:
- Provide more details about what was built
- Manually create task doc
- Skip archiving

Continue with template? [y/N]:

Success Criteria

Task documentation is successful when:

  • Task file created in .agent/tasks/
  • Filename follows convention: TASK-{XX}-{slug}.md
  • Contains all required sections
  • Navigator index updated
  • PM tool updated (if configured)
  • User can reference task later

Scripts

generate_task.py: Create task documentation from conversation

  • Input: Conversation history, task ID
  • Output: Formatted task markdown

update_index.py: Update DEVELOPMENT-README.md task index

  • Input: New task info
  • Output: Updated index

Best Practices

Good task slugs:

  • oauth-implementation (descriptive)
  • stripe-payment-flow (clear purpose)
  • user-profile-page (specific feature)

Bad task slugs:

  • feature (too vague)
  • fix (not descriptive)
  • task1 (meaningless)

When to create task docs:

  • ✅ Starting major feature (> 1 day work)
  • ✅ Completing any feature (archive)
  • ✅ Complex implementation (capture decisions)
  • ❌ Tiny bug fixes (use SOPs instead)
  • ❌ Exploratory work (wait until direction clear)

Notes

Task docs are living documents:

  • Created when starting feature (template)
  • Updated during implementation (decisions)
  • Finalized when complete (archive)

They serve as:

  • Planning tool (before implementation)
  • Progress tracker (during implementation)
  • Historical record (after completion)
  • Knowledge base (for team/future)

This skill provides same functionality as /nav:doc feature command but with natural language invocation.

© qf-studio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files in skills/nav-task of qf-studio/navigator.

  • SKILL.md
  • functions/index_updater.py
  • functions/task_formatter.py
  • functions/task_id_generator.py
  • functions/test_task_id_generator.py
  • functions/verify_extractor.py

Open the folder on GitHubat commit 3bb9eac

Compare with similar skills

Nav Task next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Nav Task

What does Nav Task do?

Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index. Nav Task is an agent skill from qf-studio/navigator. Manage Navigator task documentation - create implementation plans, archive completed tasks, update task index.

When should I use Nav Task?

Nav Task fits situations like: user starts new feature; says document this feature.

How do I install Nav Task in Claude Code?

Run `npx skills add qf-studio/navigator --skill nav-task -a claude-code`. Or copy the skill folder (skills/nav-task in qf-studio/navigator) into .claude/skills/nav-task in your project. Claude Code loads it when a task matches its description.

How do I install Nav Task in Codex?

Run `npx skills add qf-studio/navigator --skill nav-task -a codex`. Or copy the skill folder (skills/nav-task in qf-studio/navigator) into .agents/skills/nav-task in your project. Codex loads it when a task matches its description.

Can I use Nav Task in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qf-studio/navigator --skill nav-task -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nav-task, .gemini/skills/nav-task, .github/skills/nav-task and .opencode/skills/nav-task in your project.

What does Nav Task need to run?

Going by SKILL.md and its folder, Nav Task needs Python for the scripts in its folder and the command-line tools its instructions call (python3, npm and gh). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash.

Does Nav Task access the network?

SKILL.md contains no URLs. Its commands use npm and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nav Task safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Nav Task use?

Nav Task is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nav Task use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nav Task?

Skills that share tags, products or a category with Nav Task: Ask Navigator (Yeachan-Heo/oh-my-claudecode, 40k stars), Dep Create (ai-dynamo/dynamo, 8.3k stars), Implement Feature (DevBetterCom/DevBetterWeb, 157 stars) and Dev Request (FHIR/fhir-codegen, 155 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nav Task?

qf-studio (a GitHub organization) maintains it in qf-studio/navigator, which has 355 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 8, 2026.

Source: qf-studio/navigator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.