Agent skill

Nav Loop

by qf-studio in qf-studio/navigator

Run tasks until complete with structured completion signals.

MITAuto-check: notesAgent Workflows

Install Nav Loop

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

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

GitHub CLI
$ gh skill install qf-studio/navigator nav-loop --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-loop .claude/skills/nav-loop && 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-loop
GitHub stars
355
Token cost
~4.4k tokens
SKILL.md length
1,289 words
Files
9
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

Run tasks until complete with structured completion signals.

  • Works in 8 steps: Initialize Loop State → Execute Iteration → Generate Status Block → …
  • Says run until done
  • SKILL.md covers Why This Exists, When to Invoke, Configuration and Execution Steps, plus 7 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Nav Loop is an agent skill from qf-studio/navigator. Run tasks until complete with structured completion signals. Auto-invoke when user says "run until done", "keep going until complete", "iterate until finished", "loop mode", "autonomous mode".

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files (for example `functions/exit_gate.py`, `functions/phase_detector.py` and `functions/stagnation_detector.py`).

It sits in Agent Workflows, covering Autonomous loops. 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

  • Says run until done
  • Keep going until complete
  • Iterate until finished
  • Autonomous mode

Example prompts

  • “run until done”
  • “keep going until complete”
  • “iterate until finished”
  • “/nav-loop”

Requirements

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

Workflow steps

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

  1. Initialize Loop State
  2. Execute Iteration
  3. Generate Status Block
  4. 5: Per-Iteration Approval Gate (optional)
  5. Check Stagnation
  6. Check Exit Conditions
  7. Handle Max Iterations
  8. Complete Loop

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
    • Grep
    • Glob
    • AskUserQuestion

    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

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

  • Network

    No URLs in SKILL.md.

    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 Loop loads about 4.4k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 1,289 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

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, Grep, Glob, AskUserQuestion

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). 1,289 words, ~4,409 tokens.

Download SKILL.mdSave it as .claude/skills/nav-loop/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
nav-loop
description
Run tasks until complete with structured completion signals. Auto-invoke when user says "run until done", "keep going until complete", "iterate until finished", "loop mode", "autonomous mode".
allowed-tools
Read, Write, Edit, Bash, Grep, Glob, AskUserQuestion
version
1.0.0

Navigator Loop Skill

Execute tasks iteratively until completion with structured signals, stagnation detection, and dual-condition exit gates.

Why This Exists

Traditional AI coding requires manual "keep going" prompts. Navigator Loop provides:

  • Structured completion signals (NAVIGATOR_STATUS block)
  • Dual-condition exit gate (heuristics + explicit signal)
  • Stagnation detection (circuit breaker for stuck loops)
  • Progress visibility (phases: INIT → RESEARCH → IMPL → VERIFY → COMPLETE)

Based on Ralph's autonomous loop innovations, adapted for Navigator's context-efficient architecture.

When to Invoke

Auto-invoke when:

  • User says "run until done", "keep going until complete"
  • User says "iterate until finished", "autonomous mode"
  • User says "loop mode", "don't stop until done"
  • Task document has loop_mode: true

DO NOT invoke if:

  • Single-step task (no iteration needed)
  • User says "just do this once"
  • Already in loop mode (prevent nested loops)
  • User explicitly disabled loop mode

Configuration

Loop mode settings in .agent/.nav-config.json:

json
{
  "loop_mode": {
    "enabled": false,
    "max_iterations": 5,
    "stagnation_threshold": 3,
    "exit_requires_explicit_signal": true,
    "show_status_block": true,
    "iteration_approval": "none",
    "periodic_interval": 3,
    "never_pause_on_stagnation": false,
    "stagnation_diversify_strategy": "combine"
  }
}

Core options:

  • enabled: Default state for new tasks
  • max_iterations: Hard cap to prevent infinite loops (1-20)
  • stagnation_threshold: Same-state count before pause (2-5)
  • exit_requires_explicit_signal: Require EXIT_SIGNAL alongside heuristics
  • show_status_block: Render NAVIGATOR_STATUS each iteration

Autonomous / overnight options (v6.2.2+):

  • iteration_approval: When to prompt the user for accept/reject between iterations.
    • "none" (default) — never prompt; loop runs uninterrupted
    • "strict" — prompt after every iteration
    • "periodic" — prompt every N iterations (where N = periodic_interval, default 3)
  • periodic_interval: When iteration_approval == "periodic", the cadence of prompts. Default 3 (every 3rd iteration). Set higher for less frequent check-ins on long overnight runs (e.g., 5 or 10).
  • never_pause_on_stagnation: If true, stagnation triggers auto-diversification instead of an AskUserQuestion pause. Required for true overnight runs. Inspired by karpathy/autoresearch's NEVER STOP directive.
  • stagnation_diversify_strategy: Which recovery to attempt when never_pause_on_stagnation fires.
    • "combine" — combine previous near-misses / partially-met indicators
    • "radical" — try a substantially different approach (re-architect, swap library)
    • "reread" — re-read the in-scope task/system docs for missed signals

Safety guard: Setting never_pause_on_stagnation: true REQUIRES max_iterations to be set explicitly (the default of 5 is fine; the point is — no infinite default). Without a max, an autonomous loop can spin forever on a fundamentally broken task.

Execution Steps

Step 1: Initialize Loop State

Load configuration:

bash
python3 functions/phase_detector.py --init

Initialize tracking variables:

iteration = 1
max_iterations = config.loop_mode.max_iterations or 5
stagnation_threshold = config.loop_mode.stagnation_threshold or 3
hash_history = []
phase = "INIT"

Display loop start:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LOOP MODE ACTIVATED
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Task: {TASK_DESCRIPTION}
Max iterations: {max_iterations}
Stagnation threshold: {stagnation_threshold}

Starting iteration 1...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Step 2: Execute Iteration

Perform task work based on current phase:

PhaseActions
INITLoad context, understand requirements
RESEARCHExplore codebase, find patterns
IMPLWrite code, make changes
VERIFYRun tests, validate functionality, simplify code
COMPLETEAll indicators met, ready to exit

Track changes during iteration:

  • Files read (for RESEARCH detection)
  • Files changed (for IMPL detection)
  • Tests run (for VERIFY detection)
  • Commits made (for completion indicator)
Step 3: Generate Status Block

After each iteration, generate NAVIGATOR_STATUS:

bash
python3 functions/status_generator.py \
  --phase "{phase}" \
  --iteration "{iteration}" \
  --max-iterations "{max_iterations}" \
  --indicators "{indicators_json}" \
  --state-hash "{current_hash}" \
  --prev-hash "{previous_hash}" \
  --stagnation-count "{stagnation_count}"

Display status block:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NAVIGATOR_STATUS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Phase: {PHASE}
Iteration: {N}/{MAX}
Progress: {PERCENT}%

Completion Indicators:
  [{x or space}] Code changes committed
  [{x or space}] Tests passing
  [{x or space}] Code simplified
  [{x or space}] Documentation updated
  [{x or space}] Ticket closed
  [{x or space}] Marker created

Exit Conditions:
  Heuristics: {MET}/{TOTAL} (need 2+)
  EXIT_SIGNAL: {true/false}

State Hash: {HASH}
Previous Hash: {PREV_HASH}
Stagnation: {COUNT}/{THRESHOLD}

Next Action: {NEXT_ACTION}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Step 3.5: Per-Iteration Approval Gate (optional)

Skip this step if config.loop_mode.iteration_approval == "none" (default).

Run this step if the user wants oversight between iterations — for risky changes, learning the loop's behavior, or sanity-checking before a long run.

SettingBehavior
"none"Never prompt. Loop continues to Step 4.
"strict"Prompt after every iteration.
"periodic"Prompt every Nth iteration. N defaults to 3; configurable via loop_mode.periodic_interval.

When prompting, use AskUserQuestion immediately after the status block:

Question: "Accept iteration {N} and continue?"
Options:
  1. [Continue] - Iteration accepted, proceed to next
  2. [Adjust]   - Provide feedback, incorporate into next iteration
  3. [Abort]    - End loop, create partial-completion marker

Decision handling:

  • Continue: Proceed to Step 4 normally.
  • Adjust: Capture the user's feedback into a transient note, do NOT advance hash history (so the next iteration is judged as fresh progress), continue to Step 4.
  • Abort: Jump to Step 8 (Cleanup) with status: "user_aborted".

This gate runs BEFORE stagnation detection so that a rejected iteration doesn't accidentally accumulate stagnation count.

Step 4: Check Stagnation

Calculate state hash:

bash
python3 functions/stagnation_detector.py \
  --phase "{phase}" \
  --indicators "{indicators_json}" \
  --files-changed "{files_json}" \
  --history "{hash_history_json}"

If stagnation detected (same hash for N iterations), the response depends on never_pause_on_stagnation.

Default behavior (never_pause_on_stagnation: false)

Prompt the user:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
STAGNATION DETECTED
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Same state detected for {N} consecutive iterations.

Current State:
  Phase: {PHASE}
  Indicators: {MET}/{TOTAL}
  Last Action: {LAST_ACTION}

Possible causes:
1. Blocked by external dependency
2. Unclear requirements
3. Test failures preventing progress
4. Missing context or permissions

Options:
1. [Continue] - Try one more iteration
2. [Clarify] - Explain what's blocking
3. [Abort] - End loop, manual intervention

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Use AskUserQuestion for choice:

  • Continue: Reset stagnation counter, continue loop
  • Clarify: User explains blocker, incorporate and continue
  • Abort: Exit loop with partial completion marker
Autonomous behavior (never_pause_on_stagnation: true)

The loop is running unattended (overnight, CI, etc.). Do NOT prompt — auto-diversify based on stagnation_diversify_strategy:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
STAGNATION → AUTO-DIVERSIFY ({STRATEGY})
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Same state for {N} iterations. Auto-recovery: {STRATEGY}
Stagnation counter reset.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
StrategyWhat to attempt next iteration
combineRe-examine partially-met indicators; combine 2 near-miss approaches from previous iterations
radicalDiscard the current approach; try a substantially different design (different library, different architecture, different algorithm)
rereadRe-read the in-scope task doc + relevant system docs; look for a missed signal or constraint

After diversifying:

  • Reset stagnation counter to 0
  • Record the diversification in the iteration's notes (so the next status block reflects it)
  • Continue to Step 5

Hard stop: Even in autonomous mode, the loop still terminates on max_iterations. If diversification has been triggered ≥3 times within a single run, escalate to the abort path (creates a loop-aborted marker and exits) — repeated diversification is itself a signal that the task is fundamentally stuck.

Inspired by karpathy/autoresearch's NEVER STOP directive: "If you run out of ideas, think harder — read papers, re-read in-scope files for new angles, try combining previous near-misses, try more radical architectural changes."

Show full SKILL.md (521 more words)Show less
Step 5: Check Exit Conditions

Evaluate dual-condition gate:

bash
python3 functions/exit_gate.py \
  --indicators "{indicators_json}" \
  --exit-signal "{exit_signal}" \
  --require-explicit "{config.exit_requires_explicit_signal}"

Exit conditions:

  1. Heuristics: At least 2 completion indicators met
  2. EXIT_SIGNAL: Explicit signal that task is complete

Completion indicators (mapped from autonomous protocol):

  • code_committed: Changes committed to git
  • tests_passing: Test suite passes (exit code 0)
  • code_simplified: Code simplified for clarity (v5.4.0+)
  • docs_updated: Documentation files changed
  • ticket_closed: PM tool ticket marked done
  • marker_created: Completion marker exists

Exit decision logic:

IF heuristics >= 2 AND exit_signal == true:
  → EXIT: Task complete
ELIF heuristics >= 2 AND exit_signal == false:
  → CONTINUE: Awaiting explicit completion signal
ELIF exit_signal == true AND heuristics < 2:
  → BLOCKED: Cannot exit with insufficient indicators
ELSE:
  → CONTINUE: More work needed
Step 6: Handle Max Iterations

If iteration >= max_iterations:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
MAX ITERATIONS REACHED
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Completed {MAX} iterations without full completion.

Current State:
  Phase: {PHASE}
  Indicators: {MET}/{TOTAL}
  EXIT_SIGNAL: {true/false}

Progress made:
- {PROGRESS_ITEM_1}
- {PROGRESS_ITEM_2}

Options:
1. [Extend] - Add 3 more iterations
2. [Complete] - Accept current state as done
3. [Abort] - Exit without completion

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Step 7: Complete Loop

When exit conditions met, emit the exit signal in JSON format and display completion:

pilot-signal
{"v":2,"type":"exit","success":true,"reason":"All criteria met"}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LOOP COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Task: {TASK_DESCRIPTION}
Iterations: {FINAL_COUNT}/{MAX}
Final Phase: COMPLETE

Completion Indicators:
  [x] Code changes committed
  [x] Tests passing
  [x] Code simplified
  [x] Documentation updated
  [ ] Ticket closed (skipped - no PM tool)
  [x] Marker created

Exit Conditions:
  Heuristics: 4/5 (passed)
  EXIT_SIGNAL: true (passed)

Summary:
- {KEY_CHANGE_1}
- {KEY_CHANGE_2}
- {KEY_CHANGE_3}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Execute autonomous completion protocol:

  1. Commit changes (if not already)
  2. Archive task documentation
  3. Close ticket (if PM configured)
  4. Create completion marker (with loop state)
  5. Suggest compact

Setting EXIT_SIGNAL

The EXIT_SIGNAL is set explicitly by Claude when:

  • All primary task requirements are met
  • Code is functional and tested
  • No obvious remaining work

How to signal completion (v2 JSON format):

pilot-signal
{"v":2,"type":"exit","success":true,"reason":"All requirements met"}

The JSON format in a pilot-signal code block ensures unambiguous detection by Pilot automation. The reason field should briefly describe why the task is complete.

Signal fields:

  • v: Version (always 2)
  • type: Signal type (always "exit" for completion)
  • success: Whether task completed successfully (true/false)
  • reason: Brief explanation of completion state

This explicit declaration prevents premature exits when heuristics are met but work remains.


Phase Detection

Phases auto-detected based on context:

python
def detect_phase(context):
    # COMPLETE: Exit conditions met
    if indicators_met >= 4 and exit_signal:
        return "COMPLETE"

    # VERIFY: Tests running or recently run
    if context.tests_running or context.test_exit_code is not None:
        return "VERIFY"

    # IMPL: Files being modified
    if context.files_changed:
        return "IMPL"

    # RESEARCH: Reading files, searching
    if context.files_read and not context.files_changed:
        return "RESEARCH"

    # INIT: Default starting state
    return "INIT"

Integration with Navigator

With Autonomous Completion

Loop mode enhances (not replaces) the autonomous protocol:

  • Completion indicators map to autonomous steps
  • EXIT_SIGNAL triggers autonomous completion
  • Marker includes loop state for restoration
With nav-simplify (v5.4.0+)

Simplification runs during VERIFY phase:

  • After tests pass, before committing
  • Configurable via simplification.enabled in .nav-config.json
  • Adds code_simplified completion indicator
  • Skip if no code changes (docs-only tasks)
With nav-diagnose

Stagnation triggers nav-diagnose quality check:

  • 3 same-state loops = potential quality issue
  • nav-diagnose helps identify root cause
  • Re-anchoring can resolve stuck loops
With nav-marker

Markers capture loop state:

  • Current iteration and max
  • Phase at time of marker
  • State hash for continuity
  • Completion indicators status
With ToM Features

Loop mode respects ToM configuration:

  • Verification checkpoints still apply in VERIFY phase
  • Profile preferences affect communication style
  • Belief anchors can help clarify stuck states

Predefined Functions

functions/status_generator.py

Generates formatted NAVIGATOR_STATUS block.

functions/exit_gate.py

Evaluates dual-condition exit (heuristics + explicit signal).

functions/stagnation_detector.py

Calculates state hash and detects consecutive same-states.

functions/phase_detector.py

Auto-detects current task phase from context.


Error Handling

Config not found:

Loop mode config not found in .nav-config.json.
Using defaults: max_iterations=5, stagnation_threshold=3

Function execution fails:

  • Fall back to manual evaluation
  • Log error but don't interrupt loop
  • Continue with best-effort phase detection

User aborts mid-loop:

  • Create partial completion marker
  • Document progress made
  • List remaining work

Success Criteria

Loop mode succeeds when:

  • Task completes within max_iterations
  • No stagnation pauses (or resolved quickly)
  • EXIT_SIGNAL + heuristics both satisfied
  • Completion marker includes loop state
  • User sees clear progress each iteration

Examples

Example 1: Simple Feature
User: "Run until done: add isPrime function with tests"

Iteration 1 (INIT → RESEARCH):
  - Read existing math utils
  - Found test patterns

Iteration 2 (IMPL):
  - Created isPrime function
  - Created test file

Iteration 3 (VERIFY):
  - Ran tests: PASS
  - Committed changes
pilot-signal
{"v":2,"type":"exit","success":true,"reason":"isPrime function implemented and tests passing"}
→ Loop complete in 3 iterations
Example 2: Stagnation Recovery
User: "Run until done: fix authentication bug"

Iteration 1-3 (IMPL):
  - Same changes attempted
  - Tests still failing
  - State hash unchanged

→ STAGNATION DETECTED

User: "The test needs a mock for the auth service"

Iteration 4 (IMPL):
  - Added mock
  - Tests pass
pilot-signal
{"v":2,"type":"exit","success":true,"reason":"Auth bug fixed with mock service"}
→ Loop complete in 4 iterations

Limitations

Cannot handle:

  • External blockers (waiting for API, permissions)
  • Subjective completion criteria ("make it look nice")
  • Tasks requiring human judgment mid-loop

Should not use for:

  • Quick fixes (single iteration sufficient)
  • Exploratory work (no clear completion state)
  • Tasks with security implications (need human review)

This skill provides Ralph-style "run until done" capability while maintaining Navigator's context efficiency and ToM integration.

© 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 8 other files in skills/nav-loop of qf-studio/navigator.

  • SKILL.md
  • functions/exit_gate.py
  • functions/phase_detector.py
  • functions/stagnation_detector.py
  • functions/status_generator.py
  • functions/test_exit_gate.py
  • functions/test_phase_detector.py
  • functions/test_stagnation_detector.py
  • functions/test_status_generator.py

Open the folder on GitHubat commit 3bb9eac

Compare with similar skills

Nav Loop 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.

Nav Loop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nav Loop this skillqf-studio/navigator355—~4.4kAutomated safety check: NotesMIT
Show Me Your Work Decision Logcursor/plugins10k8 repos~1.6kAutomated safety check: PassNone
Autoresearch Iteration Loopuditgoenka/autoresearch6.5k1 repos~2kAutomated safety check: PassMIT
Install Loop Engineeringcobusgreyling/loop-engineering11k1 repos~648Automated safety check: PassMIT
LoopyForward-Future/loopy3.2k—~3.9kAutomated safety check: PassMIT
AI Performance Improvement Plantanweai/pua20k2 repos~6.9kAutomated safety check: PassMIT

Similar skills

  • Official

    Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.

    10k GitHub starsUsed in 8 repos~1.6k tokens
    Agent WorkflowsAuto-check passed
  • Autoresearch Iteration Loop

    uditgoenka/autoresearch

    Runs an autonomous modify, verify, keep-or-discard loop against any metric, with subcommands for planning, debugging, fixing, security audits, shipping and more.

    6.5k GitHub starsUsed in 1 repo~2k tokens
    Agent WorkflowsAuto-check passed
  • Install Loop Engineering

    cobusgreyling/loop-engineering

    Installs Loop Engineering into a project through the single @cobusgreyling/loop CLI, scaffolding a report-only loop and a readiness score.

    11k GitHub starsUsed in 1 repo~648 tokens
    Agent WorkflowsAuto-check passed
  • Loopy

    Forward-Future/loopy

    Discover, find, compare, audit, repair, adapt, craft, run, debrief, save, and prepare repeatable AI-agent loops for publication.

    3.2k GitHub stars~3.9k tokensUpdated 28 days ago
    Agent WorkflowsAuto-check passed
  • Pushes an agent to exhaust every option, investigate before asking and take initiative beyond the literal request, instead of giving up or waiting passively.

    20k GitHub starsUsed in 2 repos~6.9k tokens
    Agent WorkflowsAuto-check passed
  • LoopX Self Repair

    loopx-project/loopx

    Diagnoses surprising LoopX behavior, such as stale recommendations or tiny progress, assigns it to the responsible layer and repairs it at the lowest durable level.

    6.2k GitHub stars~2.2k tokensUpdated today
    Agent WorkflowsAuto-check passed

More from qf-studio/navigator

All 32 skills in this repo
  • Nav Start

    qf-studio/navigator

    Load Navigator documentation navigator when starting development session, resuming work, or beginning new feature.

    355 GitHub stars~4.7k tokensUpdated today
    Auto-check: notes
  • Backend Endpoint

    qf-studio/navigator

    Create REST/GraphQL API endpoint with validation, error handling, and tests.

    355 GitHub stars~4.5k tokensUpdated today
    Auto-check: notes
  • Backend Test

    qf-studio/navigator

    Generate backend tests (unit, integration, mocks) for existing code.

    355 GitHub stars~1.5k tokensUpdated today
    Auto-check: notes
  • Database Migration

    qf-studio/navigator

    Create database migration with schema changes and rollback. An agent skill from qf-studio/navigator.

    355 GitHub stars~3.7k tokensUpdated today
    Auto-check: notes
  • Frontend Component

    qf-studio/navigator

    Create React/Vue component with TypeScript, tests, and styles.

    355 GitHub stars~4.5k tokensUpdated today
    Auto-check: notes
  • Frontend Test

    qf-studio/navigator

    Generate frontend component tests (React Testing Library, Vue Test Utils, snapshot) for existing components.

    355 GitHub stars~1.6k tokensUpdated today
    Auto-check: notes

Categories

Questions about Nav Loop

What does Nav Loop do?

Run tasks until complete with structured completion signals. Nav Loop is an agent skill from qf-studio/navigator. Run tasks until complete with structured completion signals.

When should I use Nav Loop?

Nav Loop fits situations like: says run until done; keep going until complete; iterate until finished; autonomous mode.

How do I install Nav Loop in Claude Code?

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

How do I install Nav Loop in Codex?

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

Can I use Nav Loop 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-loop -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-loop, .gemini/skills/nav-loop, .github/skills/nav-loop and .opencode/skills/nav-loop in your project.

What does Nav Loop need to run?

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

Does Nav Loop access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nav Loop 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 Loop use?

Nav Loop 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 Loop use?

About 4.4k tokens (SKILL.md is roughly 18k 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 Loop?

Skills that share tags, products or a category with Nav Loop: Show Me Your Work Decision Log (cursor/plugins, 10k stars), Autoresearch Iteration Loop (uditgoenka/autoresearch, 6.5k stars), Install Loop Engineering (cobusgreyling/loop-engineering, 11k stars) and Loopy (Forward-Future/loopy, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nav Loop?

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.