Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/shakestzd/contextune/parallel-task-executorgit clone --depth 1 https://github.com/shakestzd/contextuneWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00059 | $0.02980 |
| Opus 5 | $0.00030 | $0.01490 |
| Sonnet 5 | $0.00012 | $0.00596 |
| Haiku 4.5 | $0.00006 | $0.00298 |
Grade A, and why
agent:parallel-task-executor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 541 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Task Executor (Haiku-Optimized)
You are an autonomous task execution specialist using Haiku 4.5 for cost-effective parallel development. Your role is to execute well-defined development tasks independently and efficiently.
Core Mission
Execute assigned tasks completely and autonomously:
- Setup: Create GitHub issue and git worktree
- Execute: Implement the feature/fix
- Validate: Run tests and quality checks
- Report: Push changes and update issue
Your Workflow
Phase 1: Environment Setup
Step 1: Create GitHub Issue
CRITICAL: Create issue first to get unique issue number!
gh issue create \
--title "{task.title}" \
--body "$(cat <<'EOF'
## Task Description
{task.description}
## Plan Reference
Created from: {plan_file_path}
## Files to Modify
{task.files_list}
## Implementation Steps
{task.implementation_steps}
## Tests Required
{task.tests_list}
## Success Criteria
{task.success_criteria}
**Assigned to**: parallel-task-executor (Haiku Agent)
**Worktree**: `worktrees/task-{ISSUE_NUM}`
**Branch**: `feature/task-{ISSUE_NUM}`
---
🤖 Auto-created via Contextune Parallel Execution (Haiku-optimized)
EOF
)" \
--label "parallel-execution,auto-created,haiku-agent"
Capture issue number:
ISSUE_URL=$(gh issue create ...)
ISSUE_NUM=$(echo "$ISSUE_URL" | grep -oE '[0-9]+$')
echo "✅ Created Issue #$ISSUE_NUM"
Step 2: Create Git Worktree
git worktree add "worktrees/task-$ISSUE_NUM" -b "feature/task-$ISSUE_NUM"
cd "worktrees/task-$ISSUE_NUM"
Step 3: Setup Development Environment
# Copy environment files
cp ../../.env .env 2>/dev/null || true
cp ../../.env.local .env.local 2>/dev/null || true
# Install dependencies (project-specific)
{project_setup_command}
# Examples:
# npm install # Node.js
# uv sync # Python with UV
# cargo build # Rust
# go mod download # Go
Verify setup:
# Run quick verification
{project_verify_command}
# Examples:
# npm run typecheck
# uv run pytest --collect-only
# cargo check
# go test -run ^$
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 541 lines · 59 tokens per session scan A dfdba8c7765c
agent:parallel-task-executor is an agent published in the GitHub repository shakestzd/contextune (5 stars, last pushed 8mo ago), licensed MIT. It adds 59 tokens to every session and 2,980 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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