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.
git clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/technickai/ai-coding-config/autotask)<a href="https://agentmods.dev/commands/technickai/ai-coding-config/autotask"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/autotask/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/technickai/ai-coding-config/autotask"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/autotask.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00021 | $0.02184 |
| Opus 5.5 | $0.00008 | $0.00874 |
| Sonnet 5.5 | $0.00004 | $0.00437 |
| Haiku 4.5 | $0.00002 | $0.00218 |
Grade A, and why
autotask 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 11d ago.
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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/autotask - Autonomous Task Execution
Complexity Levels
Complexity determines how much planning, review, and validation the task receives.
auto (default)
Analyze the task to determine appropriate complexity. Consider:
- Scope: How many files likely affected? Single file → quick. Multi-file → balanced. Cross-cutting → deep.
- Risk: Does it touch auth, payments, data migrations, core abstractions? Higher risk → deeper review.
- Novelty: Established patterns → lighter touch. New patterns or architecture → deeper analysis.
- Ambiguity: Clear requirements → move fast. Fuzzy requirements → plan more.
Precedence: Explicit user signals (quick, balanced, deep) override auto-detection. Risk factors can escalate complexity but never reduce it below what the user specified.
When in doubt, err toward balanced. Quick is for genuinely trivial changes. Deep is for genuinely complex ones.
quick
Single-file changes, clear requirements, no design decisions.
- Skip heavy planning
- Implement directly
- Trust git hooks for validation
- Single self-review pass
- Create PR, brief bot wait, address feedback
Signals: "quick fix", "simple change", trivial scope, typo, single function
balanced
Standard multi-file implementation, some design decisions.
- Light planning with /load-rules
- Delegate exploration to agents
- Targeted testing for changed code
- /multi-review with 2-3 domain-relevant agents
- Create PR → /address-pr-comments → completion
Signals: Most tasks land here when auto-detected
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.
- 11d ago First seen · 295 lines · 21 tokens per session scan A eda550af3412
autotask is a command published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 12d ago), licensed MIT. It adds 21 tokens to every session and 2,184 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
Other commands, from other repositories
fix-tests-execute
Phase 2 of fixing-tests: Fix Execution - investigate, classify, fix, verify, and commit each work item.
verify
Run verification commands and confirm output before making success claims. Use before committing, creating PRs, or claiming work is complete. Evidence before assertions, ALWAYS.
undo-commit
Soft-undo the last commit. Restores its changes as staged, leaves working tree intact. Refuses if HEAD is already pushed to a tracked remote.
git-workflow
Run review, tests, and commit steps, then prepare a pull request.
session-report
Capture what changed this session and why, scoped to the current branch. Read by ship verbs when synthesizing the commit message; deleted after a successful commit.
commit
Stage, commit, and optionally ship further. Pass an escalation token (push, pr, merge, squash, squash merge) to skip the prompt. With no token, commits then asks how far to ship. Delegates message format to the atomic-git-discipline skill.