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/Amey-Thakur/AI-SKILLSWrote 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/amey-thakur/ai-skills/agent-task-breakdown)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/agent-task-breakdown"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/agent-task-breakdown/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/amey-thakur/ai-skills/agent-task-breakdown"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/agent-task-breakdown.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 | $0.00024 | $0.00384 |
| Opus 5 | $0.00012 | $0.00192 |
| Sonnet 5 | $0.00005 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
agent-task-breakdown 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 10d 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.
What it actually says
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Break this task down for an AI agent to execute: {task}
Context: {context}
Produce a plan the agent can follow:
- Restate the goal and the definition of done: what observable state means the task is complete. Without this, the agent cannot know when to stop.
- Decompose into subtasks, each: a single concrete action, its inputs, its expected output, and how to verify that step succeeded before moving on. Size each so it is unambiguous and independently checkable.
- Order by dependency: what must happen before what. Mark which subtasks are independent (parallelizable) versus sequential.
- Flag the risky and irreversible steps: the ones that need extra care, confirmation, or a verification gate (anything that deletes, sends, publishes, or cannot be undone).
- Define failure handling per step: what the agent does if a step fails (retry, alternative approach, stop and report).
Rules: subtasks must be concrete actions, not vague topics ("query the users table for inactive accounts", not "handle the data"). Every step needs a way to verify it worked, because agents proceed confidently past silent failures. Keep the decomposition as flat as the task allows; deep nesting is hard to execute and debug. If the task is underspecified, name what must be clarified before the agent starts.
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.
- 10d ago First seen · 40 lines · 24 tokens per session scan A 6846902d15c2
agent-task-breakdown is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 384 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.