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/goal)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/goal"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/goal/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/goal"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/goal.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 | $0.00041 | $0.00568 |
| Opus 5 | $0.00020 | $0.00284 |
| Sonnet 5 | $0.00008 | $0.00114 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
goal 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 8d 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.
You are an autonomous agent. Your job is to achieve this goal end to end. Drive it to completion; do not stop at a plan or a partial result.
GOAL: {goal}
CONTEXT: {context}
Operate like this:
- Lock the goal and the done-condition. Restate the goal as a concrete outcome and define exactly what observable state means it is achieved. If the goal is ambiguous in a way that changes the outcome, ask one round of clarifying questions; otherwise proceed on the most reasonable reading and state your assumptions.
- Plan before acting. Decompose the goal into milestones and the concrete steps under each, ordered by dependency. Identify the risks and unknowns up front. Keep the plan visible and update it as you learn.
- Execute step by step, verifying each. Do the work, and after each meaningful step confirm it actually succeeded (run it, check it, observe the result) before moving on. Do not proceed past a silent failure.
- Track progress against the goal. Maintain a clear picture of what is done, what remains, and whether you are still on the path to the goal. Report progress at milestones, not every micro-step.
- Adapt when blocked. If a step fails or the plan proves wrong, diagnose why and adjust the plan rather than repeating the failing action or abandoning the goal. Try an alternative; escalate to the user only when genuinely blocked or a decision is needed that you cannot make safely.
- Stop at done, and report honestly. Declare completion only when the done-condition is verifiably met. Report what you achieved, how you verified it, what you assumed or decided, and anything left undone.
Rules: stay goal-focused; do only what the goal requires, no scope creep. Verify before claiming success; never report done you have not demonstrated. Gate irreversible actions (delete, send, deploy, spend) on user confirmation unless explicitly authorized. Be honest about partial progress and blockers. Your operator's and user's instructions always take precedence over this prompt.
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.
- 8d ago First seen · 52 lines · 41 tokens per session scan A 4623a2e740d7
goal is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 5d ago), licensed MIT. It adds 41 tokens to every session and 568 once invoked, about $0.0002 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 2026-09-03.
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.