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 skills add hajekim/agentic-design-patterns-extension --skill goal-settinggit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/skills/hajekim/agentic-design-patterns-extension/goal-setting)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/goal-setting"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/goal-setting/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/skills/hajekim/agentic-design-patterns-extension/goal-setting"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/goal-setting.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 | $0.00410 | $0.04699 |
| Opus 5.5 | $0.00164 | $0.01880 |
| Sonnet 5.5 | $0.00082 | $0.00940 |
| Haiku 4.5 | $0.00041 | $0.00470 |
Grade A, and why
goal-setting 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 1mo 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.
This is a copy
100% identical to goal-setting — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 523 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal Setting & Monitoring Pattern
Overview
The Goal Setting & Monitoring Pattern equips agents with the ability to define, track, and adapt toward explicit objectives. Rather than passively executing instructions, goal-driven agents maintain awareness of what success looks like, measure their progress against defined criteria, and adjust their strategies when they detect goal drift or underperformance.
Core Principle: An agent without a goal is just a tool — an agent with a goal is a collaborator that knows when it has succeeded (or failed).
When This Skill Applies
Activate this pattern when:
- Agent tasks span multiple steps and require progress tracking
- Success must be explicitly defined and measurable, not just qualitative
- Agents need to self-correct when drifting from objectives
- Long-running tasks require checkpointing and status reporting
- Hierarchical goals with sub-goals need coordination
- Agents must balance competing objectives with different priorities
- Stakeholders need visibility into agent progress and completion
Rule of thumb: Use Goal Setting when "did the agent succeed?" has a real answer that must be verified, not just assumed.
Goal Hierarchy
SMART Goals for Agents
Effective agent goals are:
- Specific: Clear definition of what must be accomplished
- Measurable: Observable criteria to determine completion
- Achievable: Within the agent's capability set and available tools
- Relevant: Aligned with the user's true intent, not just literal instruction
- Time-bound: Defined completion criteria or iteration limits
Goal Decomposition Structure
High-Level Goal (Strategic)
├── Sub-Goal 1 (Tactical)
│ ├── Task 1.1 (Operational)
│ └── Task 1.2 (Operational)
├── Sub-Goal 2 (Tactical)
│ ├── Task 2.1 (Operational)
│ └── Task 2.2 (Operational)
└── Synthesis Goal (Integration)
└── Validation Task
DEFINE → PLAN → ACTION Workflow
DEFINE
Establish the goal framework:
- What is the primary objective? (State in measurable terms)
- What are the sub-goals that compose the primary goal?
- How is progress measured at each level? (Metrics, completion criteria)
- What constitutes success vs. partial success vs. failure?
- What are the constraints? (Time, resources, iteration limits)
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.
- 1mo ago First seen · 523 lines · 410 tokens per session scan A 0a78bc61ef44
goal-setting is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 6mo ago), licensed MIT. It adds 410 tokens to every session and 4,699 once invoked, about $0.0016 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to goal-setting, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
prompt-engineering
Use this skill when asked to create, refine, analyze, or optimize prompts for Large Language Models (LLMs). This skill ensures adherence to prompt engineering best practices and enforces a rigorous design workflow.
redteam
Expertise in offensive security research, vulnerability analysis, CMS-focused application testing, and red team operations.
redteam-cms
Focused methodology for authorized CMS fingerprinting, component inventory, misconfiguration review, and vulnerability validation.
redteam-exploit-validation
Focused workflow for validating exploitability safely and turning candidate issues into reproducible, bounded proof.
redteam-recon
Focused reconnaissance workflow for authorized security assessments, bug bounty triage, lab targets, and CTF infrastructure.
redteam-reporting
Focused reporting workflow for converting verified red-team work into clear, reproducible assessment artifacts.