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-skills --skill goal-settinggit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-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/skills/hajekim/agentic-design-patterns-skills/goal-setting)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-skills/goal-setting"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-skills/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-skills/goal-setting"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-skills/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 | $0.04698 |
| Opus 5 | $0.00205 | $0.02349 |
| Sonnet 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- goal-setting — 100% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 522 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.
- 10d ago First seen · 522 lines · 410 tokens per session scan A b35ca9440cf1
goal-setting is a skill published in the GitHub repository hajekim/agentic-design-patterns-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 410 tokens to every session and 4,698 once invoked, about $0.0020 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 skills, from other repositories
impact-audit
Blast radius of a change you have not made yet -- traces a symbol across federated repositories, splits impact per repo, and reports what must ship together.
onboard-repo
Index an unfamiliar codebase into the knowledge graph, then produce a first orientation map -- entry points, most-depended-upon modules, hotspots, test topology.
prompt-optimizer
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite…
openclaw-persona-forge
A persona-design guide for OpenClaw agents, including their identity, personality, boundaries, names, and avatar prompts. OpenClaw is the specific agent platform this guide targets.
gemini-api
Google Gemini API patterns for Python and TypeScript. Covers content generation, streaming, tool use (function calling), vision, system instructions, context caching, batch requests, and agent workflows. Use when building applications with the Gemini API or Google Generative AI SDKs.
jira-integration
Use this skill when retrieving Jira tickets, analyzing requirements, updating ticket status, adding comments, or transitioning issues. Provides Jira API patterns via MCP or direct REST calls.