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 planninggit 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/planning)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/planning"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/planning/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/planning"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/planning.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 | $0.00392 | $0.03628 |
| Opus 5.5 | $0.00157 | $0.01451 |
| Sonnet 5.5 | $0.00078 | $0.00726 |
| Haiku 4.5 | $0.00039 | $0.00363 |
Grade A, and why
planning 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 planning — 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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning Pattern
Overview
The Planning Pattern enables an agent to move beyond simple reactive responses by formulating a structured sequence of actions to achieve a complex, high-level goal. Rather than reacting to each input in isolation, a planning agent first analyzes the goal, decomposes it into manageable sub-tasks, and then executes those steps — adapting its plan as new information emerges.
Core Principle: Transform a high-level objective into a structured, executable plan of actionable steps — then adapt the plan as reality unfolds.
When This Skill Applies
Activate this pattern when:
- A user's request is too complex to handle with a single action or tool call
- The task requires multiple interdependent steps executed in a logical order
- The solution path is not known in advance and must be discovered dynamically
- Workflows involve orchestrating multiple tools, APIs, or sub-agents
- The agent must adapt its approach based on intermediate results
- Tasks require systematic information gathering, synthesis, and reporting
Rule of thumb: Use Planning when the "how" needs to be discovered, not just the "what." If the solution requires a sequence of interdependent operations to reach a synthesized outcome — plan first.
DEFINE → PLAN → ACTION Workflow
DEFINE
Understand the planning requirements:
- What is the final goal state? What does success look like?
- What is the current state, including available resources and constraints?
- What sub-tasks are needed to move from initial state to goal state?
- What tools, APIs, or agents are available to execute each sub-task?
PLAN
Design the planning architecture:
- Map the high-level goal into a directed sequence of executable sub-tasks
- Identify dependencies between sub-tasks (which must complete before others begin)
- Assign tools, agents, or capabilities to each sub-task
- Define checkpoints where the plan can be reassessed based on intermediate results
- Plan for contingencies: what if a step fails or returns unexpected results?
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 · 366 lines · 392 tokens per session scan A 6e32a84c7568
planning is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 6mo ago), licensed MIT. It adds 392 tokens to every session and 3,628 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 planning, differing in 3 lines, and is treated as a copy.
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