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 agentmods add skills/awslabs/agent-plugins/planningnpx skills add awslabs/agent-plugins --skill planninggit clone --depth 1 https://github.com/awslabs/agent-pluginsWhat 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 | $0.00115 | $0.01596 |
| Opus 5 | $0.00057 | $0.00798 |
| Sonnet 5 | $0.00023 | $0.00319 |
| Haiku 4.5 | $0.00012 | $0.00160 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Principles
- One question at a time. Each question should resolve a branching decision in the plan. Avoid generic or out-of-domain questions.
- Surface constraints early. If a user decision would constrain downstream options, flag it before the plan is finalized.
- Keep plans short. Only include tasks that are necessary for the user's stated goal.
- Don't ask what you already know. Check conversation history and project files before asking the user.
Phase 1: Brainstorming
Goal: Understand what the user wants to accomplish and identify which skills belong in the plan.
Read references/input-output-contracts.md, references/model-customization-plan.md, and references/evaluate-first-plan.md to:
- Identify which skills could be relevant to the user's stated goal.
- Check whether the user has the necessary input artifacts for each skill. If not, find the skills that generate those inputs and add them first.
- Order skills to allow a smooth transition from one to the next and avoid dead ends.
- Check if a recommended workflow matches the user's needs. If not, assess what modifications are needed and verify they are possible against the contracts table.
- Decide which skills in a matching workflow can be skipped.
- Surface limitations early — if a user decision (model choice, region, evaluation method) would constrain downstream options, mention it proactively, get user feedback, and adapt the plan accordingly.
During brainstorming:
- Workflow choice gate: Before generating any plan, determine whether the user wants the evaluate-first workflow or the direct fine-tuning workflow. If the user has explicitly chosen (e.g., "evaluate first", "skip evaluation", "already evaluated the base model"), proceed with their choice. Otherwise, present both options with brief pros/cons and ask the user to choose. Saying "fine-tune" or naming a technique alone is NOT an explicit choice to skip evaluation — the user may not know evaluate-first is an option. Do NOT present a plan until the user has chosen a path. After they choose, read ONLY the corresponding reference plan.
- Use the Restrictions column of the contracts table to flag constraints as soon as the relevant decision is made. Examples (non-comprehensive list, check contracts table for the full picture):
- User picks a Nova model → alert that deployment regions are limited.
- User picks a region → alert if it conflicts with model availability.
- If a restriction applies, check whether it requires changes to other steps in the plan.
- Do NOT ask the user about base model selection or preferences. Model selection is handled exclusively by the
model-selectionskill. - Move to Phase 2 as soon as you can determine which skills and tools the plan needs.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 143 lines · 115 tokens per session scan A b8bd4679c527
planning is a skill published in the GitHub repository awslabs/agent-plugins (876 stars, last pushed 6d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,596 once invoked, about $0.0006 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 2026-08-30.
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