--- name: awslabs/planning source: https://app.decimal.ai/s/awslabs-planning@1/SKILL.md source_sha256: 5d0bc3ca4cae --- ## 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-selection` skill. - Move to Phase 2 as soon as you can determine which skills and tools the plan needs. --- ## Phase 2: Plan Generation **Goal:** Propose a structured plan for the user to review. Generate a plan as a numbered list of tasks. Each task has: - A short name - A one-sentence description of what happens - Which skill handles it (if applicable) **Format:** ``` Based on what you've described, here's what I propose: 1. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])* 2. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])* 3. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])* Does this plan look right, or would you like to change anything? ``` **Rules for plan generation:** - Infer ordering from the Prerequisites column in the contracts table — a skill cannot appear before its prerequisites. If unsure, consult `references/skill-routing-constraints.md`. - Only offer capabilities covered by an available skill. If the user needs something no skill supports, say so. - Tailor the plan to the user's actual intent. Not every plan needs every skill. - If the user already has input artifacts (e.g., a trained model), skip the steps that produce them. When the user approves the plan, write it to `PLAN.md` and save it under the project directory structure defined by the directory-management skill. ```markdown # Plan 1. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_ 2. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_ 3. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_ ``` **Status indicators:** - ⬜ Not Started - 🔄 In Progress - ✅ Completed Update `PLAN.md` whenever a task's status changes. --- ## Phase 3: Plan Iteration **Goal:** Refine the plan until the user approves it. - If the user suggests changes, regenerate the plan incorporating their feedback. - If the user approves, begin execution by handing off to the first task's skill. --- ## Execution Once the plan is approved: 1. Before starting a task, update its status in `PLAN.md` to 🔄 (In Progress). 2. If the task maps to a skill, load that skill's full SKILL.md before doing any work. Do not attempt the task from general knowledge — always defer to the skill's instructions. 3. Execute the task by following the loaded skill's workflow. 4. When the task completes: - Update its status in `PLAN.md` to ✅ (Completed). If the task generated output files (scripts, notebooks, manifests), record the file paths under the completed task: ``` - [x] Fine-tune model - Output: `scripts/01_sft_finetuning.py` - Output: `manifests/sft-llama-20260515.json` ``` - Briefly confirm completion and move to the next task. 5. If the user interrupts with a new request mid-execution: - Completed tasks are immutable — do NOT modify them. - Regenerate the remaining tasks to incorporate the user's new input. - Present the updated remainder for approval before continuing. --- ## Plan Completion When all tasks in the plan are done: Present to the user: > "We've completed everything in the plan. What would you like to do next?" This re-enters Phase 1 (Brainstorming) for a new goal. There is no terminal state — the conversation continues as long as the user wants. --- ## References Load the reference plan that matches the customer's intent, then adjust based on their needs. - `references/evaluate-first-plan.md` — The evaluate-first workflow: evaluate a base model before deciding whether to fine-tune. - `references/model-customization-plan.md` — The direct fine-tuning plan. Use when the user has explicitly committed to fine-tuning. - `references/input-output-contracts.md` - A table showing all skills, required inputs, produced outputs, prerequisites, and constraints. - `references/skill-routing-constraints.md` — Optional supplemental resource about Mandatory inclusion rules, ordering constraints, and skill boundary rules.