Notre avis
Cette compétence implémente un système de planification persistant basé sur des fichiers, aidant les agents IA à gérer des tâches complexes en plusieurs étapes via la création et la mise à jour de fichiers Markdown.
Points forts
- Offre une approche structurée et persistante qui survit aux réinitialisations de conversation.
- Réduit la pression sur le contexte en externalisant l'état dans des fichiers.
- Encourage un suivi granulaire des étapes et des mises à jour immédiates, améliorant la fiabilité de l'agent.
- Permet la reprise facile de tâches interrompues avec resume_last_run.
Limites
- Dépend de l'appel correct des outils par l'agent ; une mauvaise utilisation peut rendre les fichiers obsolètes.
- Limité au répertoire de travail courant—pas de planification inter-projets.
- Aucune capacité de collaboration ou de partage entre plusieurs agents.
Utilisez cette compétence pour toute tâche de développement complexe en plusieurs phases où la décomposition et le suivi sont cruciaux et où l'historique de conversation seul peut être insuffisant.
Évitez cette compétence pour des tâches très simples d'une seule étape où l'effort de création et de maintenance des fichiers de plan serait superflu.
Analyse de sécurité
SûrThe skill only manages three markdown planning files and does not perform any destructive, network, or arbitrary code execution operations. It is limited to the listed planning tools, which are harmless.
Aucun point d'attention détecté
Exemples
Initiate a planning session to build a Python web scraper with BeautifulSoup. Include phases for research, setup, implementation, and testing.I was working on a plan to develop a REST API but got interrupted. Please resume the last run and continue where I left off.Add a finding: under 'Technical Decisions', record that we chose PostgreSQL over MySQL because of better JSON support.name: planner description: File-based planning system. allowed-tools:
- init_planning
- resume_last_run
- read_plan
- update_plan_status
- mark_step_complete
- add_finding
- erase_plans
Planner Skill
This skill implements a persistent planning pattern using markdown files.
Files Managed
task_plan.md: Tracks phases, progress, and high-level goal.findings.md: Records research, technical decisions, and resources.progress.md: Detailed session log of actions and thoughts.
Tools
init_planning
Initialize the planning files in the current working directory. For coding task, you do not have to provide rigorous testing.
task_description: What needs to be done.phases: List of high-level phases.phase_steps: REQUIRED. List of lists of strings. Each inner list provides the detailed checklist steps for the corresponding phase.- Example:
[["Research the official API documentation for authentication methods", "Read the developer guides on rate limiting strategies"], ["Set up the initial project structure using Python Poetry and git", "Implement the core logic for the connection manager class"]]
- Example:
resume_last_run
Reads existing planning files to allow the agent to resume an interrupted task.
Returns the content of task_plan.md and the recent log from progress.md.
read_plan
Read the current status from the planning files.
update_plan_status
Update the status of a phase in task_plan.md and log a note in progress.md.
phase_name: Name of the phase.status: New status (pending, in_progress, completed).notes: Optional note to append to progress log.
mark_step_complete
Mark a specific step in task_plan.md as complete ([x]).
phase_name: The name of the phase containing the step.step_keyword: A unique substring to identify the step line.
add_finding
Record a finding, decision, or resource in findings.md.
category: Target section ("Requirements", "Research", "Technical Decisions", "Resources").content: The text to add. For technical decisions, you can use markdown table row format| Decision | Rationale |.
erase_plans
Quickly erase existing plans in artifacts/cache by deleting the three planning files.
- This is useful when you want to start fresh without any leftover plan files.
- No parameters needed.
Best Practices for Agents
- Start Strong: Always begin complex tasks with
init_planning. Break down phases into granular, actionable steps inphase_stepsrather than broad goals. - Update Frequently: As you complete sub-tasks, use
mark_step_completeIMMEDIATELY. This acts as a "save point" and keeps your reasoning grounded. - Log Discoveries: Don't rely on conversation history for long-term facts. Use
add_findingto record important URLs, file paths, or architectural decisions. - Transition Phases: When a phase is done, explicitly use
update_plan_statusto mark it ascompletedand the next one asin_progress. - Resume Smart: If you see existing plan files, prefer
resume_last_runover starting from scratch.
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