Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/planning/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/planning)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/planning"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/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/smart-ai-memory/attune-ai/planning"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.01293 |
| Opus 5.5 | $0.00013 | $0.00517 |
| Sonnet 5.5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
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.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning
Prompt refinement before scoping
Use the current turn's Attune refinement hook result, or call
prompt_refinement(action="status") if none was supplied. Follow its shared
policy to gather material missing context and incorporate answers into the
working prompt. If the user asks only to polish a prompt, return that prompt
without entering plan execution. An opt-out suppresses optional refinement,
not clarification needed for the actual planning task. Never restart intake
for a user's answer or correction, and never duplicate questions already asked
by refinement. Enable/disable only on the user's explicit request; a one-time
skip does not change the saved preference.
This skill fallback reaches planning invocations. Automatic delivery across all messages requires a host that runs the plugin hook or follows MCP server instructions; installing a skill alone does not establish that behavior.
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="spec-engine", mode="preamble") and
display the returned preamble text as a blockquote. Then
tell the user they can say "tell me more" for a step-by-step
guide, or answer the scoping questions below to proceed.
If the MCP call fails, fall back to:
Planning — Helps you plan features, architecture, and TDD strategy before writing code.
High-level development planning and architecture design.
Routes
| Subcommand | Action |
|---|---|
feature |
Plan a new feature |
tdd |
Plan TDD approach |
architecture |
Architecture review |
MCP Tools
| Tool | What It Does |
|---|---|
research_synthesis |
Synthesize insights from source documents at a path to inform planning |
Use research_synthesis when the user needs to gather
context from a directory of files or docs before planning.
Pass the directory (or file) as path; optionally set
depth to quick, standard, or deep:
research_synthesis(path="<dir or file>", depth="standard")
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 Changed · +54 lines cbcee8ca7dff
- 1mo ago First seen · 72 lines · 32 tokens per session scan A 209183fa1490
planning is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 1,293 once invoked, about $0.0001 per session on Opus 5.5. 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.
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