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Agents48k · #2025

accint-solve

Helps your agent find solutions to a goal. It uses a memory system to think and decide the best path, useful for solving problems intelligently.

What it does

This tool helps the AI think through a problem using its memory and reasoning. It either gives you the final answer or asks for your input to decide the next step.

When to use it

  • ✓When you want the AI to solve a complex problem.
  • ✓When the AI needs to use what it has already learned to answer you.
  • ✓When the AI needs your opinion to proceed.

Example of what to ask your AI

“Solve this problem: [describe the problem here]”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/accint-solve/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/accint-solve/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI uses its memory to try and solve your request. If it's unsure, it shows you its thoughts and asks for your input to decide how to move forward.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill accint-solve

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/accint-solve/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

solve

When to Use

Use this skill when you need route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

Routing sugar over the two MCP verbs — no logic lives here.

  1. Call acc_act(runtime="solve", input="<the goal>").
  2. If the result is final: surface the answer, the commitment id, and the cited [ids].
  3. If the result is a brain_frame: it is YOUR deliberation turn — the frame is typed (which hole, what was retrieved, what is predicted). Reason over it, then submit via acc_act(runtime="continue", input={"frame_id": ..., "submit_token": ..., "proposal_text": ...}).
  4. End proposal_text with PREDICT: <0.00-1.00> <why>; acc strips that line before the owner sees it and uses it to calibrate the Work Model against later outcomes.
  5. Never leave a received frame unresolved; never solo-derive outside the loop.
  6. Close the commitment honestly later with acc_act(runtime="outcome", ...).

Example

User request:

Use @accint-solve for this task: Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
FAQ

What is a 'scored-memory loop'?

It's how the AI accesses and uses its existing knowledge to tackle new problems.

What is a 'brain_frame'?

It's a summary of the AI's current thinking, showing what it understood and what it predicts next. It asks for your help to interpret it.

What does 'deliberate' mean?

It means to think carefully about something. In this context, the AI is asking you to think with it about the 'brain_frame'.

Details

Original description by the author

Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2025
License
MIT
Updated
10/15/2026

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