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Agent skill

Extended Thinking Architect

by borghei in borghei/Claude-Skills

This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning…

MITAuto-check passedBusiness, Finance & HR

Install Extended Thinking Architect

skills CLI
$ npx skills add borghei/Claude-Skills --skill extended-thinking-architect -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills extended-thinking-architect --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/extended-thinking-architect .claude/skills/extended-thinking-architect && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
extended-thinking-architect
GitHub stars
881
Token cost
~1.4k tokens
SKILL.md length
599 words
Files
5 (incl. scripts, references)
Skills in repo
349
Repo updated
First seen
Licence
MIT

At a glance

This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning…

  • Works in 5 steps: Identify the task type and whether the… → Run reasoning_budget_advisor.py with the… → If the result is prompt-first, fix the… → …
  • Asks to decide reasoning effort
  • SKILL.md covers Overview, Clarify First, Quick Start and Tools Overview, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

Extended Thinking Architect is an agent skill from borghei/Claude-Skills. This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning model".

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/reasoning-budget-patterns.md`, `references/when-to-use-extended-thinking.md` and `scripts/reasoning_budget_advisor.py`).

It sits in Business, Finance & HR. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Asks to decide reasoning effort
  • Set a thinking budget
  • To use extended thinking
  • Tune reasoning vs cost

Example prompts

  • “decide reasoning effort”
  • “set a thinking budget”
  • “when to use extended thinking”
  • “/extended-thinking-architect”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Identify the task type and whether the output is verifiable (ground truth or a checker exists).
  2. Run reasoning_budget_advisor.py with the error cost, step count, ambiguity, and latency budget.
  3. If the result is prompt-first, fix the prompt/spec (clarify, add examples) before spending any reasoning, then re-run.
  4. If the result is cheaper-model, route to a smaller/faster model and invest the savings in a better prompt.
  5. Otherwise adopt the recommended effort, note the cost multiplier, and set a per-call budget cap.

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Extended Thinking Architect loads about 1.4k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 599 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 599 words, ~1,426 tokens.

Download SKILL.mdSave it as .claude/skills/extended-thinking-architect/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
extended-thinking-architect
description
This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning model".
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
engineering
metadata.domain
ai-engineering
metadata.updated
2026-06-29
metadata.tags
extended-thinking, reasoning-effort, llm, cost-optimization, agents

Extended Thinking Architect

Category: Engineering Domain: AI Engineering

Overview

The Extended Thinking Architect skill helps you decide when an LLM task should spend a reasoning/thinking budget, how much (no-thinking / low / medium / high), and when the better move is a cheaper model with a sharper prompt instead. It turns task signals — error cost, ambiguity, step count, latency budget — into a deterministic recommendation with a rough cost multiplier, and allocates effort across the phases of an agent loop so you front-load reasoning where it pays and avoid runaway budgets.

Clarify First

Before recommending an effort level, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Task type & verifiability — what the model is actually doing (extraction, classification, planning, code-debug, math…) and whether the output is checkable (sets --task-type and --verifiable)
  • Cost of a wrong answer — how expensive a bad output is, plus the latency budget the task must fit (sets --error-cost and --latency-budget)
  • Shape of the work — how many reasoning/tool steps are expected and how ambiguous the request is (sets --steps and --ambiguity)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

bash
# Recommend a reasoning effort level for a single task
python scripts/reasoning_budget_advisor.py --task-type code-debug \
  --error-cost high --steps 4 --ambiguity low --latency-budget interactive

# A cheap, high-volume classification task — expect "cheaper model + better prompt"
python scripts/reasoning_budget_advisor.py --task-type classification \
  --error-cost low --latency-budget realtime --json

# Allocate reasoning effort across the phases of an agent loop
python scripts/reasoning_loop_allocator.py --difficulty high --steps 8 \
  --max-budget-multiplier 30

# Tight-latency loop — see effort capped per phase
python scripts/reasoning_loop_allocator.py --difficulty medium --steps 5 --realtime --json

Tools Overview

ToolPurposeKey Flags
reasoning_budget_advisor.pyRecommend an effort level (none/low/medium/high) or "prompt-first / cheaper-model" for one task, with rationale + cost multiplier--task-type, --error-cost, --steps, --ambiguity, --latency-budget, --verifiable, --json
reasoning_loop_allocator.pyAllocate reasoning effort across agent-loop phases (plan/act/observe/recover/finalize) under a total budget cap--difficulty, --steps, --max-budget-multiplier, --realtime, --json

Workflows

Choosing Effort for a New Task
  1. Identify the task type and whether the output is verifiable (ground truth or a checker exists).
  2. Run reasoning_budget_advisor.py with the error cost, step count, ambiguity, and latency budget.
  3. If the result is prompt-first, fix the prompt/spec (clarify, add examples) before spending any reasoning, then re-run.
  4. If the result is cheaper-model, route to a smaller/faster model and invest the savings in a better prompt.
  5. Otherwise adopt the recommended effort, note the cost multiplier, and set a per-call budget cap.
Show full SKILL.md (257 more words)Show less
Budgeting Reasoning Across an Agent Loop
  1. Estimate overall task difficulty and the expected number of steps.
  2. Run reasoning_loop_allocator.py to get per-phase effort (front-loaded at plan/recover, thin at act/observe).
  3. Apply the total budget cap as a hard stop so a stuck loop cannot run away.
  4. Instrument per-phase token spend; if observe/act phases consume high reasoning, that is an overthinking signal — clamp them.

Reference Documentation

  • When to Use Extended Thinking - Decision matrix of task classes where reasoning pays off vs. is wasted, interaction with tool use and agent loops, budget guards, overthinking failure modes, and eval signals.
  • Reasoning Budget Patterns - Allocation patterns, escalation ladders, caps and circuit breakers, and the cost/quality/latency tradeoff model.

Common Patterns

When Reasoning Pays Off
  • Multi-step deduction with a verifiable answer (math, constraint solving, debugging from a stack trace)
  • Planning and decomposition before a long agent run — front-load thinking once, not on every tool call
  • High error-cost decisions where a wrong answer is expensive to detect or undo
When Reasoning Is Wasted
  • Extraction, classification, and formatting — deterministic mappings, not deduction; a cheaper model usually wins
  • Underspecified requests — extra thinking confidently elaborates on the wrong goal; fix the prompt first
  • Realtime/latency-tight paths where thinking tokens blow the budget more than they improve quality
Guarding the Budget
  • Set a per-call effort cap and a loop-level total cap (e.g. a multiple of one no-thinking call)
  • Escalate effort only on failure (retry at higher effort), never start high "to be safe"
  • Treat reasoning spent on trivial sub-steps as a regression — alert on per-phase token spend

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in engineering/extended-thinking-architect of borghei/Claude-Skills.

  • SKILL.md
  • references/reasoning-budget-patterns.md
  • references/when-to-use-extended-thinking.md
  • scripts/reasoning_budget_advisor.py
  • scripts/reasoning_loop_allocator.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

Extended Thinking Architect next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Theme Detectortradermonty/claude-trading-skills3k2 repos~4.9kAutomated safety check: PassMIT
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Categories

Questions about Extended Thinking Architect

What does Extended Thinking Architect do?

This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning…. Extended Thinking Architect is an agent skill from borghei/Claude-Skills. This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning model".

When should I use Extended Thinking Architect?

Extended Thinking Architect fits situations like: asks to decide reasoning effort; set a thinking budget; to use extended thinking; tune reasoning vs cost.

How do I install Extended Thinking Architect in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill extended-thinking-architect -a claude-code`. Or copy the skill folder (engineering/extended-thinking-architect in borghei/Claude-Skills) into .claude/skills/extended-thinking-architect in your project. Claude Code loads it when a task matches its description.

How do I install Extended Thinking Architect in Codex?

Run `npx skills add borghei/Claude-Skills --skill extended-thinking-architect -a codex`. Or copy the skill folder (engineering/extended-thinking-architect in borghei/Claude-Skills) into .agents/skills/extended-thinking-architect in your project. Codex loads it when a task matches its description.

Can I use Extended Thinking Architect in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill extended-thinking-architect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extended-thinking-architect, .gemini/skills/extended-thinking-architect, .github/skills/extended-thinking-architect and .opencode/skills/extended-thinking-architect in your project.

What does Extended Thinking Architect need to run?

Going by SKILL.md and its folder, Extended Thinking Architect needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Extended Thinking Architect access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Extended Thinking Architect safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Extended Thinking Architect use?

Extended Thinking Architect is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Extended Thinking Architect use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.

What are the alternatives to Extended Thinking Architect?

Skills that share tags, products or a category with Extended Thinking Architect: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Stock API (zhangxiangliang/stock-api, 2k stars) and Theme Detector (tradermonty/claude-trading-skills, 3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Extended Thinking Architect?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 881 GitHub stars. The repository holds 349 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.