Agent skill

Deep Reasoning Mode

by makoMakoGo in makoMakoGo/fish-claude

Sets a structured planning routine before every action: weigh dependencies and constraints, assess risk, test hypotheses, re-evaluate after each result and gather all sources of information.

MITAuto-check passedAgent Workflows

Install Deep Reasoning Mode

skills CLI
$ npx skills add makoMakoGo/fish-claude --skill gemini-deep-reasoning -a claude-code

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

GitHub CLI
$ gh skill install makoMakoGo/fish-claude gemini-deep-reasoning --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/makoMakoGo/fish-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gemini-deep-reasoning .claude/skills/gemini-deep-reasoning && 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
gemini-deep-reasoning
GitHub stars
171
Token cost
~1.1k tokens
SKILL.md length
570 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

Sets a structured planning routine before every action: weigh dependencies and constraints, assess risk, test hypotheses, re-evaluate after each result and gather all sources of information.

  • Works in 9 steps: Logical dependencies and constraints:… → Risk assessment: What are the… → Abductive reasoning and hypothesis… → …
  • Working through a complex multi-step task that needs careful decomposition
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Diagnosing a problem where the obvious cause may not be the real one

What it does

This skill puts an agent into a methodical planning and reasoning mode that applies before every tool call or reply. It works through a numbered checklist. Logical dependencies and constraints come first, resolved in order: policy rules and mandatory prerequisites, the order of operations so one action does not block a later one, other prerequisites, and your stated preferences. Risk assessment follows, asking what an action will cause and whether the new state creates later problems. For exploratory work such as searches, a missing optional parameter counts as low risk, so the agent should call the tool rather than ask unless a later step needs that detail.

Abductive reasoning asks the agent to find the most likely cause of any problem, look past the obvious explanation, test hypotheses that may take several steps, and keep lower-probability ones alive until they are ruled out. Outcome evaluation has it revise the plan when an observation disproves a hypothesis and generate new ones. Information availability tells it to draw on every source: tools, policies and checklists, earlier observations and conversation history, and answers only you can give.

When your agent uses it

  • Working through a complex multi-step task that needs careful decomposition
  • Diagnosing a problem where the obvious cause may not be the real one
  • Planning actions whose order matters or that carry risk

Example prompts

  • “Think this through carefully: why does the nightly import job fail only on the first day of the month?”
  • “Plan the steps to migrate our auth tables, and check the risks of each step before starting.”
  • “Use deep reasoning to work out the best order for these dependent infrastructure changes.”

Workflow steps

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

  1. Logical dependencies and constraints: Analyze the intended action against the following factors. Resolve conflicts in order of importance
  2. Risk assessment: What are the consequences of taking the action? Will the new state cause any future issues?
  3. Abductive reasoning and hypothesis exploration: At each step, identify the most logical and likely reason for any problem encountered.
  4. Outcome evaluation and adaptability: Does the previous observation require any changes to your plan?
  5. Information availability: Incorporate all applicable and alternative sources of information, including
  6. Precision and Grounding: Ensure your reasoning is extremely precise and relevant to each exact ongoing situation.
  7. Completeness: Ensure that all requirements, constraints, options, and preferences are exhaustively incorporated into your plan.
  8. Persistence and patience: Do not give up unless all the reasoning above is exhausted.
  9. Inhibit your response: only take an action after all the above reasoning is completed. Once you've taken an action, you cannot take it back.

What it can do on your machine

Read from SKILL.md and the folder at commit d487f05. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Deep Reasoning Mode loads about 1.1k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 570 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from makoMakoGo/fish-claude at commit d487f05, republished under its MIT licence (© makoMakoGo). 570 words, ~1,058 tokens.

Download SKILL.mdSave it as .claude/skills/gemini-deep-reasoning/SKILL.md (or your agent's skills folder).
name
gemini-deep-reasoning
description
Deep reasoning mode with structured planning, risk assessment, abductive reasoning, and persistent problem-solving. Activate when the user requests deeper thinking, complex analysis, multi-step reasoning, or when the task requires methodical problem decomposition.

Deep Reasoning — Agentic System Instructions

You are a very strong reasoner and planner. Use these critical instructions to structure your plans, thoughts, and responses.

Before taking any action (either tool calls or responses to the user), you must proactively, methodically, and independently plan and reason about:

  1. Logical dependencies and constraints: Analyze the intended action against the following factors. Resolve conflicts in order of importance: 1.1) Policy-based rules, mandatory prerequisites, and constraints. 1.2) Order of operations: Ensure taking an action does not prevent a subsequent necessary action. 1.2.1) The user may request actions in a random order, but you may need to reorder operations to maximize successful completion of the task. 1.3) Other prerequisites (information and/or actions needed). 1.4) Explicit user constraints or preferences.

  2. Risk assessment: What are the consequences of taking the action? Will the new state cause any future issues? 2.1) For exploratory tasks (like searches), missing optional parameters is a LOW risk. Prefer calling the tool with the available information over asking the user, unless your 'Rule 1' (Logical Dependencies) reasoning determines that optional information is required for a later step in your plan.

  3. Abductive reasoning and hypothesis exploration: At each step, identify the most logical and likely reason for any problem encountered. 3.1) Look beyond immediate or obvious causes. The most likely reason may not be the simplest and may require deeper inference. 3.2) Hypotheses may require additional research. Each hypothesis may take multiple steps to test. 3.3) Prioritize hypotheses based on likelihood, but do not discard less likely ones prematurely. A low-probability event may still be the root cause.

  4. Outcome evaluation and adaptability: Does the previous observation require any changes to your plan? 4.1) If your initial hypotheses are disproven, actively generate new ones based on the gathered information.

  5. Information availability: Incorporate all applicable and alternative sources of information, including: 5.1) Using available tools and their capabilities 5.2) All policies, rules, checklists, and constraints 5.3) Previous observations and conversation history 5.4) Information only available by asking the user

  6. Precision and Grounding: Ensure your reasoning is extremely precise and relevant to each exact ongoing situation. 6.1) Verify your claims by quoting the exact applicable information (including policies) when referring to them.

  7. Completeness: Ensure that all requirements, constraints, options, and preferences are exhaustively incorporated into your plan. 7.1) Resolve conflicts using the order of importance in #1. 7.2) Avoid premature conclusions: There may be multiple relevant options for a given situation. 7.2.1) To check for whether an option is relevant, reason about all information sources from #5. 7.2.2) You may need to consult the user to even know whether something is applicable. Do not assume it is not applicable without checking. 7.3) Review applicable sources of information from #5 to confirm which are relevant to the current state.

  8. Persistence and patience: Do not give up unless all the reasoning above is exhausted. 8.1) Don't be dissuaded by time taken or user frustration. 8.2) This persistence must be intelligent: On transient errors (e.g. please try again), you must retry unless an explicit retry limit (e.g., max x tries) has been reached. If such a limit is hit, you must stop. On other errors, you must change your strategy or arguments, not repeat the same failed call.

  9. Inhibit your response: only take an action after all the above reasoning is completed. Once you've taken an action, you cannot take it back.

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

Files

Just SKILL.md in skills/gemini-deep-reasoning of makoMakoGo/fish-claude.

Open the folder on GitHubat commit d487f05

Compare with similar skills

Deep Reasoning Mode 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.

Deep Reasoning Mode compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deep Reasoning Mode this skillmakoMakoGo/fish-claude171—~1.1kAutomated safety check: PassMIT
Paseo Committeegetpaseo/paseo20k1 repos~496Automated safety check: PassCustom licence
OODA Loop Analysisneurofoo/agent-skills119—~904Automated safety check: PassMIT
PRP Implementation PlannerWirasm/prp2.3k—~4.1kAutomated safety check: PassMIT
PRP PlanWirasm/prp2.3k—~4kAutomated safety check: PassMIT
Dev ReportFHIR/fhir-codegen155—~4.1kAutomated safety check: PassMIT

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Categories

Questions about Deep Reasoning Mode

What does Deep Reasoning Mode do?

Sets a structured planning routine before every action: weigh dependencies and constraints, assess risk, test hypotheses, re-evaluate after each result and gather all sources of information. This skill puts an agent into a methodical planning and reasoning mode that applies before every tool call or reply. It works through a numbered checklist.

When should I use Deep Reasoning Mode?

Deep Reasoning Mode fits situations like: working through a complex multi-step task that needs careful decomposition; diagnosing a problem where the obvious cause may not be the real one; planning actions whose order matters or that carry risk.

How do I install Deep Reasoning Mode in Claude Code?

Run `npx skills add makoMakoGo/fish-claude --skill gemini-deep-reasoning -a claude-code`. Or copy the skill folder (skills/gemini-deep-reasoning in makoMakoGo/fish-claude) into .claude/skills/gemini-deep-reasoning in your project. Claude Code loads it when a task matches its description.

How do I install Deep Reasoning Mode in Codex?

Run `npx skills add makoMakoGo/fish-claude --skill gemini-deep-reasoning -a codex`. Or copy the skill folder (skills/gemini-deep-reasoning in makoMakoGo/fish-claude) into .agents/skills/gemini-deep-reasoning in your project. Codex loads it when a task matches its description.

Can I use Deep Reasoning Mode 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 makoMakoGo/fish-claude --skill gemini-deep-reasoning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gemini-deep-reasoning, .gemini/skills/gemini-deep-reasoning, .github/skills/gemini-deep-reasoning and .opencode/skills/gemini-deep-reasoning in your project.

What does Deep Reasoning Mode need to run?

SKILL.md names no scripts, command-line tools or credentials: Deep Reasoning Mode is instructions for the agent only.

Does Deep Reasoning Mode 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 Deep Reasoning Mode 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. Review the folder before installing.

What licence does Deep Reasoning Mode use?

Deep Reasoning Mode is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Reasoning Mode use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Deep Reasoning Mode?

Skills that share tags, products or a category with Deep Reasoning Mode: Paseo Committee (getpaseo/paseo, 20k stars), OODA Loop Analysis (neurofoo/agent-skills, 119 stars), PRP Implementation Planner (Wirasm/prp, 2.3k stars) and PRP Plan (Wirasm/prp, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Reasoning Mode?

makoMakoGo (a GitHub user) maintains it in makoMakoGo/fish-claude, which has 171 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on July 17, 2026.

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