Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add makoMakoGo/fish-claude --skill gemini-deep-reasoninggit clone --depth 1 https://github.com/makoMakoGo/fish-claudeWrote 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/makomakogo/fish-claude/gemini-deep-reasoning)<a href="https://agentmods.dev/skills/makomakogo/fish-claude/gemini-deep-reasoning"><img src="https://agentmods.dev/badge/skills/makomakogo/fish-claude/gemini-deep-reasoning/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/makomakogo/fish-claude/gemini-deep-reasoning"><img src="https://agentmods.dev/badge/skills/makomakogo/fish-claude/gemini-deep-reasoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 44 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00052 | $0.00898 |
| Opus 5 | $0.00026 | $0.00449 |
| Sonnet 5 | $0.00010 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
gemini-deep-reasoning 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 11d 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.
What it actually says
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:
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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 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.
- 11d ago First seen · 52 lines · 52 tokens per session scan A 340cd1e5e6c3
gemini-deep-reasoning is a skill published in the GitHub repository makoMakoGo/fish-claude (169 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 898 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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