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Self Improve With Tinyfish

OrganizationPopular
tinyfish-io
self-improve-with-tinyfish

Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.

Overview

Publishertinyfish-io
Repositorytinyfish-cookbook
Skill nameself-improve-with-tinyfish
Stars
2.2K
Forks
333
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tinyfish-io on GitHub. Read the source before you install it.

Installation

Install the Self Improve With Tinyfish AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tinyfish-io/tinyfish-cookbook.git /tmp/tinyfish-cookbook
mkdir -p .claude/skills
cp -r /tmp/tinyfish-cookbook/skills/tinyskill .claude/skills/self-improve-with-tinyfish
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Self Improve With Tinyfish in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Self Improve With Tinyfish on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Self Improve With Tinyfish is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Self-Improve With TinyFish

When To Use

Use this skill when the user asks Hermes to:

  • Learn a new tool, API, framework, workflow, or domain for future tasks.
  • Create, install, or improve a reusable Hermes skill.
  • Research official docs and examples before writing procedural memory.
  • "Teach yourself," "upgrade yourself," or "make a skill for this."

Do not use this skill for ordinary one-off answers unless the user explicitly wants a saved skill.

Pre-flight Check (REQUIRED)

Before making any TinyFish call, determine which mode to use.

1. Check for CLI:

bash
which tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_FOUND"

2. If CLI is found, check auth:

bash
tinyfish auth status

If authenticated, use CLI mode for all searches and fetches below.

3. If CLI is not found, check for API key:

bash
test -n "$TINYFISH_API_KEY" && echo "API_KEY_AVAILABLE" || echo "TINYFISH_API_KEY is not set"

If the API key is available, use API mode for all searches and fetches below.

4. If neither CLI nor API key is available, stop and tell the user:

Install the TinyFish CLI: npm install -g @tiny-fish/cli Then authenticate: tinyfish auth login

Or set TINYFISH_API_KEY in your environment. Get a key at: https://agent.tinyfish.ai/api-keys

Do NOT proceed until one mode is confirmed.


Core Workflow

  1. Acknowledge the learning request.

    • Send a short progress update: I'll research this with TinyFish and create a reusable skill.
    • Extract a clean skill topic from the user request.
    • Preserve constraints such as "check official docs," "cover all endpoints," "focus on errors," or "make it production-ready."
  2. Search with TinyFish.

    • Use the CLI or API (whichever passed pre-flight).
    • Run multiple targeted searches.
    • Prefer official docs and authoritative examples first.
    • Do not invent URLs.
  3. Analyze search results.

    • Review titles, snippets, URLs, site names, and ranks.
    • Decide if the current sources cover setup, main workflow, edge cases, and validation.
    • If coverage is weak, run follow-up searches before fetching.
  4. Fetch selected URLs.

    • Use TinyFish Fetch for known URLs.
    • Fetch 3-8 strong sources by default.
    • Prefer markdown output.
    • Continue if one URL fails; replace critical failed sources with another search result.
  5. Write the new skill.

    • Create a focused SKILL.md.
    • Make it procedural and reusable.
    • Include gotchas, defaults, validation, and references.
    • Do not dump raw docs.
  6. Install and verify.

    • Save the generated skill using Hermes skill management.
    • Verify with skill_view or skills list.
    • Tell the user only the skill name and that it can be used going forward.

Search Pattern

Use TinyFish Search when you need ranked web results, snippets, and URLs.

CLI mode

bash
tinyfish search query "{topic} official documentation" --pretty

Run multiple searches:

bash
tinyfish search query "{topic} official documentation"
tinyfish search query "{topic} API reference"
tinyfish search query "{topic} quickstart examples"
tinyfish search query "site:github.com {topic} examples issues"
tinyfish search query "{topic} common errors best practices"

For API skills, add:

bash
tinyfish search query "{topic} authentication"
tinyfish search query "{topic} SDK reference"
tinyfish search query "{topic} rate limits errors"

For framework skills, add:

bash
tinyfish search query "{topic} production best practices"
tinyfish search query "{topic} testing debugging deployment"

API mode (fallback)

bash
python3 - <<'PY'
import json
import os
import urllib.parse
import urllib.request

query = "Remotion official docs best practices"
api_key = os.environ["TINYFISH_API_KEY"]
url = "https://api.search.tinyfish.ai?query=" + urllib.parse.quote(query)

req = urllib.request.Request(url, headers={"X-API-Key": api_key})
with urllib.request.urlopen(req, timeout=15) as res:
    data = json.load(res)

for r in data.get("results", [])[:10]:
    print(json.dumps({
        "position": r.get("position"),
        "site_name": r.get("site_name"),
        "title": r.get("title"),
        "url": r.get("url"),
        "snippet": r.get("snippet"),
    }, ensure_ascii=False))
PY

Run the same query variations listed in CLI mode above, substituting the query variable each time.


Search Analysis Loop

After each search pass, decide:

  • Do I have at least one official source?
  • Do I have setup or authentication covered?
  • Do I have the main workflow covered?
  • Do I have enough code or command examples?
  • Do I have likely mistakes and validation checks?
  • Are there duplicate or low-value sources I should ignore?

If no, send a short user update and run targeted follow-up searches.

Example update:

text
I found the official docs and examples. I'm checking for pitfalls and validation guidance before writing the skill.

Source Selection Rules

Prefer sources in this order:

  1. Official docs, API references, quickstarts.
  2. Official examples, cookbook repos, templates.
  3. GitHub issues/discussions with concrete failure modes.
  4. Stack Overflow answers for specific errors.
  5. High-quality technical blog posts that fill real gaps.

Reject:

  • Marketing pages without implementation detail.
  • Duplicate docs pages.
  • Outdated sources when official docs exist.
  • Shallow posts that only repeat generic concepts.

Default to 3-8 fetched URLs total.


Fetch Pattern

Use TinyFish Fetch when you already know the URLs and need clean extracted content.

CLI mode

bash
tinyfish fetch content get --format markdown "https://example.com/docs" "https://example.com/quickstart"
  • Accepts multiple URLs in a single call — they are fetched in parallel server-side.
  • --format markdown (default) returns clean readable text.
  • Add --links to include extracted links from each page.
bash
tinyfish fetch content get --format markdown --links \
  "https://example.com/docs" \
  "https://example.com/quickstart" \
  "https://example.com/api-reference"

API mode (fallback)

Fetch up to 10 URLs per request:

bash
python3 - <<'PY'
import json
import os
import urllib.request

api_key = os.environ["TINYFISH_API_KEY"]
urls = [
    "https://example.com/docs",
    "https://example.com/quickstart",
]

body = json.dumps({
    "urls": urls,
    "format": "markdown",
}).encode()

req = urllib.request.Request(
    "https://api.fetch.tinyfish.ai",
    data=body,
    headers={
        "X-API-Key": api_key,
        "Content-Type": "application/json",
    },
    method="POST",
)

with urllib.request.urlopen(req, timeout=150) as res:
    data = json.load(res)

for page in data.get("results", []):
    print("\n---SOURCE---")
    print("URL:", page.get("url"))
    print("FINAL:", page.get("final_url"))
    print("TITLE:", page.get("title"))
    print("TEXT:")
    print((page.get("text") or "")[:12000])

for err in data.get("errors", []):
    print("\n---FETCH ERROR---")
    print(err)
PY

Fetch returns per-URL errors in errors[]. Do not fail the whole run because one source failed.


Writing The Generated Skill

The generated skill must be raw Markdown beginning with frontmatter:

markdown
---
name: concise-kebab-case-name
description: Third-person description of what the skill does and when to use it.
---

The description must include:

  • What the skill does.
  • When Hermes should use it.

Use this structure unless the topic requires a better one:

markdown
# Skill Title

## When To Use

## Core Workflow

## Defaults

## Key Patterns

## Gotchas

## Validation

## References

Write for future Hermes behavior, not for a human tutorial.

Include:

  • Exact commands or code only when they change execution.
  • Defaults and decision rules.
  • Gotchas that Hermes is likely to miss.
  • Validation checks before finishing.
  • Source URLs in References.

Avoid:

  • Raw search dumps.
  • Long copied docs sections.
  • Generic "best practices" filler.
  • Unverified claims.
  • Menus of options without a recommended default.

Installing The Skill

After writing the skill:

  1. Save it using Hermes skill management.
  2. Verify the skill exists with skill_view or equivalent.
  3. If verification fails, repair the frontmatter or skill path.
  4. Send the user a concise completion message.

Completion message:

text
Done, I added the <skill-name> skill and can use it going forward.

Do not send the full SKILL.md unless the user asks.

User Progress Updates

During long runs, send concise updates:

text
I'll research this with TinyFish and create a reusable skill.
Searching official docs and examples...
I found 6 candidate sources and am checking coverage.
Fetching the best sources...
Writing the skill...
Installing and verifying it...
Done, I added the skill and can use it going forward.

Do not expose raw JSON, terminal logs, or full source text in chat.

Validation Checklist

Before finalizing, confirm:

  • Pre-flight check passed (CLI or API key available).
  • Search results included at least one authoritative source.
  • Fetched content was actually used.
  • Frontmatter has name and description.
  • Skill name is kebab-case.
  • Skill includes workflow, defaults, gotchas, validation, and references.
  • The saved skill can be viewed by Hermes.

References

Frequently asked questions

What does the Self Improve With Tinyfish AI skill do?

Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.

Why use Self Improve With Tinyfish on TypingMind?

Because you install it once and use it with any model. Self Improve With Tinyfish is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Self Improve With Tinyfish in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tinyskill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Self Improve With Tinyfish?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Self Improve With Tinyfish?

As many as you like. As long as a model supports skills, you can use Self Improve With Tinyfish with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Self Improve With Tinyfish AI skill free?

Yes. It is published on GitHub by tinyfish-io under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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