Agent skill

Conversation To Skill

by Undertone0809 in Undertone0809/rudder

Turn the current conversation's workflow into a reusable agent skill.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Conversation To Skill

skills CLI
$ npx skills add Undertone0809/rudder --skill conversation-to-skill -a claude-code

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

GitHub CLI
$ gh skill install Undertone0809/rudder conversation-to-skill --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/Undertone0809/rudder.git skills-src && mkdir -p .claude/skills && cp -r skills-src/server/resources/bundled-skills/conversation-to-skill .claude/skills/conversation-to-skill && 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
conversation-to-skill
GitHub stars
292
Token cost
~3.6k tokens
SKILL.md length
1,930 words
Files
22 (incl. scripts, references, assets)
Skills in repo
30
Repo updated
First seen
Licence
Apache-2.0

At a glance

Turn the current conversation's workflow into a reusable agent skill.

  • Works in 12 steps: Extract The Candidate Skill From The… → Separate Stable Pattern From Incidental… → Fill Gaps With Minimal Interview Or… → …
  • Wants to make a workflow reusable
  • SKILL.md covers Use This Skill For, Do Not Use This Skill For, Core Principles and Default Workflow, plus 4 more sections
  • Runs Python scripts from its folder

What it does

Conversation To Skill is an agent skill from Undertone0809/rudder. Turn the current conversation's workflow into a reusable agent skill. Use this whenever the user wants to make a workflow reusable, standardize a successful thread, package an agent capability, or convert an ad hoc process into a repeatable skill. Read the thread first, extract the stable pattern, decide whether the skill should live in ~/.agents/skills/<name or <project-path/.agents/skills/<name, write the skill, and when quality matters add lightweight evals and iteration instead of just transcribing the chat.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 26 other files, including scripts, reference files and assets (for example `agents/analyzer.md`, `agents/comparator.md` and `agents/grader.md`).

It sits in AI & LLM Engineering, covering LLM evaluation. The repository describes itself as: Open-source local Agent harness for self-improving agent teams: run agents, review work, and turn feedback into reusable skills. The licence is Apache-2.0.

When your agent uses it

  • Wants to make a workflow reusable
  • Standardize a successful thread
  • Package an agent capability
  • Convert an ad hoc process into a repeatable skill

Example prompts

  • “/conversation-to-skill”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Extract The Candidate Skill From The Current Thread
  2. Separate Stable Pattern From Incidental Context
  3. Fill Gaps With Minimal Interview Or Research
  4. Produce An Abstraction Brief Before Writing Files
  5. Challenge Weak Abstractions
  6. Decide Location, Shape, And Scope
  7. Write The Skill Like A Real Skill
  8. Use Clean Skill Structure
  9. Bundle Repeated Deterministic Work
  10. Add Evals With The Full Suite When The Skill Warrants Them
  11. Iterate Instead Of Fossilizing Bad Drafts
  12. Close With A Clear Hand-off

What it can do on your machine

Read from SKILL.md and the folder at commit e2ba0f1. 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 4 files in scripts/ (Python, from the files we listed), which the agent can run.

    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

Conversation To Skill loads about 3.6k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 137 tokens; SKILL.md has 1,930 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~137
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

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 Undertone0809/rudder at commit e2ba0f1, republished under its Apache-2.0 licence (© Undertone0809). 1,930 words, ~3,563 tokens.

Download SKILL.mdSave it as .claude/skills/conversation-to-skill/SKILL.md (or your agent's skills folder). This skill also uses 21 other files; get the full folder from GitHub.
name
conversation-to-skill
description
Turn the current conversation's workflow into a reusable agent skill. Use this whenever the user wants to make a workflow reusable, standardize a successful thread, package an agent capability, or convert an ad hoc process into a repeatable skill. Read the thread first, extract the stable pattern, decide whether the skill should live in `~/.agents/skills/<name>` or `<project-path>/.agents/skills/<name>`, write the skill, and when quality matters add lightweight evals and iteration instead of just transcribing the chat.

Conversation To Skill

This skill turns the work happening in the current conversation into a reusable agent skill.

Its job is not just to write SKILL.md. Its job is to identify the durable workflow, separate it from one-off thread noise, decide the right packaging and placement, and produce a skill that will actually help a future agent perform better.

When useful, this skill should borrow the practical methods of skill-creator: good descriptions, clean skill structure, eval-friendly organization, and an improve-via-feedback loop. When this skill owns evaluation, bundle the relevant toolchain locally under agents/, assets/, eval-viewer/, scripts/, and references/ so it stays self-contained instead of depending on another skill directory at runtime.

Use This Skill For

Use this skill when the user is trying to:

  • turn the current task or workflow into a reusable skill
  • capture a successful collaboration pattern for future runs
  • standardize how a class of tasks should be handled
  • extract a repeatable agent workflow from the current thread
  • package a reasoning framework, execution sequence, or artifact pattern into a skill
  • upgrade an existing draft skill so it is general, usable, and easier to trigger

Typical prompts:

  • "Turn what we're doing into a skill."
  • "I want this conversation to become an agent capability."
  • "Make this reusable for next time."
  • "Abstract this workflow into a Codex skill."
  • "This should be a standard operating pattern, not a one-off chat."
  • "Clean up this skill and make it actually reusable."

Do Not Use This Skill For

Do not use this skill when the user mainly wants:

  • a summary of the conversation without creating a reusable skill
  • immediate execution of the task with no abstraction step
  • a skill generated from multiple unseen threads you cannot inspect
  • a rigid template that blindly copies file paths, project names, or temporary constraints
  • a generic skill factory that ignores what was actually valuable in the conversation

If the conversation does not yet reveal a stable workflow, say that plainly and help the user clarify the reusable part first.

Core Principles

Capture The Repeatable Value

The skill should capture the repeatable value, not the accidental details.

A good abstraction preserves:

  • the job to be done
  • the trigger conditions
  • the critical inputs and outputs
  • the sequence of reasoning or execution
  • the judgment criteria that make the workflow valuable
  • the boundaries and non-goals

A bad abstraction copies:

  • temporary filenames
  • irrelevant project-specific paths
  • incidental tools that happened to be used once
  • order-of-operations that are not actually essential
  • user wording that does not generalize
Explain Why, Not Just What

Prefer instructions that explain why a step matters. Avoid brittle mandates unless the workflow truly requires them.

If you find yourself writing a long list of rigid commands with no reasoning, you are probably transcribing the thread instead of building a skill.

Choose The Smallest Useful Shape

Do not overbuild the skill. Use the smallest structure that preserves the capability:

  • SKILL.md only, when the workflow is mostly reasoning and sequencing
  • SKILL.md plus references/, when the skill needs domain guidance
  • SKILL.md plus scripts/, when deterministic repeated work should be bundled
  • SKILL.md plus evals/, when the skill benefits from repeatable testing
Decide Placement Before Writing Files

Pick the skill location before creating files so the paths stay stable:

  • Global: ~/.agents/skills/<skill-name>
  • Project-based: <project-path>/.agents/skills/<skill-name>

If the user wants a global skill to be discoverable by Codex immediately, also create:

  • ~/.codex/skills/<skill-name> as a symlink to the global skill directory

If you plan to run evals, place the workspace next to the skill directory as:

  • <skill-name>-workspace/

Default Workflow

Follow this sequence unless the user already provided enough structure.

1. Extract The Candidate Skill From The Current Thread

Read the current conversation first. Pull out the real workflow before asking the user to restate everything.

Capture:

  • what the user was trying to achieve
  • what sequence of steps the agent followed or should follow
  • which tools or artifacts mattered
  • what corrections or preferences the user introduced
  • what output the user actually wanted
  • what makes this reusable instead of one-off
2. Separate Stable Pattern From Incidental Context

Classify each detail into one of three buckets:

  • Core: must stay because the skill breaks without it
  • Contextual: useful examples or defaults, but not universal
  • Incidental: this-thread noise that should not be baked into the skill

Useful heuristic:

  • if the detail would still matter in six months on a different project, it is probably core
  • if it only mattered because of this repository, filename, or user phrasing, it is probably contextual or incidental
3. Fill Gaps With Minimal Interview Or Research

Do not ask the user to restate the whole workflow if the thread already tells you most of it. Only ask for the missing pieces that affect the resulting skill:

  • what this skill should enable the agent to do
  • when the skill should trigger
  • what output format or artifact the user expects
  • whether lightweight test prompts would help validate the result

If examples, edge cases, dependencies, or adjacent skills matter, gather that context before writing the final version.

4. Produce An Abstraction Brief Before Writing Files

Before generating the final skill, write a short abstraction brief for the user to review unless they already said to just build it.

Use this structure:

markdown
## Skill Intent
- Name:
- Goal:
- Why this should exist:

## Trigger
- Use when:
- Do not use when:

## Inputs
- Required inputs:
- Optional inputs:

## Outputs
- Main deliverable:
- Secondary artifacts:

## Workflow
1. ...
2. ...
3. ...

## Judgment Rules
- What must stay true:
- What to avoid:

## Open Questions
- ...

If the conversation already settles these points, keep the brief short and move on.

5. Challenge Weak Abstractions

Do not act like a passive stenographer. If the proposed skill is overfit, under-scoped, or missing the real judgment logic, say so and correct it.

Common failure modes to call out:

  • "This is a transcript, not a skill."
  • "These instructions depend on this exact repo, but the user asked for a global skill."
  • "The workflow says what to do, but not how to decide when a step is necessary."
  • "The description would under-trigger because it only names one phrasing."
  • "This skill repeats manual work that should be moved into a bundled script."
6. Decide Location, Shape, And Scope

Make these decisions before writing:

  • whether the skill is global or project-based
  • whether to preserve an existing name and directory
  • whether SKILL.md alone is enough
  • whether the skill needs references/, scripts/, assets/, or evals/
  • whether a sibling workspace should be created for testing

Default location rules:

  • Global skill: ~/.agents/skills/<skill-name>
  • Project-based skill: <project-path>/.agents/skills/<skill-name>

If updating an existing skill, preserve the directory name and frontmatter name unless the user asked for a rename.

7. Write The Skill Like A Real Skill

When writing SKILL.md, include:

  • frontmatter with name and a trigger-oriented description
  • what the skill is for
  • when to use it and when not to use it
  • the default workflow
  • output expectations
  • edge cases and boundaries when they materially affect quality

Bring in the skill-creator quality bar here:

  • make the description a little aggressive so hosts do not under-trigger it
  • include both what the skill does and the contexts that should trigger it
  • prefer imperative instructions
  • explain the reasoning behind important steps
  • keep the file readable; if it grows too large, move detail into references
Show full SKILL.md (784 more words)Show less
8. Use Clean Skill Structure

Prefer this structure when it helps:

text
skill-name/
├── SKILL.md
├── references/
├── scripts/
├── assets/
└── evals/

Use progressive disclosure:

  1. metadata in frontmatter should be enough to trigger the skill
  2. SKILL.md should explain the workflow clearly
  3. large reference material should be loaded only when relevant

When the skill supports multiple variants or domains, organize references by variant and tell the future agent which file to read for which case.

If the user wants more than a draft, or explicitly asks for testing, benchmarking, or trigger tuning, add local references that capture the evaluation workflow instead of leaving that logic implicit.

If the workflow needs actual tooling, prefer bundling it inside this skill rather than pointing at another repo's copy.

9. Bundle Repeated Deterministic Work

If multiple runs of the workflow would obviously repeat the same deterministic steps, package that work into scripts/ instead of forcing future agents to reinvent it every time.

Good candidates:

  • file conversions
  • formatting helpers
  • benchmark aggregation
  • schema validation
  • packaging helpers

Do not add scripts just because you can. Only bundle work that is repeated, stable, and cheaper to reuse than to re-derive.

10. Add Evals With The Full Suite When The Skill Warrants Them

Not every conversation-derived skill needs evals. But if the skill produces objectively testable outputs, if the user asks for benchmarking, or if you are iterating on quality instead of just drafting, do not stop at a hand-wavy "light eval."

When you choose to evaluate, use the full evaluation suite:

  • create 2-3 realistic test prompts and store them in evals/evals.json
  • create a sibling <skill-name>-workspace/ for iteration outputs
  • compare with_skill against without_skill or an old snapshot
  • draft assertions while runs are executing
  • capture timing and grading artifacts per run
  • aggregate results into a benchmark
  • generate a reviewable viewer artifact for the human
  • read feedback, improve the skill, and rerun into the next iteration

The detailed procedure lives in:

  • references/evaluation-suite.md for test execution, grading, benchmark aggregation, feedback, and iteration
  • references/description-optimization.md for trigger-query generation and description tuning
  • references/compatibility.md and references/schemas.md for host differences and file formats

The local support toolchain lives in:

  • agents/ for grader, comparator, and analyst instructions
  • assets/ for review UI assets
  • eval-viewer/ for viewer generation
  • scripts/ for aggregation, optimization, validation, and packaging

If you decide evals are needed, read those reference files before proceeding.

Prefer qualitative review for subjective skills. Prefer assertions and benchmarks for objective skills.

11. Iterate Instead Of Fossilizing Bad Drafts

If the first draft feels narrow, ambiguous, or weakly triggered, improve it. Useful improvement passes include:

  • description tuning for better triggering
  • removing overfit instructions
  • generalizing from user feedback
  • turning repeated ad hoc steps into bundled resources
  • simplifying sections that make the model do busywork

Do not force a full benchmark loop if the user only wants a draft. But do not pretend the first draft is final if it clearly is not.

12. Close With A Clear Hand-off

After creating or revising the skill, report:

  • the chosen skill name
  • whether it is global or project-based
  • the final path
  • whether a Codex symlink was created
  • whether eval files or a workspace were created
  • what still needs evaluation, if anything

Naming Guidance

Choose names that are short, clear, and capability-oriented.

Prefer names like:

  • conversation-to-skill
  • workflow-standardizer
  • task-to-playbook

Avoid names that depend on this thread's temporary wording unless the user explicitly wants that.

If updating an existing skill, preserve the existing directory name and frontmatter name unless the user asked for a rename.

Output Format

Unless the user wants files written immediately, start with:

  1. a compact abstraction brief
  2. the proposed skill name and placement
  3. any risks of overfitting or under-specification

If the user asks to proceed, then write the files.

When the user already said "build it" or "just make it", go straight from the brief into file creation in the same turn.

If you also set up evals, mention:

  • the test prompts
  • what is being compared
  • where the reviewable output lives

Quality Bar

The resulting skill should make a future agent meaningfully better at the task.

That usually means it captures at least one of these:

  • a reusable workflow
  • a reusable decision framework
  • a reusable artifact format
  • a reusable boundary or escalation rule

Strong skills often also have at least one of these:

  • a well-targeted description that triggers reliably
  • a clean placement and file layout
  • a bundled helper for repeated deterministic work
  • a full eval loop that makes improvements testable

If it captures none of those, it is probably not a real skill yet.

Safety And Boundaries

Do not create misleading, hostile, or surprise-heavy skills. The skill should do what its description honestly suggests.

Do not package instructions that facilitate unauthorized access, harmful automation, or disguised exfiltration.

Roleplay, stylistic framing, and benign workflow abstraction are fine.

© Undertone0809, Apache-2.0. 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 21 other files (scripts, references, assets) in server/resources/bundled-skills/conversation-to-skill of Undertone0809/rudder.

  • SKILL.md
  • LICENSE.txt
  • agents/analyzer.md
  • agents/comparator.md
  • agents/grader.md
  • assets/eval_review.html
  • eval-viewer/generate_review.py
  • eval-viewer/viewer.html
  • references/compatibility.md
  • references/description-optimization.md
  • references/evaluation-suite.md
  • references/schemas.md
  • scripts/__init__.py
  • scripts/aggregate_benchmark.py
  • scripts/generate_report.py
  • scripts/improve_description.py
  • … and 6 more

Open the folder on GitHubat commit e2ba0f1

Compare with similar skills

Conversation To Skill 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.

Conversation To Skill compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Conversation To Skill this skillUndertone0809/rudder292—~3.6kAutomated safety check: PassApache-2.0
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
Hugging Face Local Model Evalshuggingface/skills11k2 repos~1.6kAutomated safety check: PassApache-2.0
Looperksimback/looper710—~2.7kAutomated safety check: NotesMIT
Agent Eval Engineeringlangchain-ai/langchain-skills1.3k—~4kAutomated safety check: PassMIT
Quality FlywheelGoogleCloudPlatform/vertex-ai-samples792—~2kAutomated safety check: PassApache-2.0

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Questions about Conversation To Skill

What does Conversation To Skill do?

Turn the current conversation's workflow into a reusable agent skill. Conversation To Skill is an agent skill from Undertone0809/rudder. Turn the current conversation's workflow into a reusable agent skill.

When should I use Conversation To Skill?

Conversation To Skill fits situations like: wants to make a workflow reusable; standardize a successful thread; package an agent capability; convert an ad hoc process into a repeatable skill.

How do I install Conversation To Skill in Claude Code?

Run `npx skills add Undertone0809/rudder --skill conversation-to-skill -a claude-code`. Or copy the skill folder (server/resources/bundled-skills/conversation-to-skill in Undertone0809/rudder) into .claude/skills/conversation-to-skill in your project. Claude Code loads it when a task matches its description.

How do I install Conversation To Skill in Codex?

Run `npx skills add Undertone0809/rudder --skill conversation-to-skill -a codex`. Or copy the skill folder (server/resources/bundled-skills/conversation-to-skill in Undertone0809/rudder) into .agents/skills/conversation-to-skill in your project. Codex loads it when a task matches its description.

Can I use Conversation To Skill 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 Undertone0809/rudder --skill conversation-to-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/conversation-to-skill, .gemini/skills/conversation-to-skill, .github/skills/conversation-to-skill and .opencode/skills/conversation-to-skill in your project.

What does Conversation To Skill need to run?

Going by SKILL.md and its folder, Conversation To Skill needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Conversation To Skill 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 Conversation To Skill 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 Conversation To Skill use?

Conversation To Skill is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Conversation To Skill use?

About 3.6k tokens (SKILL.md is roughly 14k 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 6.8k tokens, read only when the agent opens those files.

What are the alternatives to Conversation To Skill?

Skills that share tags, products or a category with Conversation To Skill: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars), Looper (ksimback/looper, 710 stars) and Agent Eval Engineering (langchain-ai/langchain-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Conversation To Skill?

Undertone0809 (a GitHub user) maintains it in Undertone0809/rudder, which has 292 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 9, 2026.

Source: Undertone0809/rudder on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.