implement-experiment

implement-experiment is a skill for Claude Code from TalonT-Org/AutoSkillit. It costs 24 tokens per session (3,407 once invoked), scanned A, original, MIT.

An implementation tool that builds the files and setup described in an approved experiment plan inside an isolated Git worktree. A worktree is a separate working copy of a repository.

In plain words
What is it for?
Use it to create experiment scripts, generated data, and other research files from an approved plan, with a separate commit for each phase.
Why use it?
It keeps experiment changes separate from the main codebase and organizes the work into reviewable phases.

Skill for Claude Code

Written for Claude Code: hooks in frontmatter. Also seen: mentions subagents; mentions Claude Code.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is WORKTREE_PATH="../worktrees/${WORKTREE_NAME}".

Part of the autoskillit plugin — 121 skills, 1 MCP server shipped together

Good fit Use it to create experiment scripts, generated data, and other research files from an approved plan, with a separate commit for each phase.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/TalonT-Org/AutoSkillit
agentmods
npx agentmods add skills/talont-org/autoskillit/implement-experiment

Made for: Claude Code.

Or install autoskillit, the plugin that ships this one along with the rest of its 121 skills, 1 MCP server.

Wrote 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.

agentmods badge for implement-experiment

README.md
[![agentmods](https://agentmods.dev/badge/skills/talont-org/autoskillit/implement-experiment/github.svg)](https://agentmods.dev/skills/talont-org/autoskillit/implement-experiment)
Your own site
<a href="https://agentmods.dev/skills/talont-org/autoskillit/implement-experiment"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/implement-experiment/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.

agentmods 80×15 button for implement-experiment

Your own site · 80×15
<a href="https://agentmods.dev/skills/talont-org/autoskillit/implement-experiment"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/implement-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,407 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Sonnet 5 · 7 Sept 2026 📄 Read the review
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.03407
Opus 5 $0.00012 $0.01703
Sonnet 5 $0.00005 $0.00681
Haiku 4.5 $0.00002 $0.00341

Measured 6d ago against content hash 1d91af1ab928, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

implement-experiment 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 6d 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.

src/autoskillit/skills_extended/implement-experiment/SKILL.md · 338 lines

How it starts

The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Implement Experiment Skill

Implement an experiment plan in an isolated git worktree. All experiment artifacts are created inside a single self-contained folder under research/. The worktree is left intact for the orchestrator to run the experiment, test, and merge separately.

This skill reads the experiment plan and follows its implementation phases. The plan specifies the directory layout, what scripts to write, what data to generate, and what environment to set up. This skill builds all of it.

When to Use

  • As the implementation step of the research recipe (phase 2)
  • After the experiment plan has been approved via GitHub issue

Arguments

/autoskillit:implement-experiment {plan_path}

{plan_path} — Absolute path to the experiment plan file (required). Scan tokens after the skill name for the first path-like token (starts with /, ./, or .autoskillit/).

Critical Constraints

NEVER:

  • Implement without first exploring affected systems with subagents
  • Implement in the main working directory (always use the worktree)
  • Force push or perform destructive git operations
  • Merge the worktree branch into any branch
  • Delete or remove the worktree
  • Run the full test suite — pytest with no args or targeting the entire repo (the orchestrator handles full test execution via test_check)
  • Create experiment files outside the planned research/ subfolder
  • Execute git merge commands (all branch content must be applied via git cherry-pick or git checkout <branch> -- <file>)
  • Run subagents in the background (run_in_background: true is prohibited)

ALWAYS:

  • Create a new worktree from the current branch
  • Use subagents to deeply understand the codebase context BEFORE implementing
  • Use model: "sonnet" when spawning all subagents via the Task tool
  • Follow the implementation phases from the experiment plan
  • Put all experiment artifacts in one self-contained research/ subfolder
  • Commit per phase with descriptive messages
  • Leave the worktree intact when done
  • Write tests/test_{script_name}.py alongside each experiment script created in Step 4
  • Run pytest --collect-only after creating tests to verify discovery before committing
  • Read before editing: Before issuing an Edit call on any file, ensure you have issued a Read on that file earlier in this session. Claude Code rejects Edit on unread files — the retry wastes a full API turn at current context size. If you are uncertain whether a file was read, issue a targeted Read (offset + limit to the region you plan to edit) rather than risk an error.

Read the full file on GitHub · 338 lines

Changes

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.

  1. 6d ago First seen · 338 lines · 24 tokens per session scan E 1d91af1ab928

Subscribe to this mod's changes

implement-experiment is a skill published in the GitHub repository TalonT-Org/AutoSkillit (5 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 3,407 once invoked, about $0.0001 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 2026-09-03.

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