SKILL·BE6A7E

tune

Necmttn
Updated 2 days ago
2 views
115
15
115
View on GitHub
Othergeneral

About

The `tune` skill performs a session retrospective to propose environment improvements like hooks, checks, and tool access, filing each as a triageable proposal. It analyzes the raw session log and uses `ax` as its ledger system. Developers trigger it via specific phrases like "environment retro" or the `/ax:tune` command to optimize their setup for future sessions.

Quick Install

Claude Code

Recommended
Primary
npx skills add Necmttn/ax -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/Necmttn/ax
Git CloneAlternative
git clone https://github.com/Necmttn/ax.git ~/.claude/skills/tune

Copy and paste this command in Claude Code to install this skill

Documentation

ax:tune - what to change in the rig after one session

The user has asked to tune the rig. You read what one session actually did, name the changes to the agent's environment (hooks, checks, steering files, tools, access) that would make the next run cheaper or safer, and file every candidate in ax so it is triageable later. The session log is the primary source; ax is the ledger.

Steps

  1. Call the Skill tool with writing-for-agents for the writing style.

  2. Locate the session. Default is the current one: the id is $CLAUDE_CODE_SESSION_ID and the log is ~/.claude/projects/<cwd-slug>/<id>.jsonl. If the user names another session, take its key. Done when you hold a path that exists.

  3. Make it reviewable in ax, scoped. Run ax ingest here in the background (the bare ax ingest walks every transcript on the machine). Watch its RSS; stop it at 4 GB and record that as a Tool economy finding against ax itself. Continue on the raw log while it runs.

  4. Build the ledger. Run python3 -I scripts/turns.py <log> (add --full to see each command). It prints one line per tool call with ERR, DENY, RETRY, AGENT, BIG flags and a summary of calls, flags and tokens. When ax has the session, add ax sessions show session:<id> --turns --json for timings. Done when every flagged line has a cause you can state.

  5. Hunt candidates in these categories, each with evidence from the ledger (turn, tool calls spent, bytes or tokens wasted) and a fix that names a file.

    • Navigation: the agent took long to find a file or fact. Fix: a pointer in auto-memory or the repo's CLAUDE.md, never a paragraph.
    • Automated checks: a mistake a linter, type check, test or filesystem check would have caught. Read the repo's own check command first (package.json scripts, CI workflow); an existing check that is unwired or broken is the finding. A repo with no pre-commit hook and no CI check is itself a finding.
    • Coding standards: classify the violation first. A mechanical one (banned API, import shape, file-location rule, a command shape) gets a deterministic check: a lint rule, a pre-commit hook, a PreToolUse hook that rewrites and allows rather than denies. Reserve prose rules for judgement calls no check can substitute for, and give them to the reviewer agent, which has the least context pressure.
    • Global AGENTS.md / CLAUDE.md: always-loaded lines that did not bear on this session. Candidates for a pointer and a doc, or deletion.
    • Tool economy: expensive calls. A BIG result, an alias or pager writing ANSI to a pipe, a denial that forced a verbatim resend, a search over the wrong tree. Fix: a rewrite hook, a tty guard, a scoped command.
    • No-ops: steering lines the model already obeys by default. Delete the sentence, not words from it.
    • Information access: a fact the agent needed and could not reach (a session id, a dev-server log, a third-party dashboard). Fix: an env var, a tee, read-only access.

    Done when each candidate has category, evidence, fix, and the file the fix lands in.

  6. Present the candidates to the user in order of severity: cost in this session first, then likelihood of recurrence. One paragraph each. End with which ones are mechanical (a check) and which need judgement (a rule).

  7. File in ax. Read PROPOSALS.md for the payload shapes. Then:

    ax retro brief --session=session:<id>          # writes .ax/tasks/retro/<id>.md
    ax retro emit --session=session:<id> --source=manual --from-file=<json>
    

    The JSON holds tried, worked, failed, next, and one proposals[] entry per candidate, shaped by its form. Set the brief's frontmatter status: completed. Done when ax improve list shows the new proposals.

  8. Apply only when asked. If the user says fix them, apply in severity order, one commit per repo, and re-run the ledger on the next session to confirm the flag class is gone.

Reference

Implementation vs review

Work goes through two stages. The implementation agent explores, writes and debugs under the most context pressure. The review agent receives a diff and has the least. Standards belong to the reviewer; the implementer gets checks and pointers.

Files

  • CLAUDE.md / AGENTS.md: loaded every turn in that repo. Navigation pointers only.
  • CODING_STANDARDS.md: read at review time. Judgement rules.
  • ~/.claude/hooks/*.sh: PreToolUse guards. Prefer permissionDecision: allow with updatedInput over deny.
  • ~/.claude/projects/<slug>/memory/: auto-memory. Pointers and machine-local facts.
  • Skills: docs whose description is a context pointer, or user-invoked commands.

GitHub Repository

Necmttn/ax
Path: skills/tune
0
agent-memoryagent-observabilityai-agentsbunclaude-codecodex
FAQ

Frequently asked questions

What is the tune skill?

tune is a Claude Skill by Necmttn. Skills package instructions and resources that Claude loads on demand, so Claude can perform tune-related tasks without extra prompting.

How do I install tune?

Use the install commands on this page: add tune to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.

What category does tune belong to?

tune is in the Other category.

Is tune free to use?

Yes. tune is listed on AIMCP and free to install.

Related Skills

sports-betting-analyzer
Other

This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.

View skill
llamaguard
Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill
cost-optimization
Other

This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.

View skill
quantizing-models-bitsandbytes
Other

This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.

View skill