About
The retro-meta skill performs a deep retrospective analysis across previous retros and your current setup to identify improvements your existing pipeline might miss. It uses an external AI agent with high-thinking capabilities to drive reasoning when triggered by specific phrases or commands. Use this after standard retros when proposals are sparse or you need broader exploration beyond heuristic-based suggestions.
Quick Install
Claude Code
Recommendednpx skills add Necmttn/ax -a claude-code/plugin add https://github.com/Necmttn/axgit clone https://github.com/Necmttn/ax.git ~/.claude/skills/retro-metaCopy and paste this command in Claude Code to install this skill
Documentation
ax:retro-meta - deep retro of retros
The companion to ax:retro. Where ax:retro walks the heuristic-derived
proposals one by one, retro-meta asks: what improvements does the
existing pipeline NOT yet see?
The external agent (this one, Claude Code or Codex with high thinking) drives the reasoning. The CLI just produces structured evidence and takes user-approved plans back.
When to fire
Explicit triggers only:
- "let's do a deep retro" / "retro of retros"
- "investigate my ax setup" / "what should I fix in my setup"
- "review proposals the heuristic missed"
/ax:retro-metaslash command- After
ax:retrofinishes if the user wants broader exploration
Do NOT auto-trigger on generic "look at recent work".
Prerequisites
ax(axctl) is on PATH and the local SurrealDB is reachable. Ifax doctorfails, stop and tell the userscripts/db-start.sh.- At least 3 retros in the last 30 days. Below that, evidence is too
thin for a meta pass - recommend
ax:retrofirst.
Workflow
Step 1 - Snapshot
ax retro meta --json --since=30 > /tmp/ax-meta.json
Read /tmp/ax-meta.json. The keys you care about:
experiment_status[]- read this FIRST (see Step 2). Each entry:experiment_id,proposal_dedupe_sig,proposal_title,proposal_form,artifact_path,days_since_accepted,opportunities_count,addressed_count,address_ratio,latest_checkpoint{kind,suggested,observed_at},locked_verdict. Pending verdicts (locked_verdict=null) come first.retros[]- rawtried/worked/failed/nextper session.patterns.tool_failures- sorted by total_count desc.patterns.corrections- total + max-per-session + session_count.patterns.friction_kinds- recurring kinds across sessions.current_state.skills- what's already installed (do NOT propose duplicates).current_state.open_proposals- existing heuristic proposals.current_state.accepted_experiments- accepted but verdict-pending.current_state.claude_md_user/claude_md_project- guidance file paths (null if absent).investigation_prompts[]- the prompts you must walk.
Step 2 - Vet existing experiments FIRST
Walk experiment_status in order. For each entry with
locked_verdict=null:
a. If latest_checkpoint.suggested is ignored or regressed:
investigate why (read the artifact_path, sample the matching
opportunities), then run
ax improve verdict --set=<v> <proposal_dedupe_sig> to lock the
call.
b. If latest_checkpoint.suggested is adopted AND
days_since_accepted > 30: lock it as adopted so it stops
cluttering the open list:
ax improve verdict --set=adopted <proposal_dedupe_sig>.
c. If latest_checkpoint is null OR suggested is partial: leave
open. Note in the final summary that it's still gathering signal.
A rule of thumb mirrored from investigation_prompts: if
address_ratio < 0.1 after t+30, default to locking as ignored
unless the artifact has an obvious "not yet exercised" reason.
Step 3 - Walk the investigation prompts (high thinking)
For EACH prompt in investigation_prompts:
- Inspect referenced state with Read / Glob / Grep:
- skill files in
~/.claude/skills/and~/.agents/skills/ claude_md_userif non-nullclaude_md_projectif non-null
- skill files in
- Reason about a candidate improvement. Use a high thinking budget - the point is to see what the heuristic missed.
- If you identify a real improvement (NOT a duplicate of an existing
skill or open_proposal):
a. Draft a plan doc to
~/.claude/plans/<YYYY-MM-DD>-<slug>.md, 30–100 lines. Sections: Problem, Evidence (cite retro ids), Proposed change, Success signal. b. Show the user a 4–6 line summary. c. Ask explicitly: "Register this as an accepted experiment? (y/n)" d. ONLY on yes:ax retro plan \ --slug=<kebab-slug> \ --form=skill|hook|guidance|automation \ --title="<short title>" \ --hypothesis="<one sentence>" \ --plan-path=~/.claude/plans/<file>.md \ --evidence-retros=<retro:id1,retro:id2> \ --confidence=low|medium|high - If the prompt resolves to "no change needed" or "duplicate of existing", say so out loud and move on.
Step 4 - Optional: hand off to scaffolder
For each plan you registered, you may run:
ax improve accept --with-agent <dedupe_sig>
This spawns the internal scaffolding agent to draft an artifact (SKILL.md, hook script, etc) from the plan. Skip if the plan is already self-sufficient.
Step 5 - Summary
Print one paragraph:
- N plans registered, M of those scaffolded
- V verdicts locked (with kind, e.g. "2× ignored, 1× adopted")
- K open_proposals reviewed (and their disposition)
- Any prompts that resolved to "nothing here"
- Suggested next retro window
Anti-patterns
- NEVER register a plan without an explicit user yes per plan. The human is the final filter.
- NEVER auto-accept all open_proposals - the heuristic surfaces them but the deep pass exists precisely to triage them by reasoning, not by frequency rank.
- NEVER write directly to
~/.claude/skills/. Useax retro plan+ax improve accept --with-agent. - NEVER skip Step 2's duplicate check. Proposing a Pre-Bash guard when one is already accepted just wastes the user's time.
- Don't trust frequency alone. A frequency=1 retro can still be load-bearing if it represents a category Claude can't get right.
- NEVER propose a new improvement that overlaps a pending experiment. Vet that one first - lock its verdict or escalate before piling on more proposals in the same area. The retrospective loop is incomplete if old experiments stay in limbo.
CLI reference
# Snapshot only (no side effects)
ax retro meta --since=30 [--limit-retros=50] [--pretty]
# Register a user-approved plan as accepted proposal + experiment
ax retro plan \
--slug=<kebab> \
--form=skill|hook|guidance|automation \
--title="<title>" \
--hypothesis="<hyp>" \
--plan-path=<path-to-plan.md> \
[--evidence-retros=retro:a,retro:b] \
[--artifact-path=<path>] \
[--confidence=low|medium|high] \
[--frequency=<N>] \
[--json]
# Optionally hand off scaffolding to the internal agent
ax improve accept --with-agent <dedupe_sig>
# Lock the verdict on a previously-accepted experiment
ax improve verdict --set=adopted|ignored|regressed|partial|no_longer_needed <dedupe_sig>
Output of ax retro meta defaults to JSON because the reader is you,
not a human.
GitHub Repository
Frequently asked questions
What is the retro-meta skill?
retro-meta is a Claude Skill by Necmttn. Skills package instructions and resources that Claude loads on demand, so Claude can perform retro-meta-related tasks without extra prompting.
How do I install retro-meta?
Use the install commands on this page: add retro-meta 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 retro-meta belong to?
retro-meta is in the Other category, tagged ai.
Is retro-meta free to use?
Yes. retro-meta is listed on AIMCP and free to install.
Related Skills
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.
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.
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.
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.
