About
The ax-narrate skill generates a structured session narration when triggered by phrases like "narrate this session" or "summarize what changed." It captures the complete development story including corrections, abandoned attempts, and tool failures that don't appear in final PRs. The output is saved as a JSON file in `.ax/narrations/` for review in ax studio.
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/ax-narrateCopy and paste this command in Claude Code to install this skill
Documentation
ax:narrate - write the session's story as a structured narration
You were there. This skill turns YOUR OWN memory of the session into a reviewable artifact: 3-7 stops in reading-flow order, each anchored to real evidence - code hunks, turn numbers, user quotes, failures. The point is to capture what a PR diff never shows: the corrections, the dead ends, the recoveries.
The artifact validates against SessionNarration in
apps/studio/src/routes/narration-types.ts and renders in ax studio.
Step 1 - identify the session
- Preferred:
ax sessions here --days=1 --jsonand pick the current session's id (the one matching this conversation). Use the short id. - If
axis unavailable or the session isn't ingested yet, derive a slug:<repo>-<YYYYMMDD-HHmm>. Note inmetathat turn seqs are best-effort ordinals in that case. - If available,
ax sessions show <id> --jsongives you the real turn seqs to anchor against. Prefer real seqs over guesses.
Step 2 - reconstruct the story from your own context
Re-read the conversation in your head before writing anything:
- What did the user originally ask for? (intent)
- What existed before, what exists now? (before/after)
- Where did the user redirect or correct you? EVERY one of these
becomes a
correctionanchor. No exceptions. - Which tool failures actually mattered (changed your approach, cost
real time, forced a workaround)? Each becomes a
tool_failureanchor. Skip trivial retries that changed nothing. - Which attempts were abandoned? They get a stop or at least a
turnanchor - abandonment is part of the story.
Step 3 - choose 3-7 stops, in reading-flow order
A stop is a LOGICAL unit of change, not a file. If three files changed for one reason, that is ONE stop with several anchors. Order rules (stolen from the code-tour playbook because they work):
- Entry point first: the change that, understood alone, unlocks the rest.
- Cause before effect: the correction comes before the code it caused.
- Definitions before consumers: types/schema stops before usage stops.
- Verification last: tests, typecheck, and the failures hit on the way.
- Combine trivial housekeeping into one final stop, or omit it.
Step 4 - write each stop
- title: short and friendly. "Call counts become a char diffstat", not "Changes to files-touched.ts".
- gist: ONE sentence. Not two. A reader who reads nothing else must get the stop from the gist. Conversational, the way you'd say it to a colleague.
- detail: 2-4 sentences of markdown (paragraphs,
inline code, bold). Say WHY the change looks the way it does; "we did X instead of Y because Z" is exactly what the reader wants. - transition: a short connective phrase to the next stop; empty
string
""for the last stop. - anchors: MUST be non-empty. An unanchored stop is an unsupported claim. Anchor kinds:
| kind | required fields | use for |
|---|---|---|
file_hunk | file, old_text, new_text, label, opt turn_seq | a real code change |
code_state | artifact, label, lang, code, opt turn_seq | the evolving architecture snapshot |
turn | turn_seq, label | a plain moment in the transcript |
user_direction | turn_seq, quote | user steering (not correcting) |
correction | turn_seq, quote, outcome | user correcting course |
tool_failure | turn_seq, tool, error_excerpt, recovery | consequential failure |
term | name, definition | a domain term the story leans on |
Hard anchor rules
file_hunkcarries VERBATIM old/new fragments from the actual edits you made - copy the real text, never paraphrase code. Keep hunks short (5-15 lines per side); pick the most telling fragment, not the whole edit.old_text: nullfor pure insertions,new_text: nullfor pure deletions. Never both null.- Every user correction/redirect in the session gets a
correctionanchor with a verbatim (trimmed)quoteand a concreteoutcome- what actually changed because of it. - Every consequential tool failure gets a
tool_failureanchor with a realerror_excerptand how you recovered (or"abandoned"). - Never fabricate turn seqs. Use
ax sessions showseqs when you have them; otherwise count user turns from the start of the conversation and say so in the detail. code_stateis the architecture spine of the narration: pick ONE stableartifactid (e.g."review-architecture") and restate the FULL snapshot at each stop where the design moved - pseudo-code of types/interfaces, how they compose, and the call stack (plan-style:Caller -> Callee // note). Consecutive snapshots of the same artifact animate token-by-token in studio, so KEEP shared lines byte-identical between stops and let only the real delta differ - a new method, a renamed shape, an added edge case. Usecode_statefor the evolving design; usefile_hunkfor one-off code jumps (those render as static before/after diffs, not motion).
Step 5 - emit the artifact
Write .ax/narrations/<session-id>.json (create the directory if
needed) with exactly this top-level shape:
{
"schema_version": 1,
"kind": "narration",
"meta": {
"session_id": "<id>",
"generated_at": "<ISO-8601 now>",
"generator": "skill",
"model": "<your model id>"
},
"title": "...",
"intent": "...",
"before": "...",
"after": "...",
"stops": [ { "title": "...", "gist": "...", "detail": "...", "transition": "...", "anchors": [ ... ] } ]
}
Before finishing, self-check against the validator's rules:
stopsnon-empty (3-7), every stop'sanchorsnon-empty.- Every gist is one sentence; every
correctionhas anoutcome; everytool_failurehas arecovery; nofile_hunkwith both sides null or empty. - Strings are plain JSON strings (escape newlines in hunks as
\n).
Then tell the user where the file landed and give a 2-line summary of the story you wrote. Do not paste the whole JSON into chat.
GitHub Repository
Frequently asked questions
What is the ax-narrate skill?
ax-narrate is a Claude Skill by Necmttn. Skills package instructions and resources that Claude loads on demand, so Claude can perform ax-narrate-related tasks without extra prompting.
How do I install ax-narrate?
Use the install commands on this page: add ax-narrate 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 ax-narrate belong to?
ax-narrate is in the Meta category, tagged ai.
Is ax-narrate free to use?
Yes. ax-narrate is listed on AIMCP and free to install.
Related Skills
This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
