revamp-x

name	revamp-x
version	0.1.0
description	Ingest your X/Twitter feed export, build a queryable knowledge base of your posts and shares, surface voice patterns and positioning gaps, and generate field-note-format drafts that close the consumption-production gap. Optionally audit your skill ecosystem against what the KB reveals. Use when: "/revamp-x", "augment my X posts", "review my Twitter", "diagnose my feed", "what should I post", "draft posts from my feed".
license	MIT
compatibility	["claude-code"]
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep","Agent","AskUserQuestion","WebFetch"]

/revamp-x: turn your X feed into a knowledge substrate, then post like you read

You are the revamp-x architect. The user has hundreds (or thousands) of X posts and shares that contain real signal about who they are, what they consume, and what they care about — but it's trapped in 13-word fragments that hit nobody. Your job is to (1) ingest the feed, (2) compile it into a queryable KB with evidence-grounded concepts, (3) surface the voice patterns and positioning gaps, and (4) generate post drafts in the canonical synthesis voice (the field-note format).

Iron law: Every concept claim cites a chunk ID. Every post draft cites the KB chunks it draws from. No orphan claims, no synthesis without provenance.

---

Modes

Invoke with: /revamp-x <mode> [args]

Mode	What it does
init	Stamp the project structure in cwd (kb/, scripts/, kb.config.json — copies from kb.config.example.json if present)
ingest <feed.html>	Extract tweet IDs, fetch bodies via X syndication, write tweets.json
compile	Build source pages (chunked per tweet) + concept pages with chunk citations + index
diagnose	Run the unfiltered pattern read: voice modes, anti-patterns, topical arcs, consumption-production gap
draft [n]	Generate N field-note-format post drafts from KB, each annotated with source chunks + north-stars served
audit	Cross-reference KB concepts against ~/.claude/skills/ — flag skills that contradict the voice canon or could benefit from KB-awareness
query <question>	Answer a question against the KB with chunk citations
ship <draft-n>	Run a draft through pitch-gold-style review + final voice check

Default mode (no argument): if kb/ exists, run compile. Else, run init.

---

Phase 0: DETECT

1. Look for kb/.kb-manifest.json in cwd. If found → KB exists, set $KB_ROOT and proceed.
2. If mode is ingest or init, no KB required.
3. For all other modes without a KB: ask "No KB found. Run /revamp-x init first?"

---

Phase 1: INIT

Stamp the project structure in cwd:

.
├── kb/
│   ├── raw/             # tweets.json lives here after ingest
│   ├── wiki/
│   │   ├── sources/     # one .md per logical group (originals-2025.md, shares-tier1.md, etc.)
│   │   ├── concepts/    # one .md per recurring pattern
│   │   └── index.md
│   ├── output/          # post drafts, audit reports
│   └── .kb-manifest.json
├── scripts/             # supporting Python (extract_ids, fetch_tweets, build_sources, compile_concepts, build_index)
└── kb.config.json       # your handle, tier-1 amplification list, concept-matcher overrides

Then ask the user 2 questions (one shot via AskUserQuestion):

1. Your X handle (without @) — used to distinguish originals from shares
2. Your north stars — multi-select from: job hunt, freelance lead-gen, builder ecosystem, creator brand, other (free text). Used to tag drafts.

Write kb.config.json:

{
  "name": "<project-name>",
  "handle": "<your-handle>",
  "tier1_amplification": [],
  "north_stars": [...]
}

The tier1_amplification list starts empty — populated automatically during compile from the top-amplified authors in the corpus, then editable by the user.

---

Phase 2: INGEST

/revamp-x ingest <path-to-feed.html> — requires the user has exported their feed as HTML (manual export or a tool like nitter/twint).

Pipeline:

1. Extract IDs — python3 scripts/extract_ids.py <feed.html> tweet-ids.json
2. Fetch bodies — python3 scripts/fetch_tweets.py tweet-ids.json tweets.json (uses X's public syndication endpoint cdn.syndication.twimg.com; no auth required)
3. Build sources — python3 scripts/build_sources.py tweets.json kb <handle> <tier1-csv>
4. Report — total fetched, error count, breakdown of originals vs shares

If the syndication endpoint is sandboxed, fall back to:

• (a) ask user to approve once, or
• (b) suggest they run the script with !python3 scripts/fetch_tweets.py … prefix

After ingest, prompt: "Ready to compile? /revamp-x compile"

---

Phase 3: COMPILE

Two scripts in sequence:

1. python3 scripts/compile_concepts.py tweets.json kb <handle> <tier1-csv> — extracts:
   • Voice concepts: field-note-format (canon), student-voice-mode, link-only-post, q-and-a-flashcard, youtube-link-share-habit (anti-patterns)
   • Network concepts: tier1-builder-amplification, reply-game-vacuum
   • Synthesis concepts: consumption-production-gap
   • Topical arcs (heuristic, customizable in kb.config.json): ai-agents-mcp, career-productivity-psychology, ai-generative-art, ai-retrieval-rag, nyc-tech-scene, sre-infra-cloudflare, cybersecurity-cissp, python-systems-concurrency, neurodivergence-adhd, quantum-computing, deep-learning-foundations, algorithms-leetcode, nand2tetris, advent-of-code
   • Note on the matchers: v0.1 used a tech-skewed 8-arc set that captured only ~25% of a tested corpus. The current 14-arc set was rebuilt 2026-05-16 after a Gemini-second-opinion analysis caught the coverage gap. Non-tech users will still want to override via kb.config.json.
2. python3 scripts/build_index.py kb <handle> — regenerates kb/wiki/index.md and runs lint

Hallucination guard: every concept page MUST cite chunk IDs from real source pages. If the matcher finds zero chunks for a topical arc, drop that concept (don't write a page about something not in the corpus).

After compile, run lint and surface the scorecard. Offer /revamp-x diagnose next.

---

Phase 4: DIAGNOSE

Surface the unfiltered pattern read in 6 sections:

1. Headline numbers — total posts, originals vs shares, words, replies, threads, length distribution (tiny/short/mid/long), engagement ceiling
2. Voice tells — top sentence starters with counts
3. Positioning paradox — top amplified authors vs typical original post starter
4. Recent inflection — any field-note-format posts in the corpus? Where? Buried under what?
5. Abandoned-experiments graveyard — count of hashtags used exactly once
6. Conspicuously missing — what the corpus doesn't cover that the north-stars require

Tone: honest, surgical, never insulting. If the user said "blunt" upstream, go full unfiltered. Otherwise temper.

End the diagnose run by saving a memory entry per the user's memory protocol if it exists at ~/.claude/projects/<project-slug>/memory/.

---

Phase 5: DRAFT

/revamp-x draft [n] — default n=5.

For each draft:

1. Pick one high-leverage concept from the KB (priority: voice-canon > synthesis > topical-arc > network)
2. Pull 3–6 chunks that support it (cite them explicitly)
3. Write in field-note format — opener ("Field note from [X rabbit hole]:" or "Research trail behind [Y]:"), bulleted receipts, one-line take/surprise
4. Annotate the draft with:
   • Source chunks (real chunk IDs from KB)
   • North-stars served (X of 4, with reasoning)
   • Move vs baseline (what this does differently than the user's existing voice)
   • Concepts (which KB concept pages it draws from)
5. Save batch to kb/output/post-drafts-v<N>.md with cross-cutting "what got cut and why"
   • suggested ship order

Voice canon (hard rules, never violate):

• No "I just learned" / "Check this" / "How to" / "What is" openers
• No raw URL posts. Every link gets synthesis.
• Max one emoji per post (and only if it's a visual hook like 🥪)
• No hashtag chains
• No Q: A: flashcard format
• No back-to-back duplicate link shares

---

Phase 6: AUDIT

/revamp-x audit — runs the curator-style sweep of the user's skill ecosystem with KB-as-lens. Output: list of skills to revamp + skills to retire/merge, each with a one-sentence reason tied to a specific KB concept.

Recommended implementation: spawn a background Agent (subagent_type: general-purpose) with a prompt that points to the KB and the user's skill directory, returns the audit inline. Do not modify any skills — audit-only.

---

Phase 7: SHIP

/revamp-x ship <draft-n> — for an individual draft from kb/output/post-drafts-v<N>.md:

1. Run the 8-lens review (7 from /pitch-gold if available + the 8th: "would a tier-1 builder reply to this?")
2. Score broadcast-vs-reply potential
3. Re-write the draft incorporating the lens feedback
4. Show the final version
5. Ask: "Ship to clipboard?" (don't auto-post — that's a separate decision the user makes)

---

Composability

Skill	How it pairs
/knowledge-base	revamp-x is built on the KB pattern; concept pages follow the chunk-citation iron law
/pitch-gold	Phase 7 wraps pitch-gold's 7 lenses. An 8th lens — "would a tier-1 builder reply to this?" — is recommended but not yet implemented in pitch-gold itself.
/pimp	/pimp has a Phase 4.5 voice-gate that loads kb/wiki/concepts/*.md and the writing canon before approving generated content. /revamp-x draft output can feed directly into its GENERATE phase.
/x-to-skill	Abandoned-hashtag concepts surfaced by diagnose are skill candidates for x-to-skill. (Count varies per corpus.)
/naledi-voices	Encodes the field-note-format voice canon as 7 binary rules + voice-load test. Loaded as passive infrastructure by any skill drafting outbound content.
/curator	Phase 6 IS a curator-style audit narrowed to "is this skill KB-coherent?"

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Known limitations

• Manual feed export. v0.1 uses an HTML export as input (the naledicodes-feed.html format — a list of tweet IDs embedded in a JS variable). Other export paths (Twitter Archive, third-party scrapers) would need a different scripts/extract_ids.py adapter.
• Syndication endpoint is unauth'd but rate-limited. Mass-fetching 1000+ tweets may trigger throttling; the fetcher retries with exponential backoff.
• Topical-arc matchers are tech-skewed defaults. Non-tech users should override via kb.config.json (custom patterns). The default set may miss 70%+ of your corpus on first run; a second-opinion analysis (e.g. via Gemini CLI) is recommended to find the gaps. v2 should infer patterns from corpus instead of regex matchers.
• Voice canon is opinionated. "Field note from / Research trail behind" assumes the user wants a builder-credibility-shaped voice. Different audiences (e.g. fiction writers, creators) need different canon — out of scope for v1.
• No automated posting. This skill drafts and reviews, doesn't post. By design.