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Migrate

  • 33 installs
  • 17.2k repo stars
  • Updated August 1, 2026
  • danielmiessler/personal_ai_infrastructure

Imports external content from files, Obsidian, Notion, CLAUDE.md, or Cursor rules, classifies each chunk, and routes it into PAI destinations with an approval loop.

About

Intakes existing content from external sources, classifies each chunk against the PAI destination taxonomy, and commits approved chunks with provenance. A developer uses it to bulk-import notes, rules, and exports into a PAI structure with confidence-gated approval.

  • Confidence gating: >=70% auto-approve, 40-70% confirm, <40% walk-through
  • Provenance HTML comment on every commit and substring dedup

Migrate by the numbers

  • 33 all-time installs (skills.sh)
  • Ranked #1,197 of 2,715 Automation & Workflows skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/danielmiessler/personal_ai_infrastructure --skill migrate

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Listed on Skillselion
Installs33
repo stars17.2k
Last updatedAugust 1, 2026
Repositorydanielmiessler/personal_ai_infrastructure

What it does

Imports external content from files, Obsidian, Notion, CLAUDE.md, or Cursor rules, classifies each chunk, and routes it into PAI destinations with an approval loop.

Files

SKILL.mdMarkdownGitHub ↗

Migrate — external-content intake and classification

🚨 MANDATORY: Voice Notification

curl -s -X POST http://localhost:31337/notify \
  -H "Content-Type: application/json" \
  -d '{"message": "Starting the migration. Scanning source and classifying chunks."}' \
  > /dev/null 2>&1 &

What this skill does

Migrates content into the PAI structure from external sources. Unlike /interview (which asks the user questions to fill gaps), /migrate already has the content — it just needs to classify each chunk and route it to the right PAI destination.

Sources supported in V1

  • Files: .md, .markdown, .txt (single file or directory recursion)
  • Stdin: piped content or pasted directly
  • Other PAI installs: point at their USER/TELOS/ or MEMORY/KNOWLEDGE/ directories
  • Agent-harness rule files: CLAUDE.md, .cursorrules, OpenAI Custom Instructions export
  • Exports: Obsidian vaults (markdown), Notion exports (markdown), Apple Notes exports (.txt), raw journal dumps

What it classifies chunks into

CategoryDestinations
Foundational TELOSMISSION, GOALS, PROBLEMS, STRATEGIES, CHALLENGES, BELIEFS, WISDOM, MODELS, FRAMES, NARRATIVES, SPARKS
IDEAL_STATE dimensionsHEALTH, MONEY, FREEDOM, RELATIONSHIPS, CREATIVE, RHYTHMS
Preference filesBOOKS, AUTHORS, MOVIES, BANDS, RESTAURANTS, FOOD_PREFERENCES, LEARNING, MEETUPS, CIVIC
IdentityUSER/PRINCIPAL_IDENTITY.md
KnowledgeMEMORY/KNOWLEDGE/{Ideas,People,Companies,Research}
AI collaboration rulesmemory/feedback_*.md (for "always do X", "never Y" patterns)
UnclearFlagged for the user's manual routing

Workflow

Phase 1 — Identify the source

Ask the user what he wants to migrate:

  • "Paste the content here and I'll work from stdin"
  • "Point me at a file path"
  • "Point me at a directory and I'll scan everything inside"
  • "I have a Cursor rules file at ~/Projects/X/.cursorrules"
  • "My old PAI install has TELOS at ~/old-claude/TELOS/"

Collect the source path. If content is pasted, write it to a temp file first.

Phase 2 — Scan

Run the scanner:

bun ~/.claude/PAI/TOOLS/MigrateScan.ts --source <path>
# or
echo "$CONTENT" | bun ~/.claude/PAI/TOOLS/MigrateScan.ts --stdin

Scanner output includes:

  • Total chunks found
  • Proposed routing table (how many chunks per target)
  • Average classification confidence
  • Count of UNCLEAR chunks
  • Count of low-confidence (<40%) chunks

Phase 3 — Present routing summary

Show the user the routing proposal in a scannable format:

Found 47 chunks from 3 files. Proposed routing:

  📂 TELOS/GOALS.md              12 chunks  (78% avg confidence)
  📂 TELOS/WISDOM.md              8 chunks  (65% avg confidence)
  📂 TELOS/BELIEFS.md             6 chunks  (71% avg confidence)
  📂 MEMORY/KNOWLEDGE/Ideas      15 chunks  (52% avg confidence)
  🧠 memory/feedback              4 chunks  (85% avg confidence)
  ❓ UNCLEAR                      2 chunks  (needs your call)

Options:
  - Approve everything trusted (confidence ≥60%)?
  - Walk through the low-confidence and UNCLEAR chunks one by one?
  - Review specific categories?
  - Review everything?

Phase 4 — Approval loop

Based on the user's preference:

Fast path (he says "approve all trusted"):

bun ~/.claude/PAI/TOOLS/MigrateApprove.ts --approve-all

Commits everything non-UNCLEAR. Then walk through UNCLEAR chunks conversationally.

Category path (he says "approve goals and wisdom, skip knowledge"):

bun ~/.claude/PAI/TOOLS/MigrateApprove.ts --approve-target TELOS/GOALS.md
bun ~/.claude/PAI/TOOLS/MigrateApprove.ts --approve-target TELOS/WISDOM.md

Walk-through path (he wants careful review):

bun ~/.claude/PAI/TOOLS/MigrateApprove.ts --review

Show each pending chunk. For each:

  • Show preview + proposed target + confidence + alternatives
  • Ask: approve / modify target / reject
  • Commit decision

Phase 5 — Handle UNCLEAR chunks

UNCLEAR chunks are ones where no classification rule matched strongly. For each:

  • Display full content (not just preview)
  • Ask the user: "This one's unclear — what is it? Could be X, Y, Z, or maybe Knowledge/Ideas as a catch-all?"
  • the user chooses → commit via --modify <id> --target <chosen>

Phase 6 — Completion summary

After approval pass:

  • Report total chunks committed, per-target count
  • Flag any remaining UNCLEAR
  • Recommend next step: run /interview to interview around anything the migration left sparse

Rules

  • Every commit carries provenance. The committed content includes an HTML comment noting source file + section + timestamp. Nothing gets dropped into TELOS without attribution.
  • Never bulk-approve UNCLEAR. Those require the user's explicit routing.
  • Confidence thresholds: ≥70% = trusted (auto-approve eligible). 40-70% = medium (show for confirmation). <40% = low (walk-through required).
  • Ask before touching identity. PRINCIPAL_IDENTITY.md commits always prompt — that file is load-bearing.
  • Don't duplicate. If the same content already exists in the target (substring match), flag it and ask before appending.
  • Respect private paths. Never migrate content into IDEAL_STATE/ without the user's per-dimension call (Decision #3: IDEAL_STATE is fully private and curated).
  • Feedback memories get new files. Each memory/feedback chunk becomes its own feedback_migrated_<slug>_<id>.md file — not appended to an existing memory.
  • Knowledge gets new files too. Each MEMORY/KNOWLEDGE/* chunk becomes a new typed note with source metadata.

Examples

User: /migrate ~/old-claude/TELOS/

the DA scans the old TELOS directory, classifies every chunk, presents the routing summary, offers fast-path vs. walk-through approval.

User: /migrate (then pastes CLAUDE.md content)

the DA reads from stdin, classifies the rules as memory/feedback (most) plus maybe PRINCIPAL_IDENTITY (if identity lines are mixed in), walks through approval.

User: "migrate my Cursor rules at ~/.cursor/rules"

the DA scans the rules dir, surfaces likely-feedback classifications, walks through with extra care (Cursor rules often have tool-specific stuff that doesn't translate to PAI).

User: "import the stuff I dumped in /tmp/journal.md"

the DA scans the journal, expects a lot of UNCLEAR + WISDOM, walks through each section.

Related

  • /interview — fills gaps by asking questions (not by intaking existing content)
  • /Telos Update workflow — edit a single TELOS file directly
  • /Knowledge — manage the Knowledge Archive
  • /_PROFILE — manage PRINCIPAL_IDENTITY

Troubleshooting

  • Low average confidence (<40%): the source is probably genre-mismatched (e.g., code comments, logs, raw data). Consider pre-filtering to remove non-prose chunks before scanning.
  • Everything goes to UNCLEAR: the source probably has no recognizable PAI-taxonomy patterns. Either add the content manually via /Telos or write it as general Knowledge notes.
  • Duplicate content warnings: the scanner doesn't dedupe against existing files yet. Run --dry-run first to preview before committing.

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