Remove Claude-specific language from platform skill files (0.4.8)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

CHANGELOG.md

@@ -2,6 +2,10 @@
 
 Full release notes with details on each version: [GitHub Releases](https://github.com/safishamsi/graphify/releases)
 
+## 0.4.8 (2026-04-12)
+
+- Fix: platform skill files (aider, codex, opencode, claw, droid, copilot, windows) no longer contain Claude-specific language — references to "Claude" as the AI model replaced with platform-agnostic wording (#272)
+
 ## 0.4.7 (2026-04-12)
 
 - Fix: `watch` semantic edge preservation was always empty — `graph.json` uses `links` key but code read `edges` (#269)

graphify/skill-aider.md

@@ -38,7 +38,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -157,7 +157,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1117,7 +1117,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-claw.md

@@ -38,7 +38,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -157,7 +157,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1117,7 +1117,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-codex.md

@@ -38,7 +38,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -156,7 +156,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1175,7 +1175,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-copilot.md

@@ -40,7 +40,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -159,7 +159,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1201,7 +1201,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-droid.md

@@ -38,7 +38,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -157,7 +157,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1172,7 +1172,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-opencode.md

@@ -38,7 +38,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -157,7 +157,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1171,7 +1171,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

graphify/skill-windows.md

@@ -41,7 +41,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
 
 graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.
 
-Three things it does that Claude alone cannot:
+Three things it does that your AI assistant alone cannot:
 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.
@@ -149,7 +149,7 @@ After transcription:
 
 **Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
 
-This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens).
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (your AI model, costs tokens).
 
 **Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
 
@@ -1165,7 +1165,7 @@ Supported URL types (auto-detected):
 - Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
 - arXiv → abstract + metadata saved as `.md`  
 - PDF → downloaded as `.pdf`
-- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Images (.png/.jpg/.webp) → downloaded, vision extraction runs on next build
 - Any webpage → converted to markdown via html2text
 
 ---

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "graphifyy"
-version = "0.4.7"
+version = "0.4.8"
 description = "AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, OpenClaw, Factory Droid, Trae) - turn any folder of code, docs, papers, images, or videos into a queryable knowledge graph"
 readme = "README.md"
 license = { file = "LICENSE" }