How do I install the Project Context Query skill?

Run npx skillmds@latest add gavishap/project-context-query in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Project Context Query free to use?

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Who published Project Context Query?

gavishap (@gavishap) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Project Context Query by gavishap — Agent Skill

Name: Project Context Query
Author: gavishap

Project Context Query

Answer questions about the project using the vault's layered memory — Graphify code graphs, the compiled LLM Wiki, then raw files — instead of re-reading code or transcripts. Use for any question about the project, the codebase, the process, the people, or past decisions.

gavishap Updated Sep 21, 2026

File contents

Project Context Query (3-Layer Traversal)

Route every question to the cheapest layer that can answer it. Escalate only when a layer comes up short. All commands run from the vault root (python on Windows, python3 on macOS/Linux).

Route by question type

Question is about…	Go to	How
Code structure, "what calls X", "where is Y implemented", architecture	Layer 1: Graphify	`graphify query`, `explain`, `path`, `affected`
Change impact ("what breaks if I change X")	Layer 1	`graphify affected "X" --graph graphify/<repo>/graph.json`
Business process, decisions, people, environments, constraints, history	Layer 2: Wiki	`python scripts/wiki_tool.py search-catalog --query "..."` → open top 1–3 notes
What happened in a meeting / exact wording	Layer 3a: Raw sources	open the specific `Raw/Sources/` file the wiki note cites
Exact current code needed for an edit	Layer 3b: Raw code	open the specific repo file, narrowly

When a question spans layers (e.g. "how should the new handler validate input?"), take decided rules from Layer 2 and code shape from Layer 1, then only open the files you will actually edit.

Layer 1 — Graphify (code)

On Windows/PowerShell set $env:PYTHONUTF8=1 first — without it graphify's final print can crash on Unicode (output still arrives; exit code is dirty).

# from the vault root, against the committed snapshots:
graphify query "how does authentication work" --graph graphify/<repo>/graph.json
graphify explain "<NodeName>" --graph graphify/<repo>/graph.json
graphify path "<A>" "<B>" --graph graphify/<repo>/graph.json
graphify affected "<function>" --graph graphify/<repo>/graph.json

# from inside a repo, the default graph is ./graphify-out/graph.json:
graphify query "where are sessions created"

Human-readable entry points: graphify/<repo>/_GRAPH_REPORT.md and the _COMMUNITY_*.md notes (named clusters).
Budget answers with --budget 1500 when you only need orientation.

Layer 2 — Compiled Wiki (knowledge)

python scripts/wiki_tool.py search-catalog --query "<topic>"

Open only the top matches. Key hubs: the topic note(s) in Wiki/Topics/ and the active project note in Wiki/Projects/ (status, open questions). Wiki notes carry sources: back to transcripts/docs when you need Layer 3a.

Layer 3 — Raw (last resort)

Raw sources: only the file a wiki note cites, for verification or exact wording.
Raw code: only the specific files you are editing or that Layers 1–2 pointed at. Never sweep a repo.

Memory feedback (optional but encouraged)

After a graph query materially helps (or misleads), record it so the graph's memory improves:

graphify save-result --question "..." --answer "..." --outcome useful   # or dead_end / corrected
graphify reflect    # occasionally: aggregate outcomes into LESSONS.md

Freshness

If code changed significantly since the graphs were built, refresh before trusting Layer 1: python scripts/sync_graphs.py (fast, AST-only), or a full /graphify <repo> --update + re-export for the browsable notes.

Never

Never re-read whole repos or all Raw sources to "get context".
Never answer business-rule questions from code alone — the wiki carries the decided rules (and their open questions).
Never edit generated files (graphify/, catalogs, indexes) by hand.

---name: project-context-querydescription: Answer questions about the project using the vault's layered memory — Graphify code graphs, the compiled LLM Wiki, then raw files — instead of re-reading code or transcripts. Use for any question about the project, the codebase, the process, the people, or past decisions.---# Project Context Query (3-Layer Traversal)Route every question to the cheapest layer that can answer it. Escalate onlywhen a layer comes up short. All commands run from the vault root (`python` onWindows, `python3` on macOS/Linux).## Route by question type| Question is about… | Go to | How ||---|---|---|| Code structure, "what calls X", "where is Y implemented", architecture | **Layer 1: Graphify** | `graphify query`, `explain`, `path`, `affected` || Change impact ("what breaks if I change X") | **Layer 1** | `graphify affected "X" --graph graphify/<repo>/graph.json` || Business process, decisions, people, environments, constraints, history | **Layer 2: Wiki** | `python scripts/wiki_tool.py search-catalog --query "..."` → open top 1–3 notes || What happened in a meeting / exact wording | **Layer 3a: Raw sources** | open the specific `Raw/Sources/` file the wiki note cites || Exact current code needed for an edit | **Layer 3b: Raw code** | open the specific repo file, narrowly |When a question spans layers (e.g. "how should the new handler validateinput?"), take **decided rules from Layer 2** and **code shape from Layer 1**,then only open the files you will actually edit.## Layer 1 — Graphify (code)On Windows/PowerShell set `$env:PYTHONUTF8=1` first — without it graphify'sfinal print can crash on Unicode (output still arrives; exit code is dirty).```bash# from the vault root, against the committed snapshots:graphify query "how does authentication work" --graph graphify/<repo>/graph.jsongraphify explain "<NodeName>" --graph graphify/<repo>/graph.jsongraphify path "<A>" "<B>" --graph graphify/<repo>/graph.jsongraphify affected "<function>" --graph graphify/<repo>/graph.json# from inside a repo, the default graph is ./graphify-out/graph.json:graphify query "where are sessions created"```- Human-readable entry points: `graphify/<repo>/_GRAPH_REPORT.md` and the `_COMMUNITY_*.md` notes (named clusters).- Budget answers with `--budget 1500` when you only need orientation.## Layer 2 — Compiled Wiki (knowledge)```bashpython scripts/wiki_tool.py search-catalog --query "<topic>"```Open only the top matches. Key hubs: the topic note(s) in `Wiki/Topics/` andthe active project note in `Wiki/Projects/` (status, open questions). Wikinotes carry `sources:` back to transcripts/docs when you need Layer 3a.## Layer 3 — Raw (last resort)- **Raw sources:** only the file a wiki note cites, for verification or exact wording.- **Raw code:** only the specific files you are editing or that Layers 1–2 pointed at. Never sweep a repo.## Memory feedback (optional but encouraged)After a graph query materially helps (or misleads), record it so the graph'smemory improves:```bashgraphify save-result --question "..." --answer "..." --outcome useful # or dead_end / correctedgraphify reflect # occasionally: aggregate outcomes into LESSONS.md```## FreshnessIf code changed significantly since the graphs were built, refresh beforetrusting Layer 1: `python scripts/sync_graphs.py` (fast, AST-only), or a full`/graphify <repo> --update` + re-export for the browsable notes.## Never- Never re-read whole repos or all Raw sources to "get context".- Never answer business-rule questions from code alone — the wiki carries the decided rules (and their open questions).- Never edit generated files (`graphify/`, catalogs, indexes) by hand.

Frequently asked questions