router
Skill2 starsChanged 9 days agoApache-2.0View on GitHub
Map of the agent-memory skills and the chains they are meant to run in. Read it when the user sets a task and the route is not obvious: unclear which skill solves it, the task has several steps, or you are about to do by hand what a skill already covers. Also on direct words: "which skill", "where do I start", "what is the best way to do this", "is there a skill for this".
17 other projects use a version of this.
Adopted by
Most recently taken up by
- lizzyDeng/claude-skillstheir own version, first seen Nov 8, 2026
- apattichis/agentic-research-toolkittheir own version, first seen Nov 8, 2026
- Wellsonr/skillsettheir own version, first seen Nov 8, 2026
- guang-lee-cn/scaffold-skillstheir own version, first seen Nov 8, 2026
- sam-ash-cy/sams-agentic-workflowtheir own version, first seen Nov 8, 2026
- lubusIN/frappe-skillstheir own version, first seen Nov 4, 2026
Used with
Projects with it also use research.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
NVIDIASkillresearch
Before designing a feature, research how comparable libraries solve the problem, check best practices, known pitfalls, and upstream changes. Use when starting a new feature, investigating an unfamiliar area, or the user asks to research something.
Also in 3 projects that use this
Other versions17 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 17
- gamedev-skills/awesome-gamedev-agent-skills★ 6.4ksame name
- grainulation/grainulator★ 86same name
- lubusIN/frappe-skills★ 68same name
- timharris707/skills★ 44same name
- jammyfu/open-game-skills★ 12same name
- Canary-ECAD/eplan-scripting-skill★ 5same name
- Aetherdz/huntpack★ 4same name
- SunCherry/agentic-AI_analog_IC_auto-designer★ 4same name
- Adnova-Group/muster★ 2same name
- guang-lee-cn/scaffold-skills★ 2same name
7 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository20 other files it gives its agents.
- browserSkill
- clean-memorySkill
- codebase-designSkill
- commitSkill
- desktopSkill
- graphicsSkill
- grillingSkill
- implementSkill
- latexSkill
- memory-reviewSkill
- mistakeSkill
- notesSkill
- pdfSkill
- setupSkill
- skill-quality-suiteSkill
- tddSkill
- to-specSkill
- to-ticketsSkill
- vault-indexSkill
- videoSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 13 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
If a skill covers the task and you already started by hand, stop and offer the skill.
- Very specific: line 22
All skills below live in the agent-memory plugin: call them as /agent-memory:<name>.
“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Route
Name the route and offer the first step; do not recite the skill list. One line in the user's
language, like "route: /agent-memory:grilling -> /agent-memory:to-spec -> /agent-memory:to-tickets
-> /agent-memory:implement, start with the interview?", and that is all. Do not ask permission for
every next step: ask at forks.
If a skill covers the task and you already started by hand, stop and offer the skill. If no skill covers it, say so and work by hand; do not stretch the nearest one over it.
All skills below live in the agent-memory plugin: call them as /agent-memory:<name>.
Forks that are easy to get wrong
Text in hand versus text to extract. Ready text, a summary, notes: straight to notes. A video
link: video, which extracts the transcript and calls notes itself. A PDF, a book, a scan: pdf,
which also calls notes itself. Never call notes directly on a link or a file.
A document to learn from, or a file to work on. A PDF whose content goes into the vault: pdf.
A Word, PowerPoint or Excel file to create, read, edit or convert, or a PDF to merge, split, fill or
sign: Anthropic's document-skills (docx, pptx, xlsx, pdf; in the Claude apps they show as
anthropic-skills:*). Not installed: say so and offer stage 4b of setup; do not improvise with
raw libraries when the user asked for a finished document.
Knowledge from a video, or how the video is made. video is for knowledge: a lecture, a talk, a
tutorial, a podcast interview, a bare link. If the user wants to understand how a clip, ad or vlog is
built (hooks, pacing, editing), that is not video: say so and do it by hand, or ask "the knowledge
from it, or how it is made?".
Memory or the vault. A fact about the user, their decisions, a tool pitfall: agent memory
(the memory repository). Knowledge about the world: the vault, through notes. A procedure that is
always done the same way: a skill or a script. Test: could this be recovered from the vault or the
git log? Then it does not belong in memory.
A skill as the subject. Work on a skill (check it, fix one that half-works, prepare it for
publishing, read a stranger's before trusting it) goes to skill-quality-suite. Writing a new one
from scratch goes to the official skill-creator if it is installed, then skill-quality-suite
check. A note about how skills work is notes: the topic being skills does not make it skill work.
Chains
Knowledge into the vault
video / pdf -> notes, or notes directly when the text is in hand. A diagram inside a note:
notes draws analogue pictures itself and calls graphics for templates and data charts. New notes
reach the MemPalace index through notes step 8; a vault that grew without it: vault-index.
Code, a project, a feature
Three entrances, one channel:
A. an idea of their own -> grilling --+
B. someone else's brief ------------------+-> to-spec -> codebase-design -> to-tickets -> implement
C. a clear task ------------------+grilling: the interview that surfaces unstated assumptions (why, what if not, does it exist already, the sign of success, stack, scale), then the decision tree.to-spec: what and why, and the boundaries; no paths or signatures, so it survives a refactor.codebase-design: modules, seams, interface depth. A reference read at the moment the shape is decided.to-tickets: order and blocking; tickets as local markdown in.scratch/unless the project names a tracker.implement: the build, withtddinside.
Scale decides how much of the chain is needed:
| What | Route |
| One edit, a bug with a clear repro | none, just work |
| A feature in a living project | to-tickets -> implement |
| A new project or MVP | the full chain |
| A timed algorithm task | no route at all |
Look at what exists first (a plan note, a repository, tickets in .scratch/) and enter the chain
at the first step not yet done. A detailed plan with acceptance criteria already is a spec.
To check finished work: the built-in /code-review, /security-review, /simplify. To commit: commit.
A decision outside code
grilling for anything that needs a clear plan: a niche, the structure of a vault section, a plan
for a period, a contested architecture. Not for routine (a note, a video, an edit, an answer): there
an interview only gets in the way.
A website
Reading a public page, docs or an API: WebFetch or curl, no browser. Doing something on a site (a
form, filters, pages of results, a JS-rendered page, WebFetch returned an empty shell): browser.
The user wants to watch, or it is their own dev server: the app's built-in browser. It needs their
logins from their main Chrome: Claude in Chrome.
A desktop app
The app's own route first: a CLI flag, a config file, a scripting API, an MCP server. A native
window with no such route (a dialog, a settings page, a form, a tray menu): desktop. No
accessibility tree at all (a game, a canvas, a remote screen): the harness's screenshot computer use.
Formulas, proofs, slides
latex: a full .tex, a snippet, or Beamer slides.
Memory
Gave a wrong answer and corrected it: mistake, at once, without a reminder. Clean up now:
clean-memory. The weekly pass (repeats across sessions, stale facts, the journal emptied):
memory-review. First install, or paths changed: setup.
When no skill is needed
A short question, a single file edit, a search through the vault, a conversation. A skill is a procedure for multi-step work, not a wrapper around every action.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

