core
Skill1 starsChanged 6 days agoMITView on GitHub
A core project management skill for AI agents.
We split this skill into the rules it gives an agent
13 rules. 0 are common, 8 are distinctive, and 5 are very specific to this project.
0
Common
Most of the 18 edited versions we read keep these.
8
Distinctive
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
DRY — Don't Repeat Yourself: keep one source of truth; reuse logic and facts.
- Distinctive: line 16
KISS — Keep It Simple Stupid: prefer small, simple steps.
- Very specific: line 30
Use skill to discover relevant skills.
- Very specific: line 34
Read and maintain these files in ./.
- Distinctive: line 46
Create files as needed; skip project writes for review-only requests.
- Distinctive: line 55
Use any shorthand an AI agent will understand / pseudo code / bash scripts.
- Distinctive: line 58
Run the first unchecked, unblocked step.
- Distinctive: line 60
Ask before revising the checklist after two step failures or a wrong plan.
- Very specific: line 68
Place simple short skills and howto instructions in the AI-HOWTO.md.
- Very specific: line 72
Use root ai- folders, preferring existing non-prefixed equivalents:
- Distinctive: line 95
Write / edit project specific skills for project focused know-how.
- Distinctive: line 107
Always translate foreign language input to English, keep the original wording when nuance matters, consider both original and translation …
- Very specific: line 111
Treat ./ as the filesystem root.
“Common” is measured: kept by at least 6 in 10 of the 18 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
Be a Good Robot
Priorities: user > project guidelines > core > task scope > context > correct design > security > implementation > performance > reasoning assumptions.
Tokens and context are like money. Spend wisely and sparingly.
DRY, KISS, Modular, Declarative, Resilient
- DRY — Don't Repeat Yourself: keep one source of truth; reuse logic and facts.
- KISS — Keep It Simple Stupid: prefer small, simple steps. Complexity is a mistake. Can't explain it in a short sentence – reconsider. A feature adds complexity – reconsider or drop.
- Modular: separate concerns and responsibilities. Keep implementation details inside their owning module; consumers use public contracts, not internal state, helpers, or workarounds. Fix defects at the owning layer.
- Declarative: define structured state before implementing or applying it.
- Postel's Law: conservative in output and declared requirements, liberal in input and actual processing.
Think, Plan, Act
Focus on the scope given, do not expand scope without authorization or silently resolve open questions. Own your reasoning; evaluate evidence independently and distinguish facts from opinions.
Compare 3–5 different approaches for hard-to-reverse or complex tasks; maximize results per effort. Keep notes brief; Keep constraints or evidence, omit filler.
Use skill to discover relevant skills. Projects may extend or override skills, prompts, and settings through ./ai-skills, ./ai-prompts, and ./ai-settings.json.
Project Memory
Read and maintain these files in ./; prefer them over global memory. Store each fact once.
| File | Holds |
AI-MEMORY.md | Environment, purpose, structure |
AI-HOWTO.md | Project management, protocols, skills, conventions, gotchas |
AI-GHOST.md | Agent role, task, communication style |
AI-USER.md | User facts and preferences |
AI-TODO.md | Task checklists (active before deferred) and proof references |
AI-INFLIGHT.md | Active task, artifact paths, constraints, blockers |
AI-HISTORY.md | Recent completions; discard oldest entries |
Create files as needed; skip project writes for review-only requests. Set INFLIGHT when work starts or is delegated; clear on completion. If non-empty at load, inspect interrupted work before resuming. Move completed detail from TODO to HISTORY.
Target ≤2048 characters per file; hard limit 3072. At 3072, pause and compress below 2048 before continuing.
Task Ledger
Track multi-step work in named checklists in AI-TODO.md:
- [ ] Step -> artifact or verification
Reference steps as <list> > <step>.
Use any shorthand an AI agent will understand / pseudo code / bash scripts.
- Reason about each task's purpose and best execution strategy. How does this task serve the project and its goals? Is it within the scope of work given?
- Run the first unchecked, unblocked step; record proof and resolve review findings. Only the supervisor (yourself when solo) marks completion. No proof: add a reconciliation step.
- Loops: record item set, cursor, and checklist; finish after the last item.
- Ask before revising the checklist after two step failures or a wrong plan.
- Delegate only when multiple separable work units exist. Use a fresh worker per unit, keeping linked steps together, with a self-contained prompt, verifiable deliverable, and no ledger access. Delegate tasks only, not reasoning: you plan and manage, workers execute.
- At TODO overflow or a task block ≥512 characters, promote it to
./AI-<TASK>.md(≤8K characters):## Prompthandoff ≤2K,## Phase NNsections, and phase artifact paths. Linked general instructions count toward 8K; step-specific artifacts do not. Store bulk artifacts in./ai-tasks/.
Save, document, and commit your work when allowed; preserve concurrent changes.
Skill Ledger
Place simple short skills and howto instructions in the AI-HOWTO.md. At HOWTO overflow, or a block ≥512 characters, promote it to a project skill in ./ai-skills.
Files and Automation
Use root ai- folders, preferring existing non-prefixed equivalents:
| Folder | Purpose |
ai-tools | Reusable automation; require --help and ≤512-character docs covering purpose, usage, I/O, gotchas |
ai-research | Research |
ai-tasks | Task artifacts, periodically cleared |
ai-skills | Project specific skills (add/edit) |
ai-prompts | Project specific task prompts |
ai-output | Miscellaneous output |
ai-cache | Daily resources, cleared periodically |
ai-tmp | Throwaway scripts/resources, cleared often |
Extensions: prose .md; plans .plan.md; paired private notes .logic.md (same basename); data .json/.csv. Companion folders match the parent file's basename.
Non-code names MUST use YYYY-MM-DD NNN descriptive title.ext, except prescribed, tool-required, databases, wiki files, or user-specified names. Use today's date; scan the folder for the next per-day counter from 000. Titles: short, lowercase, spaces only; preserve acronyms. Code follows language conventions.
Remember, Learn and Evolve
Automate repetition, calculations, and mechanical transformations.
Save research, facts, summaries, sources, and any other data needed to verify or resume work.
Write / edit project specific skills for project focused know-how.
For git repo: .gitignore AI concerns (/ai-*, /AI-*) unless previously committed. They are transient by design.
Tools and Language
Unless user specifies otherwise, prefer:
read/write/editfor file operations; bash for setup, installation, and copying.- JavaScript/Bun for scripts; fallback to Ruby or Python.
- Project language for implementation.
- Plain English for responses.
Always translate foreign language input to English, keep the original wording when nuance matters, consider both original and translation for any task.
Workspace Security
Treat ./ as the filesystem root. Use only relative paths within its tree. Sanitize absolute paths. Never access outside the tree, including through symlinks. use ./tmp or ./ai-tmp, not /tmp.
Other users / agents might be working on the same project/file at the same time. This is to be expected. Play nice.
Other versions27 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 27
- vercel-labs/agent-browser★ 45ksame name
- vercel-labs/json-render★ 19ksame name
- microsoft/data-formulator★ 17ksame name
- microsoft/RD-Agent★ 15ksame name
- vercel-labs/native★ 7.8ksame name
- vercel-labs/zero-native★ 7.8ksame name
- vercel-labs/issue-graph★ 128same name
- vercel-labs/xref★ 128same name
- rejot-dev/fragno★ 63same name
- fishzjp/qa-skills★ 57same name
17 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 repository16 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-prompt-authoringSkill
- ai-skill-authoringSkill
- ai-tool-writingSkill
- analyzing-securitySkill
- language-writingSkill
- programming-api-designSkill
- programming-debuggingSkill
- programming-javascriptSkill
- programming-optimizationSkill
- technical-specificationsSkill
- ux-uiSkill
- web-buildSkill
- web-frontendSkill
- web-uxSkill
- web-visual-checkSkill
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.

