SkillAnything Skill Generator
AgentSkillOS/SkillAnything
Generates a complete agent skill for a target tool, API, library or workflow through a seven-phase pipeline that ends with testing, tuning and packaging for several platforms.
Create, configure, and maintain custom agent profiles and author new skills via the repl tool.
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add JimLiu/science-skills --skill customize -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install JimLiu/science-skills customize --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/customize .claude/skills/customize && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .claude/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/JimLiu/science-skills/tree/main/skills/customizeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add JimLiu/science-skills --skill customize -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install JimLiu/science-skills customize --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/customize .agents/skills/customize && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .agents/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add JimLiu/science-skills --skill customize -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install JimLiu/science-skills customize --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/customize .cursor/skills/customize && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .cursor/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/JimLiu/science-skills.git --path skills/customize--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add JimLiu/science-skills --skill customize -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install JimLiu/science-skills customize --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/customize .gemini/skills/customize && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .gemini/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install JimLiu/science-skills customizeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add JimLiu/science-skills --skill customize -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/customize .github/skills/customize && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .github/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add JimLiu/science-skills --skill customize -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install JimLiu/science-skills customize --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/customize .opencode/skills/customize && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "customize" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/customize into .opencode/skills/customize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customize", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
customizeCreate, configure, and maintain custom agent profiles and author new skills via the repl tool.
Customize is an agent skill from JimLiu/science-skills. Create, configure, and maintain custom agent profiles and author new skills via the repl tool. Use when the user wants to create an agent profile, build a custom agent, modify agent capabilities, attach or detach skills/connectors on a profile, author a skill, or inspect which connectors and tools are available. Also use whenever you need the host.agents. or host.skills. Python SDK.
Its SKILL.md is about 5.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Skill authoring. It works with Python. The licence is Apache-2.0.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit fb309c3. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Customize loads about 5.4k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 1,938 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
teardown. Do NOT tell the user they'll see N cards for N deletes.Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from JimLiu/science-skills at commit fb309c3, republished under its Apache-2.0 licence (© JimLiu). 1,938 words, ~5,392 tokens.
.claude/skills/customize/SKILL.md (or your agent's skills folder).Build and maintain agent profiles and skills programmatically via the
repl tool using host.agents.* and host.skills.*.
A profile is a named bundle that shapes how an agent behaves:
system_prompt — the profile's identity. This is the opening of the
agent's system prompt; it REPLACES the generic "You are Claude Science" base identity.
Write it in second person, lead with You are {display_name}, ..., state what
the agent specializes in and what it does NOT do. Everything else (tool-usage
rules, working-style bullets, scope guardrail) is inherited automatically —
don't restate it.display_name / description / icon_key / color_key — picker metadata.skill_names (optional restriction) — by default a profile sees the
full live skill catalog via search_skills / skill(...), same as the
main agent. Pass an explicit list ONLY to deliberately restrict it; [] creates
a zero-skill specialist. Restricting skills also restricts connectors —
a single unrestricted flag governs both; passing skill_names flips the
profile to curated mode and starts it with zero connectors (see next).detach_connector to subtract specific ones. A curated
profile (one created with an explicit skill_names list, or flipped via
{"unrestricted": False}) starts with no connectors — reach is exactly
what you attach_connector.excludedTools — per-tool blocklist applied after connectors resolve.
Use to strip specific high-risk or irrelevant tools from an otherwise-useful
connector. Per-connector, not a profile field — set via
attach_connector(..., include_tools_pattern=/exclude_tools_pattern=); the
profile's excludedTools in list() is the read-only aggregation across all
its attached connectors. Patterns match the connector's bare tool names
(e.g. '^list_marts$', as returned by list_connectors(name)['tools']); the
aggregated excludedTools entries are stored fully-qualified as
mcp_<connector>_<tool> since the list spans every attached connector.All calls run via the repl tool (see "Runs via the repl tool"
below). Return values are plain dicts/lists; errors raise RuntimeError
with a host.agents.*: / host.skills.*: prefix.
host.agentshost.agents.list()
# → [{"name", "displayName", "description", "source", "enabled",
# "systemPrompt", "iconKey", "colorKey",
# "skillNames": ["skill", ...], "connectors": ["name", ...],
# "excludedTools": [...]}, ...]
# (Return-dict keys are camelCase — wire shape, not kwarg names.)
# `connectors` is a list of connector names (strings, same as skillNames) —
# pass one to attach_connector/detach_connector or list_connectors(name).
host.agents.create(name, display_name, description,
system_prompt="", skill_names=None)
# name: 2–32 chars, UPPERCASE letters / digits / underscores only —
# e.g. "RNASEQ_REVIEWER". Lowercase / dashes are rejected.
# display_name is the human-friendly picker label.
# skill_names controls catalog visibility:
# - leave it unset → the profile sees the FULL live skill catalog, same as
# the main agent — skills published later appear automatically. It also
# gets every connector the main agent has, resolved dynamically — new
# connectors added later appear automatically too. This is the default;
# don't pass a list unless you mean to restrict.
# - pass a list (including []) → restricts `search_skills`/`skill(...)` to
# EXACTLY those names. [] creates a zero-skill specialist.
# → the stored profile record (same shape as one list() entry)
host.agents.update(name, patch)
# patch: dict of fields to change — any of display_name, description,
# system_prompt, skill_names, unrestricted, icon_key, color_key
# (camelCase also OK).
# patch["skill_names"] is an EXACT REPLACE of the whole skill list and flips
# the profile to restricted mode — anything you omit is DETACHED. To add or
# remove a few skills, use attach_skill / detach_skill — they work on both
# restricted AND unrestricted profiles without changing the mode.
#
# If you do need a full-list replace, CHECK cur["unrestricted"] FIRST: on an
# unrestricted profile cur["skillNames"] is the lossy disk-cache view (not
# the full live catalog), so `cur["skillNames"] + ["x"]` would permanently
# freeze the profile to that partial list. The safe pattern:
# cur = [a for a in host.agents.list() if a["name"] == name][0]
# if cur["unrestricted"]:
# host.agents.attach_skill(name, "new-skill") # stays unrestricted
# else:
# host.agents.update(name,
# {"skill_names": cur["skillNames"] + ["new-skill"]})
# (return-dict keys are camelCase — read "skillNames", write "skill_names")
# patch["unrestricted"] = True → back to the full live catalog + all
# connectors (undoes a skill_names restriction).
# → updated profile record
# (excludedTools is NOT a patch field — it's per-connector; use
# attach_connector's include_tools_pattern/exclude_tools_pattern below.)
host.agents.switch(name)
# Ask to continue THIS conversation as `name`. Shows the user an approval
# card; on Allow, the switch takes effect on their NEXT message (the current
# turn finishes as the current profile). On decline, tell the user they can
# select the profile from the session config popover on any new conversation.
# → {"switched": True, "name", "displayName"}
host.agents.delete(name)
# → {"deleted": name}
host.agents.attach_skill(name, skill)
host.agents.detach_skill(name, skill)
# → updated profile record
host.agents.attach_connector(name, connector,
include_tools_pattern=None,
exclude_tools_pattern=None)
host.agents.detach_connector(name, connector)
# → updated profile record. Omit both patterns on a fresh attach to expose
# every tool the connector offers; re-attaching an already-attached
# connector without patterns preserves its existing exclusion list (pass
# include_tools_pattern='.*' to clear it). Patterns match the connector's
# BARE tool names (as returned by list_connectors(name)['tools'], e.g.
# '^list_marts$'); the resulting excludedTools entries are stored
# fully-qualified as mcp_<connector>_<tool>.
host.agents.list_connectors(connector_name=None)
# no arg → [{"name", "displayName", "source", "description",
# "authState", "attachedAgents": [...]}, ...]
# with connector_name → single dict with an extra
# "tools": [{"name", "description"}, ...]host.skillshost.skills.list()
# → [{"name", "origin", "description"}, ...]
# origin: "anthropic" (bundled, read-only — fork under a new name),
# "organization"/"personal" (editable), "draft" (local, unpublished)
host.skills.read(name, path="SKILL.md")
# → {"name", "path", "content": "..."}
host.skills.edit(name, path, content, old_string=None)
# old_string=None → create `path` with `content` (fails if the file already
# exists — read it, then edit with a non-empty old_string)
# old_string=str → str_replace the single exact match (rejected unless it
# matches exactly once — add surrounding context if needed)
# → {"action", "path", "draft_path", "note"}
host.skills.publish(name, overwrite=False)
# publish takes NO content args — write SKILL.md via .edit() first.
# → {"status": "published", "skill_id", "name", "note"}
host.skills.delete(name)
# draft → removes local dir; org/personal → unpublishes + removes local
# cache; anthropic bundled → protected.
# → {"deleted": name} (plus "unpublished": True for published skills)repl toolhost.agents.* / host.skills.* execute in the control-plane
kernel — a separate Python process from your python cells, reached via
the repl tool (not the python tool). It shares your workspace
directory (cwd) but not memory, so variables from python cells aren't
visible there and vice-versa. To hand results across, write to a file —
same pattern as Python↔R:
# repl tool
import json, os
os.makedirs("handoff", exist_ok=True)
profiles = host.agents.list()
json.dump(profiles, open("handoff/agents.json", "w"))# python tool
import json, pandas as pd
profiles = json.load(open("handoff/agents.json"))
pd.DataFrame(profiles)[["name", "source", "enabled"]]Do not call generate_plan for profile CRUD. This is a single
scope→draft→confirm loop; ask_user (step 4) is the review gate. A plan
adds a second approval that duplicates the ask_user confirmation and
drags in step-status bookkeeping that fights this workflow.
User approval. Most host.agents.* / host.skills.* calls apply
immediately — create/update (including unrestricted),
attach_*/detach_*, and skills.publish/skills.edit are pre-approved
at session start and the cell does NOT pause. An approval card (cell
pauses, resumes automatically on Allow — you don't retry) appears only
for the calls that hand off identity or free a granted name:
host.agents.switch(name) — per-target; Allow covers this name onlyhost.agents.update(name, {"name": ...}) (rename) — per-targethost.agents.delete(name) / host.skills.delete(name) — one card
per project: the first delete shows a card; "Allow for this project"
covers every subsequent delete in this project, including bulk
teardown. Do NOT tell the user they'll see N cards for N deletes.The name is read from the runtime call for switch and rename, so both
literal and variable names work (host.agents.switch("FOO") or
host.agents.switch(name_var)); keep the ask_user review step
so the click is a quick confirm, not a surprise. After the call returns,
read back (host.agents.list()) to confirm the actual state — don't
narrate an expected card.
Call host.agents.list() to see the user's current profiles so you don't
duplicate an existing agent. The main agent's bundled profile is protected —
it cannot be renamed or deleted.
What is this agent for? A profile should have one job. Pick an UPPER_SNAKE
name (RNASEQ_REVIEWER, not RNA-seq review helper). Use ask_user if the
name or scope is unclear — profiles are user-visible in the picker.
system_prompt is the agent's opening paragraph — it replaces the base
identity, it's not an addendum. Lead with You are {display_name}. State the
specialization and the boundaries ("You handle X, Y, Z. You do not handle
..."). Keep it under ~200 words; the heavy how-to lives in skills, not the
prompt.
Before creating, ask_user whether this profile should have full access
(the live skill catalog and every connector — same reach as the main agent;
new skills and connectors appear automatically) or a restricted subset
(a fixed list you'll curate together). Don't infer this from the role
description — a narrowly-described specialist may still want full reach,
and a broadly-described one may want a tight loadout. Pair this with the
name/prompt review in step 4 so it's one round-trip.
Show the proposed name, display name, description, system_prompt, and the
user's full-vs-subset choice from step 3. ask_user to confirm before
writing. If they chose a subset, list the proposed skills/connectors here.
Call host.agents.create(name, display_name, description, system_prompt=...).
If the user chose full access, leave skill_names unset. If they chose a
subset, pass skill_names=[...] with the agreed list — this flips the
profile to curated mode and starts it with zero connectors, so attach the
agreed connectors after create as in step 6. For edits to an existing profile,
use host.agents.update(name, {...}) with targeted fields; prefer
host.agents.attach_skill(...) / host.agents.detach_skill(...) and
host.agents.attach_connector(...) / host.agents.detach_connector(...)
over wholesale skill_names replacement so you don't clobber the user's own
edits.
After the profile exists, offer to switch to it:
host.agents.switch(name). The user sees an approval card; on Allow, this
conversation continues as the new specialist from their next message. If they
decline, tell them they can select it from the session config popover on any
new conversation.
If the user chose a subset in step 3, curate after create:
host.agents.update(name, {"skill_names": [...]}) with the
exact list, or detach_skill one at a time. Check host.skills.list()
for available names. On update, skill_names is an exact replace — never
send a partial list to "add"; fetch the current skillNames (camelCase in
the return dict), modify, send back.skill_names
list, or flipped via {"unrestricted": False}) starts with NO
connectors — reach is exactly what you attach. Call
host.agents.list_connectors() to see every available connector (bundled +
directory + user-added MCP) with its auth state, then
host.agents.attach_connector(name, connector_name) for each connector
the user agreed to keep. Use include_tools_pattern=/exclude_tools_pattern=
on the attach call to scope which tools the profile gets (omit both to
expose every tool; re-attaching without patterns preserves the existing
exclusion list — pass include_tools_pattern='.*' to clear). A connector
with authState other than "authorized" or "not-required" must be
connected via the Connectors panel before it can be attached.
detach_connector only removes an explicit attachment; on a curated
profile there is nothing to detach — don't use it to "restrict".host.agents.get(name)["connectors"] to confirm
the reach matches what you told the user.host.agents.update(name, {"unrestricted": True}).After the profile exists, propose a conda environment for it: name it after the
profile (lowercase slug, e.g. rnaseq-reviewer), pick python or r based on
the skills you attached, and list the packages those skills need. ask_user to
confirm the package list, then call
manage_environments(mode="create", name="<slug>", packages=[...]) and, if
anything further is needed,
manage_packages(mode="install", environment="<slug>", packages=[...]). The
profile uses this env by default; the system-owned python and r skeletons
remain available as fallbacks.
system_prompt. Full
catalog access by default — restrict the loadout only when the user asks.system_prompt that belong in a skill.For the full skill-authoring guide (anatomy, progressive disclosure, eval loop, description optimization), first load it:
skill({"skill": "skill-creator"})Then use the host.skills SDK via the repl tool to write and publish:
# draft
host.skills.edit("my-skill", "SKILL.md", """---
name: my-skill
description: ...
---
# My Skill
...
""")
# bundle a helper
host.skills.edit("my-skill", "kernel.py", "def helper(x): ...\n")
# inspect / iterate
print(host.skills.read("my-skill")["content"])
# publish to the live skill set
host.skills.publish("my-skill")Once published, the skill is in the live catalog — every unrestricted profile
(including the main agent and any profile created with the default) sees it
immediately. For a restricted profile, attach it explicitly with
host.agents.attach_skill(profile_name, "my-skill").
kernel.py / kernel.R)If a skill's workflow depends on reusable helper functions, ship them as
kernel.py (and/or kernel.R) at the skill root. When any agent calls
skill({skill: <name>}), that file is executed in its persistent python/R kernel and
the tool result reports which top-level names were defined — so SKILL.md can
say "call annotate_df(df)" and the function already exists.
Sidecars are validated before execution so that loading a skill only defines names — nothing author-written runs at load time. Allowed at the top level:
def / async def (no decorators). Default argument values must be
literals — def f(url=MY_CONSTANT) gets the whole file rejected. Wrap
constants with an explicit is None check: def f(url=None): then
if url is None: url = MY_CONSTANT (not url = url or MY_CONSTANT,
which also replaces 0, "", []).import / from … import name (no *). Defer third-party imports to
inside function bodies — the skeleton python env ships stdlib + a small
starter set (numpy, pandas, scipy, matplotlib, seaborn, pillow), so e.g.
import requests at module scope surfaces a load error on every fresh
kernel. Import errors don't fail the skill load; the agent sees the
traceback and can manage_packages then re-load.VERSION = "1",
LIMITS = (1, 2, 3)). Computed values like os.path.join(...) are
rejected — move them into a function body.Anything else at the top level (classes, calls, if/for, non-literal
assigns) is rejected with [kernel.py rejected] …. Names starting with _
are reserved by the loader and cannot be bound at the top level (use
import os and reference os.path inside functions, not
import os as _os). Function bodies are not restricted — they run only
when the agent calls them in a python cell.
Keep kernel.py small and self-contained (same guidance applies to
kernel.R). If a helper wants more than ~100 lines, trim it to the core
operations the agent actually calls; scripts/ is for standalone CLI
tools run via bash, not for backing the sidecar. For kernel.py, the
skill directory is not on sys.path, so from scripts.X import … fails
— but the directory is on disk and readable. A Python sidecar function
can locate it via its own co_filename to shell out to a scripts/ tool:
# kernel.py
import os, sys, subprocess
def run_pipeline(cfg_path):
here = os.path.dirname(sys._getframe().f_code.co_filename)
if not here:
raise RuntimeError("skill dir unavailable in this runtime")
tool = os.path.join(here, "scripts", "pipeline.py")
return subprocess.run([sys.executable, tool, cfg_path],
capture_output=True, text=True, check=True).stdout__file__ is not set (sidecars share one kernel namespace, so a global
__file__ would point at whichever skill loaded last). co_filename is
per-function and normally points at this skill's on-disk kernel.py; in
rare cases (e.g. when the loader can't resolve the skill dir on disk) it
falls back to the bare name "kernel.py" — hence the if not here guard
above.
The runtime exports PYTHONSAFEPATH=1, which the subprocess.run child
inherits — so python scripts/pipeline.py does NOT put scripts/ on the
child's sys.path. A multi-file tool that does sibling imports must add
sys.path.insert(0, os.path.dirname(__file__)) at the top of its entry
script (or pass env={**os.environ, "PYTHONSAFEPATH": ""} to
subprocess.run). If the child exits non-zero, capture_output=True
hides the traceback — inspect CalledProcessError.stderr.
kernel.R has no runtime analogue of co_filename (the R loader parses
with keep.source=FALSE), so an R sidecar that needs to reach scripts/
should take the path as an argument from the caller.
Minimal example:
# kernel.py
import pandas as pd # starter-set package — OK at module scope
def annotate_df(df: pd.DataFrame, gene_col: str = "gene") -> pd.DataFrame:
"""Attach HGNC symbols; see SKILL.md ## Workflow step 3."""
import requests # not in starter set — defer to function body
...
return dfWrite sidecars via host.skills.edit(name, "kernel.py", src). When the
gate probe can judge, the edit result carries sidecar_gate: {ok, error?}
— the same structural gate the load path runs — so a reject (non-literal
default, _-prefixed name, top-level call) surfaces immediately. When the
probe can't judge (interpreter unavailable, or the source doesn't parse
under the host's interpreter — possible version drift), the key is absent
and note says why. Iterate against that; don't load the skill just to
test the sidecar. host.skills.publish refuses only on a structural
reject.
© JimLiu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/customize of JimLiu/science-skills.
Open the folder on GitHubat commit fb309c3
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in JimLiu/science-skills, which our catalogue first saw on October 7, 2026.
Customize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Customize this skillJimLiu/science-skills | 227 | 2 repos | ~5.4k | Automated safety check: Warn | Apache-2.0 | |
| SkillAnything Skill GeneratorAgentSkillOS/SkillAnything | 471 | — | ~1.9k | Automated safety check: Pass | MIT | |
| DBS Skill Makerdontbesilent2025/dbskill | 11k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Skill Creatorluongnv89/asm | 953 | — | ~5.3k | Automated safety check: Pass | MIT | |
| Run History Skill Builderdongshuyan/compass-skills | 751 | — | ~1.8k | Automated safety check: Pass | MIT | |
| CLI-Anything for CodexHKUDS/CLI-Anything | 52k | — | ~1.5k | Automated safety check: Pass | Apache-2.0 |
AgentSkillOS/SkillAnything
Generates a complete agent skill for a target tool, API, library or workflow through a seven-phase pipeline that ends with testing, tuning and packaging for several platforms.
dontbesilent2025/dbskill
Turns a problem you keep running into into a single installable, tested skill, and prepares a GitHub repository only when you ask to share it.
luongnv89/asm
Create a skill or bring an existing one up to the same standard (validate + asm eval fix loop); run evals, tune triggering.
dongshuyan/compass-skills
Turn a completed task, browser flow, artifact pipeline, failure-recovery trace, or repeatedly refined workflow into a new reusable skill package or a reviewed skill-design plan.
HKUDS/CLI-Anything
Lets Codex build, refine, test, validate and list CLI-Anything harnesses for GUI applications or source repositories, following the project's full methodology.
rohitg00/ai-engineering-from-scratch
Validates an Agent Skill package against a portable contract with a Python checker, then picks the smallest set of instruction, capability or lifecycle primitives for the task.
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
JimLiu/science-skills
Set up a compute environment on a remote provider so Claude Science jobs can run there.
JimLiu/science-skills
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi.
JimLiu/science-skills
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model.
JimLiu/science-skills
Embed proteins with Meta AI's ESM-2 (fair-esm package). An agent skill from JimLiu/science-skills.
JimLiu/science-skills
Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab.
Works with
Categories
Create, configure, and maintain custom agent profiles and author new skills via the repl tool. Customize is an agent skill from JimLiu/science-skills. Create, configure, and maintain custom agent profiles and author new skills via the repl tool.
Customize fits situations like: the user wants to create an agent profile; build a custom agent; modify agent capabilities; detach skills/connectors on a profile.
Run `npx skills add JimLiu/science-skills --skill customize -a claude-code`. Or copy the skill folder (skills/customize in JimLiu/science-skills) into .claude/skills/customize in your project. Claude Code loads it when a task matches its description.
Run `npx skills add JimLiu/science-skills --skill customize -a codex`. Or copy the skill folder (skills/customize in JimLiu/science-skills) into .agents/skills/customize in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JimLiu/science-skills --skill customize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customize, .gemini/skills/customize, .github/skills/customize and .opencode/skills/customize in your project.
Going by SKILL.md and its folder, Customize needs the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way.
Customize is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.4k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Customize: SkillAnything Skill Generator (AgentSkillOS/SkillAnything, 471 stars), DBS Skill Maker (dontbesilent2025/dbskill, 11k stars), Skill Creator (luongnv89/asm, 953 stars) and Run History Skill Builder (dongshuyan/compass-skills, 751 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
JimLiu (a GitHub user) maintains it in JimLiu/science-skills, which has 227 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on July 1, 2026.
Source: JimLiu/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.