skill-loader
Skill4 starsChanged 9 months agoMITView on GitHub
Reads the remote skill catalog from GitHub, analyzes project context, recommends relevant skills, and fetches them for the session.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- julweber/ai_agent_skillstheir own version, first seen Mar 20, 2026
- Kubementat/ai_agent_skillstheir own version, first seen Mar 20, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- julweber/ai_agent_skills★ 2same name
- Kubementat/ai_agent_skills★ 2same name
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 repositoryOne other file it gives its agents.
- skill-cleanupSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
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
- Very specific: line 33
Run this to get the README (substitute your configured SKILLS_REPO_API):
- Distinctive: line 39
Read and understand the full catalog of available skills and their descriptions.
- Distinctive: line 43
Check each of the following (skip silently if not present):
- Distinctive: line 143
Write one skill name per line, listing every skill that was fetched in this session.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill Loader
<!-- ============================================================
CONFIGURATION — edit these values before using this skill
============================================================ --><!--
GITHUB_USERNAME: your GitHub username (e.g. acme-corp)
REPO_NAME: the repo that holds your skills (e.g. my-claude-skills)
SKILLS_REPO_URL: raw GitHub URL to your catalog README
SKILLS_REPO_API: GitHub API base path for the repo (no trailing slash)
PERMANENT_SKILLS: skills that are always locally installed — never fetch,
never add to the manifest, never clean up
-->GITHUB_USERNAME: your-github-username
REPO_NAME: your-skills-repo
SKILLS_REPO_URL: https://raw.githubusercontent.com/your-github-username/your-skills-repo/main/README.md
SKILLS_REPO_API: repos/your-github-username/your-skills-repo
PERMANENT_SKILLS: skill-loader, skill-cleanup (add any others here, e.g. a brand-reference skill)
<!-- ============================================================ -->Load only the skills you need for this session from the central skill repository.
Step 1 — Fetch the catalog
Run this to get the README (substitute your configured SKILLS_REPO_API):
bash -c 'source ~/.bashrc && gh api {SKILLS_REPO_API}/contents/README.md --jq ".content" | base64 -d'Read and understand the full catalog of available skills and their descriptions.
Step 2 — Collect project context signals
Check each of the following (skip silently if not present):
# What files exist in the working directory?
ls -la
# Package manager / language signals
cat package.json 2>/dev/null | head -30
cat requirements.txt 2>/dev/null | head -10
cat go.mod 2>/dev/null | head -5
cat Cargo.toml 2>/dev/null | head -5
cat composer.json 2>/dev/null | head -10
# Project instructions
cat CLAUDE.md 2>/dev/null
cat .claude/CLAUDE.md 2>/dev/null
# Infrastructure signals
cat Dockerfile 2>/dev/null | head -20
cat docker-compose.yaml 2>/dev/null | head -20
cat docker-compose.yml 2>/dev/null | head -20Also note:
- What task or goal has the user stated for this session?
- What file extensions dominate the working directory?
Permanent residents (always available — never fetch, never add to manifest)
These skills (defined in PERMANENT_SKILLS above) are installed locally at all times. Do not recommend fetching them and do not write them to .session-skills:
(Replace this list with your configured PERMANENT_SKILLS values)
skill-loaderskill-cleanup
Step 3 — Generate recommendations
Based on the catalog descriptions and project signals, select skills that are directly relevant to this project and session. For each recommendation, give one sentence of reasoning tied to a specific signal. Skip any permanent residents — they are always available without fetching.
Aim for 3–8 skills. Be selective — do not recommend everything. Skills you don't recommend are still available if asked.
Step 4 — Present and confirm
Present in this format:
Based on [key signals observed], I recommend fetching these skills:
✓ `skill-name` — reason tied to specific signal
✓ `skill-name` — reason tied to specific signal
...
Also available but not recommended for this project: skill-a, skill-b, skill-c
Confirm this list, adjust it, or add skill names?Wait for the user to respond before fetching anything.
Step 5 — Fetch approved skills
For each confirmed skill name, run (substitute your SKILLS_REPO_API):
# Fetch SKILL.md
bash -c 'source ~/.bashrc && \
SKILL=<skill-name> && \
mkdir -p ~/.claude/skills/$SKILL && \
gh api "{SKILLS_REPO_API}/contents/$SKILL/SKILL.md" --jq ".content" | base64 -d > ~/.claude/skills/$SKILL/SKILL.md && \
echo "Fetched: $SKILL/SKILL.md"'Then fetch any reference files for that skill:
bash -c 'source ~/.bashrc && \
SKILL=<skill-name> && \
REFS=$(gh api "{SKILLS_REPO_API}/git/trees/main?recursive=1" --jq ".tree[] | select(.path | startswith(\"$SKILL/references/\")) | .path") && \
if [ -n "$REFS" ]; then
mkdir -p ~/.claude/skills/$SKILL/references
echo "$REFS" | while IFS= read -r path; do
fname=$(basename "$path")
gh api "{SKILLS_REPO_API}/contents/$path" --jq ".content" | base64 -d > ~/.claude/skills/$SKILL/references/$fname
echo "Fetched: $path"
done
fi'Repeat for each skill in the confirmed list.
Step 6 — Write the session manifest
cat > ~/.claude/skills/.session-skills << 'MANIFEST'
<skill1>
<skill2>
<skill3>
MANIFESTWrite one skill name per line, listing every skill that was fetched in this session.
Step 7 — Confirm
Report which skills were fetched and are now active. Remind the user:
When you're done with this session, invoke
skill-cleanupto remove these skills and keep~/.claude/skills/lean.
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.

