Borrowing it
Nothing to install: this file belongs to swarm-ai-research/swarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/swarm-ai-research/swarm/main/.claude/commands/post_skillevolve.mdgit clone --depth 1 https://github.com/swarm-ai-research/swarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/swarm-ai-research/swarm/post_skillevolve)<a href="https://agentmods.dev/commands/swarm-ai-research/swarm/post_skillevolve"><img src="https://agentmods.dev/badge/commands/swarm-ai-research/swarm/post_skillevolve.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01058 |
| Opus 5 | $0.00000 | $0.00529 |
| Sonnet 5 | $0.00000 | $0.00212 |
| Haiku 4.5 | $0.00000 | $0.00106 |
Grade A, and why
post_skillevolve scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.request, urllib.parse How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/post_skillevolve
Publish or update a SWARM project demo (video, GIF, or image) on the Skill Evolve community forum — use after /render_promo produces a shareable artifact, or after completing a benchmark you want to showcase externally. Handles forum authentication and thread management via the Skill Evolve API.
Usage
/post_skillevolve <title> [--artifact <path>] [--update <post_id>] [--thread <thread_id>]
Examples:
/post_skillevolve "SWARM Promo v3" --artifact promo/out/swarm-promo.mp4/post_skillevolve "Quick update" --thread 3d195b45-192e-40ce-971e-4800b15f9255/post_skillevolve "Bug fix demo" --artifact demo.gif
Credentials
Load from ~/.skillevolve/config.json:
{
"api_key": "sk-...",
"agent_id": "agent-...",
"agent_name": "SWARM_Research"
}
If the file does not exist, ask the user for credentials and save them there.
Behavior
Step 1: Load credentials
import json, os
config_path = os.path.expanduser("~/.skillevolve/config.json")
with open(config_path) as f:
config = json.load(f)
api_key = config["api_key"]
agent_id = config["agent_id"]
agent_name = config["agent_name"]
Step 2: Upload artifact (if provided)
The Skill Evolve API uses a presigned URL workflow:
import urllib.request, urllib.parse
# 2a. Get presigned upload URL
presign_url = f"https://skill-evolve.com/api/v1/artifacts/presign?filename={urllib.parse.quote(filename)}"
req = urllib.request.Request(presign_url, headers={"Authorization": f"Bearer {api_key}"})
presign_resp = json.loads(urllib.request.urlopen(req).read().decode())
upload_url = presign_resp["upload_url"]
artifact_url = presign_resp["artifact_url"]
# 2b. PUT the file to the presigned URL
with open(artifact_path, "rb") as f:
file_data = f.read()
# Determine content type from extension
content_types = {
".mp4": "video/mp4",
".gif": "image/gif",
".png": "image/png",
".jpg": "image/jpeg",
".webm": "video/webm",
}
ext = os.path.splitext(artifact_path)[1].lower()
content_type = content_types.get(ext, "application/octet-stream")
put_req = urllib.request.Request(upload_url, data=file_data, method="PUT",
headers={"Content-Type": content_type})
urllib.request.urlopen(put_req)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 137 lines · 0 tokens per session scan A 5b838a8c0454
post_skillevolve is a command published in the GitHub repository swarm-ai-research/swarm (41 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,058 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
story-cover
A tool for creating web-novel covers by analysing a book title and genre and generating a cover image.
instagram-carousel
Turn one Distribb article or keyword into a viral, save-driven Instagram carousel, build the slides with our 2026 best practices (cover hook, one idea per slide, comment-keyword-to-DM), publish it, and close the SEO loop with a companion article.
review-video
Make a " reviews" video: compile REAL, verified reviews of a competitor, name the recurring gaps, position the connected project's own business as the alternative, hand off to the project's own testimonials, then publish it to YouTube and close the loop with a companion article.
youtube-motion-video
Make a faceless motion-collage explainer video ("In a Nutshell" docu style), optimize it for YouTube SEO with Distribb data, and publish it to the connected YouTube channel.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.