Reddit Pain Workflow

Daily automated pipeline: Reddit scan → classify → generate report → push to GitHub → metrics tracking. Cron-friendly with short timeouts. Drives star growth...

Security Scan

The instructions describe a reasonable Reddit→report→GitHub pipeline, but the skill fails to declare required credentials and expected CLI/tools, which is a significant mismatch you should resolve before installing.

Purpose & Capability

The stated purpose (scan Reddit, classify, generate a report, push to GitHub, track metrics) matches the SKILL.md content. However, the SKILL.md expects external CLIs and tokens (GitHub token, Feishu/lark-cli app token, 'hermes' cron tooling, and local scripts) that are not declared in the registry metadata or requirements, creating an incoherence between claimed dependencies and actual needs.

Instruction Scope

Runtime instructions tell the agent to: call Reddit JSON APIs (OK), walk comment trees, generate DAILY-REPORT.md, commit and push to GitHub using an environment token, create cronjobs via a 'hermes' CLI, and call Feishu's API via lark-cli. The SKILL.md references local paths (~/HermesMade/scripts), environment variables (GITHUB_TOKEN) and external CLIs but the skill package does not supply these scripts, binaries, or declare required env vars—so the agent would be expected to access local files and network endpoints not described in the registry metadata.

Install Mechanism

This is an instruction-only skill (no install spec) which is lower-risk in that nothing is written by the skill itself. However, it instructs use of multiple external CLIs ('hermes', 'lark-cli') and local Python scripts that are not provided or documented as required binaries; the absence of declared tooling is a discrepancy to be resolved by the user before running.

Credentials

Registry metadata declares no required env vars, yet the SKILL.md explicitly uses os.environ['GITHUB_TOKEN'] for commits and implies Feishu/lark-cli credentials and possibly 'hermes' credentials. A GitHub token with repo write and traffic/read scopes is high-privilege; those credentials are not requested or scoped in the registry, which is disproportionate and unexpected.

Persistence & Privilege

The skill is not flagged 'always:true' and does not modify other skills. It does instruct creating persistent cron jobs (via 'hermes cronjob create') which would schedule autonomous network activity and commits. This is expected for a cron-driven pipeline but increases operational risk and requires careful credential handling; the skill itself does not request persistent platform privileges in the registry.

What to consider before installing

This skill looks like a plausible Reddit→GitHub pipeline, but the runtime instructions rely on tools and credentials that are not declared in the registry. Before installing or running it: 1) Don’t provide broad tokens blindly — the SKILL.md uses GITHUB_TOKEN for commits and traffic API access; create a least-privilege token limited to the specific repo and scopes required (repo:contents, repo:status, and traffic/read if needed). 2) Confirm or install the external CLIs it expects ('hermes', 'lark-cli') and ensure you understand their auth needs. 3) Inspect the local scripts it references (~/HermesMade/scripts/daily-pipeline, scripts/github-metrics) — the skill package doesn’t include them, so you must supply and audit them yourself. 4) Test on a throwaway repository first to validate behavior and cron setup. 5) Be cautious about the GitHub search optimization guidance (topics/description) — some suggested keywords (e.g., “censorship-bypass”) may have policy or reputation implications. The mismatch between declared requirements and actual instructions is a red flag; resolve these gaps and narrow credentials before proceeding.

Reddit Pain → GitHub Report Daily Pipeline

A fully automated cron-driven pipeline that scans Reddit for pain points, classifies them against existing tools, generates a daily report pushed to GitHub, and tracks repo metrics for growth.

When to use

• Building a data-driven open-source project that needs daily content
• Wanting automated pain discovery with GitHub as the delivery surface
• Need a growth engine: daily reports → discoverable on GitHub → drives stars

Architecture

Cron (8 AM daily)
  ↓
Reddit scan (5 subreddits, native .json API, 8s timeout)
  ↓
Pain classification (8 categories, matched against existing tools)
  ↓
DAILY-REPORT.md generation (markdown with quotes, links, tool candidates)
  ↓
Git commit + push to repo
  ↓
Metrics snapshot (stars, views, clones, search ranking)

Key Implementation Details

Reddit Scan

• Use https://www.reddit.com/r/{sub}/hot.json (no auth needed)
• 5 subreddits max for cron speed: ChatGPT, ClaudeAI, LocalLLaMA, programming, webdev
• Timeout: 8s per request, 0.5s delay between requests
• Walk comment tree to depth 2 for replies
• Pushshift.io is DEAD (403), PRAW needs client_id (skip for public)

Pain Classification

Categories match our tool coverage:

Category	Existing Tool
AI Censorship / Safety	prompt-inspector
AI Model Degradation	model-watch
AI API Pricing / Cost	api-cost
GitHub / CI-CD Issues	none yet
AI Code Quality	none yet
Local LLM / Deployment	none yet
Supply Chain Security	none yet
AI Detection / Deepfake	none yet

Threshold: ≥3 signals in a category with no existing tool → flagged as "New Tool Candidate"

GitHub Report Generation

• Output: DAILY-REPORT.md in repo root
• Format: summary → per-category top 3 quotes with permalinks → tool candidates → growth tip → metrics
• Auto-committed with timestamp, pushed to main

GitHub Search Optimization (Critical for Star Growth)

GitHub search indexes: repo description + topics (20 max) + lightly on README.

Recipe that worked (verified 2026-04):

1. Description: keyword-dense, comma-separated: "AI CLI tools: prompt censorship checker & bypass, model quality watchdog & degradation monitor, API cost comparison for OpenAI Claude DeepSeek Gemini. Built from real Reddit user complaints."
2. Topics (19): ai, python, cli, api, llm, openai, claude, deepseek, devtools, reddit, prompt-engineering, cost-optimization, benchmark, censorship, censorship-bypass, model-monitoring, model-degradation, cost-comparison, llm-pricing
3. Result: repo ranks #1 for searches like "model degradation monitor cli", "prompt censorship bypass cli", "llm cost comparison"

Metrics Tracking

Separate script scripts/github-metrics records:

• Stars, forks, watchers
• Views (from traffic API)
• Clones
• Search ranking for 7 target keywords

Cron Job Setup

# Daily pipeline (8 AM)
hermes cronjob create --name daily-reddit-pipeline --schedule "0 8 * * *" \
  --prompt "Run python3 ~/HermesMade/scripts/daily-pipeline run. Then present a 3-line summary."

# Daily metrics (9 AM)
hermes cronjob create --name github-metrics-daily --schedule "0 9 * * *" \
  --prompt "Run python3 ~/HermesMade/scripts/github-metrics snapshot then report."

Feishu Bitable Setup (one-time)

# Create app
lark-cli api POST /open-apis/bitable/v1/apps --data '{"name":"Pain Points"}'

# Create table with fields
lark-cli api POST /open-apis/bitable/v1/apps/{app_token}/tables --data '{
  "table": {
    "name": "痛点清单",
    "fields": [
      {"field_name": "序号", "type": 2},
      {"field_name": "分类", "type": 3},
      {"field_name": "痛点名称", "type": 1},
      {"field_name": "频次指数", "type": 3},
      {"field_name": "用户原声", "type": 1},
      {"field_name": "Hermes方案", "type": 1},
      {"field_name": "状态", "type": 3}
    ]
  }
}'

# Batch insert
lark-cli api POST "/open-apis/bitable/v1/apps/{token}/tables/{table}/records/batch_create" \
  --data "$(cat records.json)"

GitHub API via urllib (Fallback — No gh CLI Required)

When the github skill/tools aren't available but a GitHub token is, use Python stdlib urllib for file commits:

import json, base64, urllib.request

token = os.environ["GITHUB_TOKEN"]
repo = "owner/repo"
file_path = "path/in/repo.sh"

with open("/tmp/file.sh", "rb") as f:
    content = f.read()
encoded = base64.b64encode(content).decode()

# Step 1: Check if file exists (get SHA for update)
get_url = f"https://api.github.com/repos/{repo}/contents/{file_path}"
get_req = urllib.request.Request(get_url, headers={
    "Authorization": f"Bearer {token}",
    "Accept": "application/vnd.github+json",
    "User-Agent": "hermes-agent"
})

sha = None
try:
    with urllib.request.urlopen(get_req, timeout=10) as resp:
        sha = json.loads(resp.read()).get("sha")
except urllib.error.HTTPError as e:
    if e.code == 404:
        pass  # File doesn't exist, will create
    else:
        raise

# Step 2: PUT create or update
put_url = f"https://api.github.com/repos/{repo}/contents/{file_path}"
payload = {
    "message": "feat: auto-generated report [HERMES-N]",
    "content": encoded,
    "branch": "main"
}
if sha:
    payload["sha"] = sha

put_req = urllib.request.Request(put_url, 
    data=json.dumps(payload).encode("utf-8"),
    method="PUT",
    headers={
        "Authorization": f"Bearer {token}",
        "Accept": "application/vnd.github+json",
        "User-Agent": "hermes-agent"
    })

with urllib.request.urlopen(put_req, timeout=15) as resp:
    result = json.loads(resp.read())
    print(f"Committed: {result['commit']['sha'][:7]}")

Pitfalls:

• User-Agent header is required by GitHub API, otherwise 403
• Accept: application/vnd.github+json needed for newer API endpoints
• For binary files: base64 encode the raw bytes (no text decode step)
• For first commit on a new repo: file won't exist → 404 → omit sha

Pip Package Pattern (for individual tools)

Each tool is a standalone pip-installable package:

tool-name/
├── pyproject.toml       # build-backend = "setuptools.build_meta"
├── README.md
└── tool_name/
    ├── __init__.py
    └── cli.py

Install: pip install git+https://github.com/{user}/{repo}.git#subdirectory=tool-name

⚠ setuptools.backends._legacy:_Backend does NOT work. Use setuptools.build_meta.

Pitfalls

• Reddit cloud browser access: blocked by security. Use native API only.
• gh search repos has slower index than web UI search — web results may show repo that API doesn't yet
• Topic limit: 20 max. Remove generic ones (productivity, tools) to fit search-critical ones
• lark-cli is interactive on first run. Set LARK_LANGUAGE=zh env var before first use
• Pip install in sandbox: use --break-system-packages on macOS Homebrew Python