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Engineering Manager Intelligence — Team Performance & Project Health

Name: Engineering Manager Intelligence — Team Performance & Project Health
Author: larryfang

作者 larryfang · GitHub ↗ · v2.1.1 · MIT-0

cross-platform ✓ 安全检测通过

329

总下载

当前安装

版本数

在 OpenClaw 中安装

/install em-intel

功能描述

Engineering Manager Intelligence: track team performance, engineer contributions, and project health across GitLab/GitHub + Jira/GitHub Issues. Use when aske...

使用说明 (SKILL.md)

em-intel — Engineering Manager Intelligence

Track team performance, engineer contributions, and project health across GitLab/GitHub + Jira/GitHub Issues.

Agent Instructions (read this first)

When this skill is triggered:

1. Check if configured:

test -f \x3Cskill_dir>/.env && echo "configured" || echo "not_configured"

If not configured, say:

"em-intel needs a one-time setup. Run this and I'll guide you through it (takes ~2 min):" python3 \x3Cskill_dir>/em_intel.py setup

Or to preview without any credentials first: python3 \x3Cskill_dir>/em_intel.py morning-brief --dry-run

The setup command handles everything: asks questions, opens token pages in the browser, writes .env, installs deps, and runs doctor automatically.

If configured, run the requested command directly:

cd \x3Cskill_dir> && python3 em_intel.py \x3Ccommand> [--dry-run]

On error, run doctor and surface the failing checks:

python3 \x3Cskill_dir>/em_intel.py doctor

Quick Start (manual)

# 1. Copy and fill in your API keys
cp .env.example .env
# See SETUP.md for token URLs and required scopes

# 2. Install dependencies
pip3 install -r requirements.txt

# 3. Validate configuration
python3 em_intel.py doctor

# 4. Preview with mock data (no credentials needed)
python3 em_intel.py morning-brief --dry-run

# 5. Run for real
python3 em_intel.py morning-brief

Commands

Command	Description
`doctor`	Check env vars and test API connections
`morning-brief [--dry-run]`	Merged yesterday, open PRs >3d, quiet engineers, stalled epics
`eod-review [--dry-run]`	Today's merges/opens, contributor list, cycle time trend
`team-report [--days N] [--dry-run]`	Full team performance report
`contributions [--engineer NAME] [--days N] [--dry-run]`	Branch→ticket contribution map
`quiet-engineers [--dry-run]`	Engineers with no MR activity
`epic-health [--dry-run]`	Stalled and unassigned epics
`newsletter [--week]`	Weekly digest via configured delivery channel

--dry-run

Pass --dry-run to any supported command to use realistic synthetic mock data instead of hitting real APIs. Useful for previewing output format before configuring credentials.

Configuration

Set EM_CODE_PROVIDER to gitlab or github, and EM_TICKET_PROVIDER to jira or github_issues.

Delivery channels: telegram, slack, email, or print (stdout fallback).

See .env.example for all configuration options.

Architecture

em_intel.py          ← CLI entrypoint (argparse)
adapters/            ← Code platform + ticket system adapters
  base.py            ← Abstract base classes & data models
  gitlab_adapter.py  ← GitLab REST API
  github_adapter.py  ← GitHub REST API
  jira_adapter.py    ← Jira REST API
  github_issues_adapter.py ← GitHub Issues as ticket system
  mock_adapter.py    ← Synthetic data for --dry-run mode
core/                ← Business logic
  branch_mapper.py   ← Map branches → tickets → engineers
  team_pulse.py      ← Quiet engineers, MR trends, cycle times
  jira_health.py     ← Stale epics, unassigned tickets, PR age
  newsletter.py      ← Weekly digest generation
  delivery.py        ← Telegram / Slack / Email / Print routing
commands/            ← Command implementations
  morning_brief.py   ← Morning briefing
  eod_review.py      ← End-of-day review
  team_report.py     ← Full team report
  newsletter.py      ← Newsletter generation & delivery

安全使用建议

This skill appears to do what it says: collect data from GitHub/GitLab and Jira and produce reports. Before installing or running it: 1) Inspect requirements.txt and only install packages you trust (the setup runs pip install -r requirements.txt). 2) Prefer least-privilege tokens (read-only scopes) and separate service accounts where possible. 3) Be aware the setup writes credentials to a .env file in the skill directory (plaintext) — store it securely or use an env-file mechanism with proper filesystem protections. 4) Use the --dry-run to preview output without credentials. 5) If you must run the setup on a shared machine, consider running inside an isolated environment (Python venv or Docker) to limit side effects. Overall, the pieces are coherent with the stated purpose.

功能分析

Type: OpenClaw Skill Name: em-intel Version: 2.1.1 The em-intel skill is a legitimate engineering management tool designed to aggregate performance metrics from GitLab, GitHub, and Jira. While it handles sensitive API tokens and provides delivery via Slack, Telegram, and Email (core/delivery.py), the code logic is transparent and aligns strictly with its stated purpose. There is no evidence of data exfiltration, malicious execution, or prompt-injection attacks; the use of subprocesses in run.sh and setup_wizard.py is limited to standard environment configuration and dependency management.

能力评估

✓ Purpose & Capability

Name/description (team performance, contributions, Jira/GitHub/GitLab integration, delivery channels) matches the included adapters, report commands, and delivery code. The adapters expect GitLab/GitHub tokens, Jira tokens, and optional Slack/Telegram/SMTP settings — all consistent with the declared functionality.

ℹ Instruction Scope

SKILL.md and the setup wizard instruct the agent to check for a .env, run the CLI (em_intel.py) and optionally run a setup flow which opens token pages, writes a .env, installs dependencies, and runs doctor. These runtime actions access only the skill directory, prompt for tokens, and call the documented APIs; they do not instruct the agent to read unrelated system files or exfiltrate data to unknown endpoints. Note: the setup uses webbrowser.open and subprocess.run (pip, docker) which will execute on the host — expected for a CLI-style tool but worth noting.

ℹ Install Mechanism

There is no registry install spec; installation is manual. The setup wizard runs pip install -r requirements.txt and may build a Docker image. That is a standard pattern but introduces the usual risks of installing third-party Python packages and building/running containers — review requirements.txt and exercise usual caution before running pip/docker commands.

ℹ Credentials

The skill will require multiple credentials (GITLAB_TOKEN / GITHUB_TOKEN, JIRA_TOKEN/JIRA_EMAIL, and optional SMTP/SLACK/TELEGRAM credentials). Those are directly needed for the described integrations and delivery channels. Registry metadata lists no required env vars, which is a minor inconsistency; the skill expects the credentials to be supplied via a .env file created by the setup wizard. The setup persists secrets in plaintext .env under the skill directory — expected but a security consideration.

ℹ Persistence & Privilege

always:false (no forced presence). The setup wizard writes a .env file, may run pip install and docker build/run, and may create an em-intel Docker image. The skill does not modify other skills or system-wide config beyond typical dependency installation and writing its own config. These actions alter the local environment and should be run only on trusted machines.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install em-intel
安装完成后，直接呼叫该 Skill 的名称或使用 /em-intel 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v2.1.1

v2.1.1: Re-publish with all v2.1.0 changes — Docker support, run.sh launcher, setup wizard, HTML email, --dry-run on all commands.

v2.1.0

v2.1.0: Docker support — run.sh auto-detects Python or Docker and uses whichever is available. Dockerfile included. Setup wizard builds Docker image automatically. No Python required.

v2.0.0

v2.0.0: Interactive setup wizard (python3 em_intel.py setup) — asks questions, opens token pages in browser, writes .env, installs deps, runs doctor automatically. Auto-installs deps on first import. Welcome screen for new users. Zero manual steps to get started.

v1.5.0

v1.5.0: Added agent-guided first-run setup flow in SKILL.md. Added .env.example with all variables and inline docs. Users are now walked through config before any command runs.

v1.4.1

v1.4.1: Email sends HTML with clickable Jira links for epics/tickets (multipart/alternative, markdown dep added)

v1.4.0

v1.4.0: Email delivery now sends HTML with clickable Jira links (multipart/alternative — HTML + plain text fallback). Added markdown dep.

v1.3.0

v1.3.0: Added --dry-run to newsletter command. All 7 commands verified with real GitLab+Jira data and mock data.

v1.2.0

v1.2.0: Fixed contributions command (was timing out on large GitLab groups — now derives branches from MR source_branch, instant results). All 6 commands fully tested against real GitLab + Jira.

v1.1.0

v1.1.0: Added doctor command (validates all API connections), --dry-run mode (preview without credentials), SETUP.md with step-by-step token guides. Tested against real GitLab + Jira APIs.

v1.0.0

Initial release — branch→ticket contribution mapping, quiet engineer detection, epic health, morning brief, EOD review, newsletter. Supports GitLab + GitHub + Jira + GitHub Issues.

元数据

Slug em-intel

版本 2.1.1

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 10

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