Best for
- user says "fix", "debug", "run this", "check the repo", or "push it"
- the task depends on command output, git state, test results, or a verified local fix
- the answer must distinguish changed locally, verified locally, committed, and pushed
affaan-m/ECC/skills/terminal-ops/SKILL.md
Evidence-first repo execution workflow for ECC. Use when the user wants a command run, a repo checked, a CI failure debugged, or a narrow fix pushed with exact proof of what was executed and verified.
Decision brief
Use this when the user wants real repo execution: run commands, inspect git state, debug CI or builds, make a narrow fix, and report exactly what changed and what was verified.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/affaan-m/ECC --skill "skills/terminal-ops"Inspect the Agent Skill "terminal-ops" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/terminal-ops/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Before changing anything:
the response names the proving command or test
Pull these ECC-native skills into the workflow when relevant:
user says "fix", "debug", "run this", "check the repo", or "push it"
Permission review
The documentation asks the agent to read local files, directories, or repositories.
inspect the file or testEvidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 81/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 234,327 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Use this when the user wants real repo execution: run commands, inspect git state, debug CI or builds, make a narrow fix, and report exactly what changed and what was verified.
This skill is intentionally narrower than general coding guidance. It is an operator workflow for evidence-first terminal execution.
Pull these ECC-native skills into the workflow when relevant:
verification-loop for exact proving steps after changestdd-workflow when the right fix needs regression coveragesecurity-review when secrets, auth, or external inputs are involvedgithub-ops when the task depends on CI runs, PR state, or release statusknowledge-ops when the verified outcome needs to be captured into durable project contextSettle:
Before changing anything:
Solve one dominant failure at a time:
Use exact status words:
SURFACE
- repo
- branch
- requested mode
EVIDENCE
- failing command / diff / test
ACTION
- what changed
STATUS
- inspected / changed locally / verified locally / committed / pushed / blocked
Alternatives
affaan-m/ECC
Review terminal-ops's use cases, installation, workflow, and original source instructions.
affaan-m/ECC
Review terminal-ops's use cases, installation, workflow, and original source instructions.
event4u-app/agent-config
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
K-Dense-AI/scientific-agent-skills
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.