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promql-cli

价格未确认★ 203 GitHub Stars目录更新于 · 2026年9月6日agent-skill

概览

CLI for querying Prometheus and PromQL-compatible engines (Thanos, Cortex, VictoriaMetrics, Grafana Mimir, Grafana Tempo...) — instant queries, range queries, metric discovery (metrics/labels/meta subcommands), output formats (table/csv/json/graph). Apply when executing PromQL queries, troubleshooting performance issues on a software having observability, investigating latency/error rates/saturation, or analyzing time series data.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

promql-cli — Prometheus Query CLI Skill

promql-cli (github.com/nalbury/promql-cli) is a Go CLI for querying, analyzing, and visualizing Prometheus metrics, plus PromQL fundamentals.

Reference Files

Read the relevant reference file(s) before executing tasks:

File	When to read
`references/installation.md`	User needs to install promql-cli or set up configuration (hosts, auth, token, password, multi-host)
`references/usage.md`	User wants to discover metrics/exporters/labels, run queries, or choose output formats
`references/graphing.md`	User wants to visualize Prometheus data as an ASCII chart in the terminal
`references/debugging.md`	User is investigating a performance issue, latency, errors, saturation, data gaps, or query cost issues
`references/promql-reference.md`	User needs help writing PromQL, understanding metric types, functions, or aggregations

For most tasks, read references/usage.md. For PromQL help, read references/promql-reference.md. When debugging, read both references/debugging.md and references/promql-reference.md.

Setup Check

Before running any query, verify that a host is configured:

promql 'up'   # succeeds if host is reachable; fails with connection error if not configured
# or
promql --host xxx 'up'

Recognize these errors as a configuration/auth problem and refer to references/installation.md:

Error	Cause
`dial tcp ... connection refused`	No host running at the configured address
`dial tcp ... no such host`	Hostname not resolved — wrong host in config
`error querying prometheus: ...401...`	Bearer token missing or invalid
`error querying prometheus: ...403...`	Token valid but insufficient permissions
`please specify an authentication type`	Auth flags partially set — use config file instead

If any of these appear, do not create config files on behalf of the user — config files may contain credentials (tokens, passwords) that must never pass through an LLM. Instead, guide the user to set it up themselves:

"Please create ~/.promql-cli.yaml manually with your Prometheus host (and credentials if needed). See references/installation.md for the exact format. Let me know once it's ready."

Only after the user confirms the config is in place should you proceed with queries.

Quick Command Reference

promql 'up'                                          # instant query
promql 'rate(http_requests_total[5m])' --start 1h    # range query (ASCII graph)
promql 'up' --output csv                             # CSV output
promql 'up' --output json                            # JSON output
promql metrics                                       # list all metric names
promql labels <metric>                               # list labels for a metric
promql meta <metric>                                 # show metric type and help
promql --config ~/.promql-cli-prod.yaml 'up'         # target a specific host

Key Principles

Use rate() on counters, never raw values — raw counters only ever increase; the absolute value is meaningless. rate() gives the per-second change rate, which is what you actually care about.
When debugging, isolate a single instance — aggregating across replicas masks per-instance anomalies. A single overloaded pod hidden behind healthy peers won't show up in averages.
Filter early with label matchers in the innermost selector — Prometheus evaluates selectors before functions, so filtering late means scanning all time series. Early filters reduce data scanned and query latency.
For histograms, keep le in the by clause before histogram_quantile() — the function needs all le buckets to interpolate percentiles; dropping le early produces NaN or wrong results.
Prefer --output graph for range queries — ASCII sparklines convey trend direction (rising, falling, spiking) in a compact format that LLMs parse well; raw timestamp tables require mental modeling. Never send thousands of raw JSON/CSV rows into the LLM context — use --output graph instead, or run --output graph first and --output table only to inspect a narrow window.
Store credentials in ~/.promql-cli.yaml and ~/.promql_token, chmod 600 — passing tokens as CLI args exposes them in shell history and process listings.

Query Cost Rules

Always apply these before and during any query session:

Always use the promql CLI — never call the Prometheus HTTP API from Python scripts or shell curl. The CLI handles auth, formatting, and output consistently; Python API calls bypass all of that and produce raw JSON that must be parsed, inflating context and masking the graph output that models interpret best.
Check cardinality first — before querying an unfamiliar metric, count its time series (count(metric_name)). High-cardinality metrics without label filters time out or flood the output. See references/debugging.md for patterns.
Confirm the time window upfront — always ask before running range queries. Large intervals are expensive; prefer multiple short-interval queries over one long one.
Clarify past vs. recent — for new investigations, ask whether the user wants a past event (specific timestamp) or a recent trend. If recent, offer concrete choices: last hour, last day, last week, last month.
Aggregate in Prometheus — never pull raw series to aggregate in Python or shell. Push sum by(...), avg by(...), or topk() into the PromQL expression — Prometheus collapses series server-side.
Timeout = query too broad — if a query takes >15s, reduce scope: add label filters, shorten --start, or add an aggregation wrapper. Apply the same narrowed scope to all subsequent queries in the session.
Data gaps → check up — when a metric shows missing data, run up{job="...", instance="..."} before diagnosing the application. A 0 value confirms the exporter was down. See references/debugging.md.

This skill is not exhaustive. Please refer to the official promql-cli documentation and examples for up-to-date information. Context7 can help as a discoverability platform.

If you encounter a bug or unexpected behavior in promql-cli itself, open an issue at https://github.com/nalbury/promql-cli/issues.

查看来源 ↗

文件元数据

name: promql-cli
description: CLI for querying Prometheus and PromQL-compatible engines (Thanos, Cortex, VictoriaMetrics, Grafana Mimir, Grafana Tempo...) — instant queries, range queries, metric discovery (metrics/labels/meta subcommands), output formats (table/csv/json/graph). Apply when executing PromQL queries, troubleshooting performance issues on a software having observability, investigating latency/error rates/saturation, or analyzing time series data.
license: MIT
compatibility: Requires promql-cli and jq
user-invocable: true
metadata:
  author: samber
  version: "1.2.0"
  openclaw:
    emoji: "📊"
    homepage: https://github.com/samber/cc-skills
    install:
      - kind: go
        package: github.com/nalbury/promql-cli
        bins: [promql]
      - kind: brew
        formula: jq
        bins: [jq]
    requires:
      bins:
        - promql
        - jq
    skill-library-version: "0.3.0"
allowed-tools: Read Edit Write Glob Grep Agent Bash(promql:*) mcp__context7__resolve-library-id mcp__context7__query-docs AskUserQuestion

查看原始文本

---
name: promql-cli
description: CLI for querying Prometheus and PromQL-compatible engines (Thanos, Cortex, VictoriaMetrics, Grafana Mimir, Grafana Tempo...) — instant queries, range queries, metric discovery (metrics/labels/meta subcommands), output formats (table/csv/json/graph). Apply when executing PromQL queries, troubleshooting performance issues on a software having observability, investigating latency/error rates/saturation, or analyzing time series data.
license: MIT
compatibility: Requires promql-cli and jq
user-invocable: true
metadata:
  author: samber
  version: "1.2.0"
  openclaw:
    emoji: "📊"
    homepage: https://github.com/samber/cc-skills
    install:
      - kind: go
        package: github.com/nalbury/promql-cli
        bins: [promql]
      - kind: brew
        formula: jq
        bins: [jq]
    requires:
      bins:
        - promql
        - jq
    skill-library-version: "0.3.0"
allowed-tools: Read Edit Write Glob Grep Agent Bash(promql:*) mcp__context7__resolve-library-id mcp__context7__query-docs AskUserQuestion
---

# promql-cli — Prometheus Query CLI Skill

`promql-cli` (github.com/nalbury/promql-cli) is a Go CLI for querying, analyzing, and visualizing Prometheus metrics, plus PromQL fundamentals.

## Reference Files

Read the relevant reference file(s) before executing tasks:

| File | When to read |
| --- | --- |
| `references/installation.md` | User needs to install promql-cli or set up configuration (hosts, auth, token, password, multi-host) |
| `references/usage.md` | User wants to discover metrics/exporters/labels, run queries, or choose output formats |
| `references/graphing.md` | User wants to visualize Prometheus data as an ASCII chart in the terminal |
| `references/debugging.md` | User is investigating a performance issue, latency, errors, saturation, data gaps, or query cost issues |
| `references/promql-reference.md` | User needs help writing PromQL, understanding metric types, functions, or aggregations |

For most tasks, read `references/usage.md`. For PromQL help, read `references/promql-reference.md`. When debugging, read both `references/debugging.md` and `references/promql-reference.md`.

## Setup Check

Before running any query, verify that a host is configured:

```bash
promql 'up'   # succeeds if host is reachable; fails with connection error if not configured
# or
promql --host xxx 'up'
```

Recognize these errors as a configuration/auth problem and refer to `references/installation.md`:

| Error | Cause |
| --- | --- |
| `dial tcp ... connection refused` | No host running at the configured address |
| `dial tcp ... no such host` | Hostname not resolved — wrong host in config |
| `error querying prometheus: ...401...` | Bearer token missing or invalid |
| `error querying prometheus: ...403...` | Token valid but insufficient permissions |
| `please specify an authentication type` | Auth flags partially set — use config file instead |

If any of these appear, **do not create config files on behalf of the user** — config files may contain credentials (tokens, passwords) that must never pass through an LLM. Instead, guide the user to set it up themselves:

> "Please create `~/.promql-cli.yaml` manually with your Prometheus host (and credentials if needed). See `references/installation.md` for the exact format. Let me know once it's ready."

Only after the user confirms the config is in place should you proceed with queries.

## Quick Command Reference

```bash
promql 'up'                                          # instant query
promql 'rate(http_requests_total[5m])' --start 1h    # range query (ASCII graph)
promql 'up' --output csv                             # CSV output
promql 'up' --output json                            # JSON output
promql metrics                                       # list all metric names
promql labels <metric>                               # list labels for a metric
promql meta <metric>                                 # show metric type and help
promql --config ~/.promql-cli-prod.yaml 'up'         # target a specific host
```

## Key Principles

1. **Use `rate()` on counters, never raw values** — raw counters only ever increase; the absolute value is meaningless. `rate()` gives the per-second change rate, which is what you actually care about.
2. **When debugging, isolate a single instance** — aggregating across replicas masks per-instance anomalies. A single overloaded pod hidden behind healthy peers won't show up in averages.
3. **Filter early with label matchers in the innermost selector** — Prometheus evaluates selectors before functions, so filtering late means scanning all time series. Early filters reduce data scanned and query latency.
4. **For histograms, keep `le` in the `by` clause** before `histogram_quantile()` — the function needs all `le` buckets to interpolate percentiles; dropping `le` early produces `NaN` or wrong results.
5. **Prefer `--output graph` for range queries** — ASCII sparklines convey trend direction (rising, falling, spiking) in a compact format that LLMs parse well; raw timestamp tables require mental modeling. Never send thousands of raw JSON/CSV rows into the LLM context — use `--output graph` instead, or run `--output graph` first and `--output table` only to inspect a narrow window.
6. **Store credentials in `~/.promql-cli.yaml` and `~/.promql_token`, chmod 600** — passing tokens as CLI args exposes them in shell history and process listings.

## Query Cost Rules

Always apply these before and during any query session:

0. **Always use the promql CLI** — never call the Prometheus HTTP API from Python scripts or shell `curl`. The CLI handles auth, formatting, and output consistently; Python API calls bypass all of that and produce raw JSON that must be parsed, inflating context and masking the graph output that models interpret best.
1. **Check cardinality first** — before querying an unfamiliar metric, count its time series (`count(metric_name)`). High-cardinality metrics without label filters time out or flood the output. See `references/debugging.md` for patterns.
2. **Confirm the time window upfront** — always ask before running range queries. Large intervals are expensive; prefer multiple short-interval queries over one long one.
3. **Clarify past vs. recent** — for new investigations, ask whether the user wants a past event (specific timestamp) or a recent trend. If recent, offer concrete choices: last hour, last day, last week, last month.
4. **Aggregate in Prometheus** — never pull raw series to aggregate in Python or shell. Push `sum by(...)`, `avg by(...)`, or `topk()` into the PromQL expression — Prometheus collapses series server-side.
5. **Timeout = query too broad** — if a query takes >15s, reduce scope: add label filters, shorten `--start`, or add an aggregation wrapper. Apply the same narrowed scope to all subsequent queries in the session.
6. **Data gaps → check `up`** — when a metric shows missing data, run `up{job="...", instance="..."}` before diagnosing the application. A `0` value confirms the exporter was down. See `references/debugging.md`.

This skill is not exhaustive. Please refer to the [official promql-cli documentation](https://github.com/nalbury/promql-cli) and examples for up-to-date information. Context7 can help as a discoverability platform.

If you encounter a bug or unexpected behavior in promql-cli itself, open an issue at [https://github.com/nalbury/promql-cli/issues](https://github.com/nalbury/promql-cli/issues).

查看并核实来源

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运行 Skill: 尚未确认运行要求，请查看来源中的 Agent、API 和服务费用。
许可证: MIT
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已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
No Windows support mentioned in installation instructions (only macOS/Linux).
The skill description mentions 'promql-cli' but the actual binary is 'promql' — minor naming inconsistency.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 203 stars, 15 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: samber/cc-skills
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月5日
目录更新于: 2026年9月6日
技能指令路径: skills/promql-cli/SKILL.md @ aece46382ba7

版本来自目录元数据，使用前请核实来源发布记录。

质量

67/100

有潜力

信任

57/100

Do not auto-install

审计

73/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
No Windows support mentioned in installation instructions (only macOS/Linux).
The skill description mentions 'promql-cli' but the actual binary is 'promql' — minor naming inconsistency.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 203 stars, 15 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

更多详情

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      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 203 stars, 15 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 73,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "No Windows support mentioned in installation instructions (only macOS/Linux).",
      "The skill description mentions 'promql-cli' but the actual binary is 'promql' — minor naming inconsistency.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 203 stars, 15 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 67,
    "label": "Promising"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Data analysis",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "No Windows support mentioned in installation instructions (only macOS/Linux).",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The skill description mentions 'promql-cli' but the actual binary is 'promql' — minor naming inconsistency.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use promql-cli in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 73/100 Needs review",
      "Safety: 29/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "samber-promql-cli (promql-cli)",
      "install_command": "npx skills add samber/cc-skills --skill promql-cli",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "samber-promql-cli",
      "task": "Use promql-cli in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/samber-promql-cli",
    "api": "https://www.openagentskill.com/api/agent/skills/samber-promql-cli",
    "audit": "https://www.openagentskill.com/skills/samber-promql-cli/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=samber-promql-cli&task=Use%20promql-cli%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20promql-cli%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20promql-cli%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/samber-promql-cli/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/samber-promql-cli"
  }
}

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创作者: samber
来源: samber/cc-skills
收录方: OpenAgentSkill 社区索引

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