What makes dora-metrics notable?

1K downloads. Trust evidence available

How should dora-metrics be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

dora-metrics

Computes DORA delivery-performance metrics from git and GitHub API Skill: dora-metrics Owner: athola Summary: Computes DORA delivery-performance metrics from git and GitHub API Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:17:24.564Z | user Release v1.9.19 v1.9.17 | 2026-07-30T05:37:47.638Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:54:31.159Z | user Release v1.9.16 v1.9.14 | 2026-06-30T18:03:05.433Z | user Release v1.9.14 v1.9.13 | 2026-06-27T16:21:16.094Z | user

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.9.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.9.19release · observed Aug 26, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-minister-dora-metrics

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-minister-dora-metrics/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

83,529 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: dora-metrics
description: Computes DORA delivery-performance metrics from git and GitHub API
version: 1.9.8
triggers:
- dora
- metrics
- delivery
- engineering-management
- github
- assessing deployment frequency
- lead time
- or change failure rate
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/minister", "emoji": "\ud83e\udd9e"}}
source: claude-night-market
source_plugin: minister
---

> **Night Market Skill** — ported from [claude-night-market/minister](https://github.com/athola/claude-night-market/tree/master/plugins/minister). For the full experience with agents, hooks, and commands, install the Claude Code plugin.

# DORA Metrics

## Purpose

Compute the four DORA delivery-performance metrics (Deployment
Frequency, Lead Time for Changes, Change Failure Rate, and Time to
Restore Service) from local git history and the GitHub API. Classify
each metric into Elite, High, Medium, or Low using thresholds from
DORA's State of DevOps research, and surface the single weakest
dimension as the next improvement target.

## When to Use

- Engineering management retrospectives and quarterly reviews.
- Auditing whether agentic workflows (AI-assisted PRs, automated
deploys) improve velocity and stability or quietly regress them.
- Feeding a tier signal into `minister:release-health-gates`.

## When Not to Use

- Single-team velocity tracking that needs story-point burndowns
rather than delivery-performance evidence.
- Repositories without a clear production branch or release cadence;
DORA assumes one.

## Workflow

1. Run the helper script with the desired window:

```bash
python3 -m minister.dora_metrics --window 30 --branch main
```

2. Read the output: per-metric value, tier classification, and the
bottleneck pointer.

3. For agentic-workflow audits, run the same window twice. Once
filtering to AI-authored PRs (e.g., `--failure-label ai-bug`),
once across all PRs. Compare the CFR delta. See
`modules/agentic-workflow-signals.md`.

4. Optionally pipe `--json` into the tracker so trend data persists
alongside `release-health-gates` snapshots.

5. Optionally render trend charts with kuva when reviewing multiple
windows or comparing before/after an agentic-workflow change:

```bash
# Collect weekly snapshots into a TSV, then plot all four metrics
# week<TAB>metric<TAB>value
kuva line trends.tsv --x week --y value --color-by metric \
--title "DORA trends (30-day windows)" -o dora-trends.svg

# Quick terminal preview without writing a file
kuva line trends.tsv --x week --y value --color-by metric --terminal
```

kuva reads TSV/CSV from stdin or a file path. Install once:
`cargo install kuva --features cli`. No project source changes
required. See [kuva](https://github.com/Psy-Fer/kuva) for the
full plot-type reference.

## Inputs

| Flag | Default | Meaning |
|------|---------|---------|
| `--window` | 30 |

_meta.json

{
  "ownerId": "kn7d107jg9jv602h9ytsegydq184a42s",
  "slug": "nm-minister-dora-metrics",
  "version": "1.9.19",
  "publishedAt": 1787750244564
}

modules/agentic-workflow-signals.md

# Agentic Workflow Signals from DORA

DORA metrics were designed for human-driven engineering teams, but
the same four numbers expose specific failure modes in
AI-assisted pipelines.

## What to Watch

### Change Failure Rate, AI vs human

Run the metric twice with different `--failure-label` values:

```bash
python3 -m minister.dora_metrics --window 30 --failure-label bug --json > all.json
python3 -m minister.dora_metrics --window 30 --failure-label ai-bug --json > ai.json
```

If AI-authored CFR exceeds human-authored CFR by more than five
percentage points, treat it as a signal that review is too lenient
on AI output, not that AI is unsafe in general. The right response
is usually adding a hookify rule or imbue gate at the friction
point, not banning AI assistance.

### Lead Time, before vs after agent adoption

Compute lead time for the 30 days before and after enabling an
agentic workflow. If LT improved but CFR or TRS regressed, the team
is trading stability for velocity. The bottleneck dimension surfaced
by the skill points at which trade was made.

### Time to Restore, agent-driven hotfixes

If TRS got worse after agents started shipping hotfixes, suspect
incomplete root-cause analysis. The Replit incident is a case study:
fast restore claims that turn out to be fabricated extend TRS once
the truth surfaces.

### Deployment Frequency, ceiling check

Agents can push DF arbitrarily high. Pair DF with CFR; if DF rose
and CFR rose proportionally, the agent is generating noise rather
than signal. A high-DF, high-CFR team produces churn.

## Producing a Comparison Report

Combine two windows side-by-side:

```python
from minister.dora_metrics import compute_metrics
# ... collect events for each cohort ...
human = compute_metrics(human_deploys, human_failures, window_days=30)
agent = compute_metrics(agent_deploys, agent_failures, window_days=30)
print("Human:", human.tier())
print("Agent:", agent.tier())
print("Human bottleneck:", human.bottleneck())
print("Agent bottleneck:", agent.bottleneck())
```

If the bottleneck differs across cohorts, that is the most
useful single output: it tells the engineering manager which
guardrail is missing for which population.

## Anti-Patterns

- Reporting only DF as proof of agent ROI without CFR.
- Excluding agent-authored failures from the failure label.
- Comparing against last quarter when agent adoption mid-window
  invalidates the comparison.

modules/thresholds.md

# DORA Tier Thresholds

Source: DORA's State of DevOps research. The thresholds below match
the published bands; minor adjustments per release year are common
but the band shape is stable.

## Deployment Frequency (DF)

How often code is deployed to production. Higher is better.

| Tier | Threshold |
|------|-----------|
| Elite | At least once per day |
| High | Between once per week and once per day |
| Medium | Between once per month and once per week |
| Low | Less often than once per month |

## Lead Time for Changes (LT)

Median time from commit to production. Lower is better.

| Tier | Threshold |
|------|-----------|
| Elite | Less than one day |
| High | One day to one week |
| Medium | One week to one month |
| Low | More than one month |

## Change Failure Rate (CFR)

Percentage of deployments that cause a production failure. Lower is
better.

| Tier | Threshold |
|------|-----------|
| Elite | At most 15% |
| High | 16-30% |
| Medium | 31-45% |
| Low | More than 45% |

## Time to Restore Service (TRS)

Median time to recover from a production failure. Lower is better.

| Tier | Threshold |
|------|-----------|
| Elite | Less than one hour |
| High | Less than one day |
| Medium | Less than one week |
| Low | One week or more |

## Boundary Behavior

The implementation places the boundary value in the better tier:

- DF exactly 1.0/day classifies as Elite, not High.
- LT exactly 24 hours classifies as Elite, not High.
- CFR exactly 15% classifies as Elite, not High.
- TRS exactly 1 hour classifies as High, not Elite (TRS uses strict
  `<` for Elite to keep the "less than one hour" wording honest).

Boundary tests in
`plugins/minister/tests/unit/test_dora_metrics.py` pin these
choices.

skill-card.md

## Description:

Computes DORA delivery-performance metrics from git and GitHub API.

This skill is ready for commercial/non-commercial use.

## Publisher:

[athola](https://clawhub.ai/user/athola)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, engineering managers, and release teams use this skill to generate DORA delivery-performance reports from repository history and GitHub issue or PR signals. It supports retrospectives, quarterly reviews, and audits of whether agentic workflows improve delivery speed without increasing failure or restore-time risk.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill may activate on broad engineering and metrics terms.

Mitigation: Use it only when a DORA report or delivery-performance audit is intended, and review proposed commands before running them.

Risk: The workflow reads repository history and GitHub issue or PR labels.

Mitigation: Run it only on repositories where that operational data is appropriate to inspect and share.

Risk: Optional charting asks users to install a third-party Cargo package without a pinned version.

Mitigation: Pin or review the charting tool before installation, especially in managed or production workstations.

## Reference(s):

- [Agentic Workflow Signals from DORA](modules/agentic-workflow-signals.md)
- [DORA Tier Thresholds](modules/thresholds.md)
- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-minister-dora-metrics)
- [Project homepage](https://github.com/athola/claude-night-market/tree/master/plugins/minister)
- [kuva charting reference](https://github.com/Psy-Fer/kuva)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown with inline shell commands, optional JSON payloads, and short text reports]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Reports can include per-metric numeric values, tier classifications, an overall weakest-tier signal, and a bottleneck dimension.]

## Skill Version(s):

1.9.19 (source: server release metadata; artifact frontmatter reports 1.9.8)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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