dora-measurement

dora-measurement is a skill for Claude Code, Codex from paruff/uFawkesAI. It costs 64 tokens per session (3,093 once invoked), scanned A, original, MIT.

A procedure for calculating the four DORA delivery metrics from monitoring data. DORA is a research-based framework for measuring how quickly and reliably a team delivers software; Prometheus and Loki are monitoring systems that store service metrics and logs.

In plain words
What is it for?
Checking the monitoring services, querying their data, producing monthly DORA snapshots, comparing post-release trends, and generating evidence for return-on-investment analysis.
Why use it?
It turns deployment data into comparable delivery measurements and clearly marks when the available data is only a proxy.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code; installed under .agents/ (shared by several agents); mentions Codex.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is Usage: python scripts/compute_dora_metrics.py --window 30 --output metrics/.

Good fit Checking the monitoring services, querying their data, producing monthly DORA snapshots, comparing post-release trends, and generating evidence for return-on-investment analysis.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/paruff/uFawkesAI
agentmods
npx agentmods add skills/paruff/ufawkesai/dora-measurement

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dora-measurement

README.md
[![agentmods](https://agentmods.dev/badge/skills/paruff/ufawkesai/dora-measurement/github.svg)](https://agentmods.dev/skills/paruff/ufawkesai/dora-measurement)
Your own site
<a href="https://agentmods.dev/skills/paruff/ufawkesai/dora-measurement"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/dora-measurement/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dora-measurement

Your own site · 80×15
<a href="https://agentmods.dev/skills/paruff/ufawkesai/dora-measurement"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/dora-measurement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,093 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.03093
Opus 5 $0.00032 $0.01546
Sonnet 5 $0.00013 $0.00619
Haiku 4.5 $0.00006 $0.00309

Measured 11d ago against content hash 458ea64c5357, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

dora-measurement scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "${PROMETHEUS_URL}/-/healthy" | grep -q "Prometheus" || echo "ERROR: Prometheus not healthy"
.agents/skills/dora-measurement/SKILL.md · 349 lines

How it starts

The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: DORA Measurement

Load trigger: "load dora-measurement skill" > DORA: AI Capability 2 (Healthy data ecosystems) + Capability 7 (Quality internal platforms) Token cost: Medium (queries external endpoints)

Purpose

Translate uFawkesObs telemetry into the four DORA delivery metrics and a plain-language ROI signal. Bridges the gap between "substrate is running" and "we have DORA numbers."

Dependency: uFawkesObs must be running and ingesting events. If deployment event sources are not yet wired from uFawkesPipe, proxy metrics are used and flagged explicitly. Never produce metrics without flagging proxy usage — that would be misleading data.

Pre-conditions

# Verify uFawkesObs is running
curl -s "${PROMETHEUS_URL}/-/healthy" | grep -q "Prometheus" || echo "ERROR: Prometheus not healthy"
curl -s "${LOKI_URL}/ready" | grep -q "ready" || echo "ERROR: Loki not ready"
curl -s "${GRAFANA_URL}/api/health" | jq '.database' | grep -q "ok" || echo "ERROR: Grafana not healthy"

# Required environment variables
: "${PROMETHEUS_URL:?Set PROMETHEUS_URL (e.g. http://localhost:9090)}"
: "${LOKI_URL:?Set LOKI_URL (e.g. http://localhost:3100)}"
: "${GRAFANA_URL:?Set GRAFANA_URL (e.g. http://localhost:3000)}"
: "${MEASUREMENT_WINDOW_DAYS:=30}"
: "${REPO:?Set REPO (e.g. paruff/uFawkesObs)}"

The Four DORA Delivery Metrics

1. Deployment Frequency

How often code is successfully deployed to production.

Primary query (Prometheus):

# Deployments per week over the measurement window
rate(deployment_events_total{repo=~"REPO", status="success"}[${WINDOW}d]) * 604800

Proxy metric (if deployment events not yet wired — flag proxy_metrics: true):

# PR merge rate as proxy for deployment frequency
rate(github_pr_merged_total{repo=~"REPO"}[${WINDOW}d]) * 604800

DORA tier thresholds:

Tier Value
Elite On-demand (multiple deploys/day)
High 1/week to 1/day
Medium 1/month to 1/week
Low Less than 1/month

Read the full file on GitHub · 349 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 349 lines · 64 tokens per session scan A 458ea64c5357

Subscribe to this mod's changes

dora-measurement is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed 18d ago), licensed MIT. It adds 64 tokens to every session and 3,093 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

planning-with-files

Persistent file-based planning for multi-step AI-agent work. Keeps taskplan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and…

mxyhi/ok-skills · 117 tokens

infrastructure-overview

Top-level skill for the research template infrastructure layer. Use in Cursor, Claude Code, or similar agents when editing or importing anything under infrastructure/, understanding the two-layer architecture, or wiring build/validation/rendering/publishing. Covers module discovery, import patterns, thin…

docxology/template · 80 tokens

infrastructure-validation

Skill for the validation infrastructure module providing PDF validation, markdown validation, output integrity checks, link verification, documentation audits, issue categorization, and repository scanning. Use when validating research outputs, checking document quality, running audits, or verifying cross-references.

docxology/template · 54 tokens

template-academic-paper

Template-native manuscript planning, outline, drafting, revision, formatting, citation check, and AI-use disclosure routing. USE WHEN the user asks to write, outline, revise, format, or prepare a paper inside the Research Project Template.

docxology/template · 52 tokens

infrastructure-llm

Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or…

docxology/template · 66 tokens

research-workflow

Seven-stage research workflow (SCOPE→LITERATURE→REASON→DESIGN→COMPUTE→SYNTHESIZE→WRITE). Use for: structuring an AI agent's research process, generating literature review prompts, scoping methodology. Usage: from infrastructure.research import ResearchWorkflow; ResearchWorkflow.describe() Config: set stage overrides…

docxology/template · 91 tokens