Common Sdlc Metrics

Community

HoangNguyen0403

common-sdlc-metrics

Define the delivery metrics contract for agent-assisted SDLC — DORA four plus per-stage indicators derived from artifact and git history, and the control bands that turn metric drift into a routed intake. Use when reporting cycle time, plan adherence, or defining response thresholds.

Overview

Publisher	HoangNguyen0403
Repository	`agent-skills-standard`
Skill name	`common-sdlc-metrics`
Stars	573
Forks	166
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by HoangNguyen0403 on GitHub. Read the source before you install it.

Installation

Install the Common Sdlc Metrics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/common/common-sdlc-metrics
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/common/common-sdlc-metrics

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standard.git /tmp/agent-skills-standard
mkdir -p .claude/skills
cp -r /tmp/agent-skills-standard/.agents/skills/common/common-sdlc-metrics .claude/skills/common-sdlc-metrics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Common Sdlc Metrics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Common Sdlc Metrics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Common Sdlc Metrics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SDLC Metrics Standard

Priority: P2 (MEDIUM)

Measure the handoffs, not the keystrokes. Every metric here is derived from committed artifacts and git history, so it survives a change of runtime and cannot be inflated by activity.

1. Contract, Not Collector

This skill defines what to report and how. It does not gather provider or CI telemetry itself.
The host runtime or orchestrator supplies the raw counts; unavailable inputs are reported as unavailable, never estimated.
Emit artifacts/sdlc-metrics.md at a workflow terminal state, alongside artifacts/session-cost.md.
Load references/metrics-schema.md for the report skeleton.

2. Aggregate Health (DORA)

Deployment frequency: successful deploys per period, from deployment reports.
Lead time for changes: first commit to production, per change.
Change failure rate: deploys needing hotfix or rollback, divided by deploys.
Time to restore: mitigation timestamp minus incident detection, from the incident record.

3. Per-Stage Indicators

Stage	Leading	Lagging
BRD	first conversation to committed BRD	BRD survival rate into PRD
PRD / SRS	BRD to PRD to SRS commit deltas	requirement rework after first `docs/srs/srs-task-list-[slug].md`
Build	share merging on first implementation pass	plan adherence, merged diff versus `docs/srs/srs-task-list-[slug].md`
Test	first-pass CI success, eval pass rate	defect escape rate
Review	time to first finding	findings per review by severity
Release	change lead time	change failure rate
Maintain	band breach to intake in queue	repeat incidents by class

4. Control Bands

Define bands in docs/ops/bands.yaml, version controlled and reviewed like code. Schema: references/bands-template.yaml.
Baseline is a rolling window, not a fixed target. Detection is deterministic and computed before any agent reads the signal.
Tiers escalate by evidence: log, then read-only diagnose, then propose a change. Never let a tier act beyond its declared tools and routes.
Dismissals tune the band. A band that fires constantly is miscalibrated, not urgent.

5. Reporting Rules

Trend over snapshot: report direction against the previous period, not a bare number.
Name the source: every value cites the artifact, commit range, or pipeline run it came from.
No composite scores: do not blend indicators into a single productivity number.
Separate agent and human runs: attribute a change to the identity that produced it.

Anti-Patterns

No unavailable value guessed: Mark it unavailable and say why.
No lines-of-code or commit-count metrics: Measure outcomes and handoffs.
No fixed baseline: Use a rolling window per band.
No metric without a source: Cite the artifact or commit range.
No band with no owner: Name who triages a breach.

Red Flags

Stop if a metric ranks individuals: Report the flow, not the person.
Stop if a band fires every run: Recalibrate before responding.
Stop if adherence is measured against an unwritten plan: Require the committed docs/srs/srs-task-list-[slug].md.

Canonical response anchors

When this skill applies, preserve the following domain terminology or equivalent concrete examples in the answer when relevant:

artifacts/sdlc-metrics.md
DORA
plan adherence
rolling baseline
docs/ops/bands.yaml
report unavailable
artifacts/runs//

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Common Sdlc Metrics AI skill do?

Why use Common Sdlc Metrics on TypingMind?

Because you install it once and use it with any model. Common Sdlc Metrics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Common Sdlc Metrics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/common/common-sdlc-metrics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Common Sdlc Metrics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Common Sdlc Metrics?

As many as you like. As long as a model supports skills, you can use Common Sdlc Metrics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Common Sdlc Metrics AI skill free?

Yes. It is published on GitHub by HoangNguyen0403 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

SDLC Metrics Standard

Priority: P2 (MEDIUM)

1. Contract, Not Collector

2. Aggregate Health (DORA)

3. Per-Stage Indicators

4. Control Bands

5. Reporting Rules

Anti-Patterns

Red Flags

References

Canonical response anchors

Bundled files

Frequently asked questions

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