Agent skill

Spec-Driven Feature Development

by tech-leads-club in tech-leads-club/agent-skills

Plans and implements a feature through four phases, specify, design, tasks and execute, with testable requirements, atomic commits and a separate verifier checking the work.

CC-BY-4.0Auto-check passedDevelopment

Install Spec-Driven Feature Development

skills CLI
$ npx skills add tech-leads-club/agent-skills --skill tlc-spec-driven -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tech-leads-club/agent-skills tlc-spec-driven --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tech-leads-club/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'packages/skills-catalog/skills/(development)/tlc-spec-driven' .claude/skills/tlc-spec-driven && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tlc-spec-driven
GitHub stars
7k
Token cost
~4.3k tokens
SKILL.md length
1,760 words
Files
18 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
CC-BY-4.0

At a glance

Plans and implements a feature through four phases, specify, design, tasks and execute, with testable requirements, atomic commits and a separate verifier checking the work.

  • Works in 5 steps: Tests derive from the spec's acceptance… → The gate must pass (tests pass) before a… → One atomic commit per task. Mark the… → …
  • Planning a new feature from a written specification through to tasks
  • SKILL.md covers Critical Rules (read before…, Auto-Sizing: The Core Principle, .specs Structure and Workflow, plus 6 more sections
  • Runs Python scripts from its folder; calls python3 and git

What it does

The skill structures feature work as Specify, Design, Tasks and Execute, auto-sizing how much depth each phase gets to the complexity of the feature. Requirements are written in EARS notation so they are testable, tasks are kept atomic, and each task ends in one atomic Conventional Commit, with traceability back to the requirement it satisfies. Reference files under the skill's own references folder cover each phase in detail, and Python scripts such as validate_spec.py, validate_tasks.py and check_commit.py enforce structural rules in code instead of relying on the agent remembering them.

Its execution contract is strict: tests must come from the spec's acceptance criteria rather than mirror the implementation, and the test runner decides whether a task is done, not the agent's own judgment. After the last task a separate Verifier, never the same agent that wrote the code, runs a spec-anchored check plus a discrimination sensor. The skill also keeps a decision log, a test-coverage matrix and a lessons file, and limits what finishing a task authorizes: local commits only, with pushing, deploying or touching a production database needing a separate explicit go-ahead.

When your agent uses it

  • Planning a new feature from a written specification through to tasks
  • Implementing a feature with atomic commits tied to individual tasks
  • Verifying a finished implementation against its original spec

Example prompts

  • “Specify the password-reset feature, then take it through design and tasks.”
  • “Implement the next task in tasks.md and commit it on its own once tests pass.”
  • “Run the verifier against the finished checkout feature and show me the traceability matrix.”

Requirements

  • python3, to run the skill's validation scripts

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Tests derive from the spec's acceptance criteria and assert spec-defined outcomes - they never mirror the implementation.
  2. The gate must pass (tests pass) before a task is done - the test runner decides, not self-assessment.
  3. One atomic commit per task. Mark the task complete in tasks.md (and update spec traceability when used) before that commit, and include…
  4. After the LAST task, a fresh Verifier always runs automatically (author ≠ verifier) - spec-anchored outcome check + discrimination sensor…
  5. Blast radius: approving a spec or tasks authorizes local implementation and local commits only. git push, force-push, deploy, production…

What it can do on your machine

Read from SKILL.md and the folder at commit 6df68d5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Spec-Driven Feature Development loads about 4.3k tokens when it runs, and up to ~36k if it reads all its reference files. Until then it costs about 245 tokens; SKILL.md has 1,760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~245
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~36k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tech-leads-club/agent-skills at commit 6df68d5, republished under its CC-BY-4.0 licence (© tech-leads-club). 1,760 words, ~4,300 tokens.

Download SKILL.mdSave it as .claude/skills/tlc-spec-driven/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.
name
tlc-spec-driven
description
Feature planning and implementation with 4 adaptive phases (Specify, Design, Tasks, Execute). Auto-sizes depth by complexity. Writes testable requirements in EARS notation, atomic tasks, atomic Conventional Commits, and requirement traceability. Ships deterministic Python validation scripts so structural gates are enforced by code, not memory. Features an independent Verifier (author != verifier, evidence-or-zero), a discrimination sensor, a decision log (STATE.md), a test-coverage matrix, and a self-improving lessons layer. Stack-agnostic and tool-agnostic. Use when (1) planning features, (2) implementing with verification and atomic commits, (3) validating an implementation against a spec. Triggers on "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "record decision", "pause work", "resume work". Do NOT use for pure architecture decomposition analysis or standalone technical design documents.
license
CC-BY-4.0
metadata.author
Felipe Rodrigues - github.com/felipfr
metadata.version
3.3.0

Tech Lead's Club - Spec-Driven Development

Plan and implement features with precision. Granular tasks. Clear dependencies. Right tools. Zero ceremony.

┌──────────┐   ┌──────────┐   ┌─────────┐   ┌─────────┐
│ SPECIFY  │ → │  DESIGN  │ → │  TASKS  │ → │ EXECUTE │
└──────────┘   └──────────┘   └─────────┘   └─────────┘
   required      optional*      optional*     required

* Agent auto-skips when scope doesn't need it

Critical Rules (read before acting)

Loading this skill's files. Reference files live under references/ in this skill's own directory (where this SKILL.md resides). Resolve them relative to the skill directory - never the workspace root - and load them through the active skill by name; never assume a fixed install path. When a step tells you to read a reference, read it completely (to EOF) before acting - never act on a partial/truncated read.

Running this skill's scripts. Every scripts/*.py shipped with this skill lives under that same skill directory. Resolve the skill directory first, then invoke python3 <skill-dir>/scripts/<name>.py .... Never run python3 scripts/... from the consuming project root - that looks for a project-local scripts/ tree that is not this skill. Project data under .specs/ is still read/written relative to the project root (pass --root when the cwd is elsewhere). Below, <skill-dir> means the directory that contains this SKILL.md.

Execution contract - every task, non-negotiable (holds even if you do not open the reference files):

  1. Tests derive from the spec's acceptance criteria and assert spec-defined outcomes - they never mirror the implementation.
  2. The gate must pass (tests pass) before a task is done - the test runner decides, not self-assessment.
  3. One atomic commit per task. Mark the task complete in tasks.md (and update spec traceability when used) before that commit, and include those updates in the same commit. Never batch tasks; never weaken, skip, or delete tests to make them pass.
  4. After the LAST task, a fresh Verifier always runs automatically (author ≠ verifier) - spec-anchored outcome check + discrimination sensor. It is never optional and never prompted. See Sub-Agent Delegation.
  5. Blast radius: approving a spec or tasks authorizes local implementation and local commits only. git push, force-push, deploy, production DB changes, and other remote / externally visible / destructive operations require an explicit go-ahead for that action.

Deterministic gates run before human review - not from memory. The structural gates for the spec and tasks are enforced by scripts in this skill's scripts/ directory, so they cannot silently drift when the model forgets a step:

  • Before confirming a spec: python3 <skill-dir>/scripts/validate_spec.py <spec-path-or-feature> (closure gate: EARS-shaped ACs, filled assumptions, well-formed requirement IDs, required sections).
  • Before presenting tasks for approval: python3 <skill-dir>/scripts/validate_tasks.py <tasks-path-or-feature> (granularity smell, diagram-vs-Depends on parity within a phase, no forward-phase dependency, every task carries Tests + Gate).
  • On each commit: python3 <skill-dir>/scripts/check_commit.py --message "<msg>" (Conventional Commits). Optionally wire it as a git commit-msg guard (git only, no agent dependency) - see implement.md.
  • Before declaring a feature done: python3 <skill-dir>/scripts/validate_state.py <feature> (completion gate: the Verifier's validation.md exists, its verdict is filled to PASS, and it cites file:line evidence - a missing, FAIL, placeholder, or evidence-free report fails). The closing step of Execute runs this automatically, the same way the lessons layer runs at distillation; it is not a manual step.

A non-zero exit means STOP and fix before proceeding. Skip a script only when no code-execution tool is available; then perform the same checks by reading the artifact.

Before Execute: read implement.md completely and run <skill-dir>/scripts/validate_tasks.py; if a formal tasks.md packs into more than one task-budgeted batch (> ~8 tasks), present the sub-agent offer first (see Sub-Agent Delegation).

Auto-Sizing: The Core Principle

The complexity determines the depth, not a fixed pipeline. Before starting any feature, assess its scope and apply only what's needed:

ScopeWhatSpecifyDesignTasksExecute
Small≤3 files, one sentenceOne-liner spec (inline)SkipSkipImplement + verify inline
MediumClear feature, <10 tasksSpec (brief)Skip - design inlineSkip - tasks implicitImplement + verify
LargeMulti-component featureFull spec + requirement IDsArchitecture + componentsFull breakdown + dependenciesImplement + verify per task
ComplexAmbiguity, new domainFull spec + discuss gray areasResearch + architectureBreakdown + phase planImplement + interactive UAT

Rules:

  • Specify and Execute are always required - you always need to know WHAT and DO it
  • Design is skipped when the change is straightforward (no architectural decisions, no new patterns)
  • Tasks is skipped when there are ≤3 obvious steps (they become implicit in Execute)
  • Discuss is triggered within Specify when the agent detects ambiguous gray areas that need user input, or when the feature has any implicit-requirement dimension present (persistence/state, external calls, auth, payments, concurrency, state transitions)
  • Interactive UAT is triggered within Execute only for user-facing features with complex behavior

Safety valve: Even when Tasks is skipped, Execute ALWAYS starts by listing atomic steps inline (see implement.md). If that listing reveals >5 steps or complex dependencies, STOP and create a formal tasks.md - the Tasks phase was wrongly skipped.

.specs Structure

.specs/
├── STATE.md            # Project memory: Decisions log (AD-NNN) + Handoff snapshot
├── LESSONS.md          # Self-improving lessons playbook (rendered by scripts/lessons.py - do not hand-edit)
├── lessons.json        # Canonical lessons state (machine-owned)
└── features/           # Feature specifications
    └── [feature]/
        ├── spec.md         # Requirements with traceable IDs
        ├── context.md      # User decisions for gray areas (only when discuss is triggered)
        ├── design.md       # Architecture & components (only for Large/Complex)
        ├── tasks.md        # Atomic tasks with verification (only for Large/Complex)
        └── validation.md   # Verifier report: PASS/FAIL, per-AC evidence, sensor result, diff range

Create artifacts lazily. Write each file only when its phase actually produces content - never scaffold empty context.md, design.md, or tasks.md up front. An empty file signals a phase happened when it did not; absence is the correct state for a skipped phase. The deterministic validators (scripts/validate_spec.py, scripts/validate_tasks.py, scripts/check_commit.py, scripts/validate_state.py) ship inside this skill's own scripts/ directory, alongside lessons.py.

Workflow

New feature:

  1. Specify → (Design) → (Tasks) → Execute (depth auto-sized)

Resume work:

  1. Read .specs/STATE.md (Handoff + Decisions).
  2. Reconcile Handoff against git (branch, status --porcelain, recent commits) and tasks.md - evidence wins over a stale snapshot. Full procedure: memory.md.
  3. Propose the reconciled next step before writing code.

Context Loading Strategy

On-demand load (only what the current task needs):

  • .specs/STATE.md - Decisions section (read at Design, re-read on resume); Handoff section (read on resume only)
  • confirmed lessons - load at Specify and Design via python3 <skill-dir>/scripts/lessons.py list --status confirmed (lessons.md); confirmed only, never candidates
  • spec.md (when working on a specific feature)
  • context.md (when designing or implementing from user decisions)
  • design.md (when implementing from design)
  • tasks.md (when executing tasks)

Never load simultaneously:

  • Multiple feature specs
  • Multiple architecture docs

Target: <40k tokens total context **Reserve:** 160k+ tokens for work, reasoning, outputs **Monitoring:** Display status when >40k (see context-limits.md)

Show full SKILL.md (801 more words)Show less

Sub-Agent Delegation

Trigger: count total tasks. If the feature packs into more than one task-budgeted batch (> ~8 tasks) → offer sub-agents; if it fits a single batch (≤ ~8 tasks) → execute inline.

Offer-then-confirm - never auto-spawn. The user must accept before any sub-agent is dispatched.

One worker per task-budgeted batch (~7 tasks, whole phases): Phases stay the semantic/dependency unit; a batch is the execution unit - one or more consecutive whole phases packed to ~7 tasks. Walk phases in order, accumulate whole phases into the current batch until it reaches the budget, then start the next - never split a phase across workers. ~20 tasks → ~3 workers; scales linearly (40 → ~6). Each worker executes all its tasks in order (implement → gate → atomic commit), then reports a compact summary (tasks done, commit hashes, test counts, deviations). Batches run sequentially - a batch never starts until the previous one reports all tasks complete. Workers never spawn further sub-agents.

Verifier (always-on, never prompted): After the final task is committed, the orchestrator dispatches a fresh Verifier sub-agent automatically - regardless of phase count. Validation never requires a user prompt; it is the closing step of Execute. Author ≠ verifier: the Verifier re-derives coverage independently using evidence-or-zero; it does not inherit the author's mental model. The Verifier: (1) performs a spec-anchored outcome check - confirms each test's asserted value matches the spec-defined expected outcome, flags spec-precision gaps; (2) runs a discrimination sensor - injects behavior-level faults in an isolated scratch (temp worktree or file copies - never git stash), confirms tests kill them, discards the scratch and verifies real-tree porcelain matches the pre-sensor baseline; surviving mutants become fix tasks; (3) writes .specs/features/[feature]/validation.md (PASS/FAIL, per-AC evidence, sensor result, diff range); (4) returns a compact verdict + ranked gap list to the orchestrator in chat. Gaps become fix tasks; the fix→re-verify loop is bounded to 3 iterations before escalating. (5) distills lessons - turns each grounded failure (surviving mutant, spec-precision gap, failed AC, SPEC_DEVIATION) into a reusable project-local lesson via <skill-dir>/scripts/lessons.py; a clean PASS records nothing (see lessons.md).

Model tier per role (only if the harness supports choosing a model per sub-agent). Match the reasoning cost to the work instead of paying top-tier reasoning for boilerplate. A batch worker on a mechanical, low-ambiguity phase (entities, config, wiring, straightforward CRUD) runs on a faster/cheaper tier; a worker on a core-domain or high-ambiguity phase, and the Design phase itself, runs on a high-reasoning tier; the Verifier runs on a mid-to-high tier because it does adversarial reasoning and designs mutations. This is a portable recommendation: if the harness cannot set a per-sub-agent model, ignore it. Full rubric in sub-agents.md.

Standalone fallback: Without sub-agents, run validate.md as an independent fresh-eyes pass after the final commit - including the spec-anchored check and discrimination sensor.

Full mechanics (worker payload, compact summary format, failure handling, context sizing, model tier, Verifier report format): sub-agents.md.

Commands

Feature-level (auto-sized):

Trigger PatternReference
Specify feature, define requirementsspecify.md
Discuss feature, capture context, how should this workdiscuss.md
Design feature, architecturedesign.md
Break into tasks, create taskstasks.md
Implement task, build, executeimplement.md
Validate, verify, test, UAT, walk me through itvalidate.md

Memory:

Trigger PatternReference
Record decision, this is a project-level decisionmemory.md
Pause work, end session, I need to stopmemory.md
Resume work, continue, pick up where we left offmemory.md
Load lessons, what have we learned, apply past lessonslessons.md
Record lesson, distill lessons (auto-runs after validation)lessons.md

Knowledge Verification Chain

When researching, designing, or making any technical decision, follow this chain in strict order. Never skip steps.

Step 1: Codebase → check existing code, conventions, and patterns already in use
Step 2: Project docs → README, docs/, inline comments, `.specs/STATE.md` (Decisions)
Step 3: Context7 MCP → resolve library ID, then query for current API/patterns
Step 4: Web search → official docs, reputable sources, community patterns
Step 5: Flag as uncertain → "I'm not certain about X - here's my reasoning, but verify"

Rules:

  • Never skip to Step 5 if Steps 1-4 are available
  • Step 5 is ALWAYS flagged as uncertain - never presented as fact
  • NEVER assume or fabricate. If you cannot find an answer, say "I don't know" or "I couldn't find documentation for this". Inventing APIs, patterns, or behaviors causes cascading failures across design → tasks → implementation. Uncertainty is always preferable to fabrication.

Output Behavior

Do the work; do not narrate the machinery. Produce the right artifact for the phase instead of announcing the phase ("I will now run the Specify phase"). The user judges the output, not a play-by-play of the process. This keeps the flow from reading as robotic.

Match effort to the work. Lightweight steps (feature-level checks, validation, mechanical tasks) do not need top-tier reasoning; heavy steps (complex design, ambiguous features) do. If the harness lets you pick a model per sub-agent, apply the tier rubric in sub-agents.md; otherwise proceed and simply invest more care on the heavy steps. Mention this once per session at most, and only if it helps; skip it for an experienced user.

Write generated artifacts in a plain, decided voice. Specs, ADRs, validation reports, commit messages, and chat summaries follow the writing rules in coding-principles.md: lead with the verdict, state decisions definitively, cut filler and mechanical hedging.

Code Analysis

Use available tools with graceful degradation. See code-analysis.md.

© tech-leads-club, CC-BY-4.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 17 other files (scripts, references) in packages/skills-catalog/skills/(development)/tlc-spec-driven of tech-leads-club/agent-skills.

  • SKILL.md
  • references/code-analysis.md
  • references/coding-principles.md
  • references/context-limits.md
  • references/design.md
  • references/discuss.md
  • references/implement.md
  • references/lessons.md
  • references/memory.md
  • references/specify.md
  • references/sub-agents.md
  • references/tasks.md
  • references/validate.md
  • scripts/check_commit.py
  • scripts/lessons.py
  • scripts/validate_spec.py
  • scripts/validate_state.py
  • scripts/validate_tasks.py

Open the folder on GitHubat commit 6df68d5

Compare with similar skills

Spec-Driven Feature Development next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Spec-Driven Feature Development compared with similar skills
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CodFlow Change Workflowbighadj22/codflow354—~1.1kAutomated safety check: PassApache-2.0
Veomni ReviewByteDance-Seed/VeOmni2.2k—~1.9kAutomated safety check: PassApache-2.0
Build a New-Project SliceKhazP/vibe-coding-prompt-template3.1k—~353Automated safety check: NotesMIT
Saleor Commit Workflowsaleor/saleor23k—~575Automated safety check: PassBSD-3-Clause

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Works with

Questions about Spec-Driven Feature Development

What does Spec-Driven Feature Development do?

Plans and implements a feature through four phases, specify, design, tasks and execute, with testable requirements, atomic commits and a separate verifier checking the work. The skill structures feature work as Specify, Design, Tasks and Execute, auto-sizing how much depth each phase gets to the complexity of the feature. Requirements are written in EARS notation so they are testable, tasks are kept atomic, and each task ends in one atomic Conventional Commit, with traceability back to the requirement it satisfies.

When should I use Spec-Driven Feature Development?

Spec-Driven Feature Development fits situations like: planning a new feature from a written specification through to tasks; implementing a feature with atomic commits tied to individual tasks; verifying a finished implementation against its original spec.

How do I install Spec-Driven Feature Development in Claude Code?

Run `npx skills add tech-leads-club/agent-skills --skill tlc-spec-driven -a claude-code`. Or copy the skill folder (packages/skills-catalog/skills/(development)/tlc-spec-driven in tech-leads-club/agent-skills) into .claude/skills/tlc-spec-driven in your project. Claude Code loads it when a task matches its description.

How do I install Spec-Driven Feature Development in Codex?

Run `npx skills add tech-leads-club/agent-skills --skill tlc-spec-driven -a codex`. Or copy the skill folder (packages/skills-catalog/skills/(development)/tlc-spec-driven in tech-leads-club/agent-skills) into .agents/skills/tlc-spec-driven in your project. Codex loads it when a task matches its description.

Can I use Spec-Driven Feature Development in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tech-leads-club/agent-skills --skill tlc-spec-driven -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tlc-spec-driven, .gemini/skills/tlc-spec-driven, .github/skills/tlc-spec-driven and .opencode/skills/tlc-spec-driven in your project.

What does Spec-Driven Feature Development need to run?

Going by SKILL.md and its folder, Spec-Driven Feature Development needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and git). Our summary lists: python3, to run the skill's validation scripts.

Does Spec-Driven Feature Development access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Spec-Driven Feature Development safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Spec-Driven Feature Development use?

Spec-Driven Feature Development is published under the CC-BY-4.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Spec-Driven Feature Development use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 31k tokens, read only when the agent opens those files.

What are the alternatives to Spec-Driven Feature Development?

Skills that share tags, products or a category with Spec-Driven Feature Development: Bulletproof Workflow (artemiimillier/bulletproof, 153 stars), CodFlow Change Workflow (bighadj22/codflow, 354 stars), Veomni Review (ByteDance-Seed/VeOmni, 2.2k stars) and Build a New-Project Slice (KhazP/vibe-coding-prompt-template, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spec-Driven Feature Development?

tech-leads-club (a GitHub organization) maintains it in tech-leads-club/agent-skills, which has 7,045 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 9, 2026.

Source: tech-leads-club/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.