Agent skill

Subagent Development

by softspark in softspark/ai-toolkit

Executes plans via fresh subagents per task with two-stage review (spec → quality).

Apache-2.0Auto-check: notesAgent Workflows

Install Subagent Development

skills CLI
$ npx skills add softspark/ai-toolkit --skill subagent-development -a claude-code

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

GitHub CLI
$ gh skill install softspark/ai-toolkit subagent-development --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/softspark/ai-toolkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/app/skills/subagent-development .claude/skills/subagent-development && 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
subagent-development
GitHub stars
179
Token cost
~2.2k tokens
SKILL.md length
887 words
Files
4
Skills in repo
112
Repo updated
First seen
Licence
Apache-2.0

At a glance

Executes plans via fresh subagents per task with two-stage review (spec → quality).

  • Works in 3 steps: Read and Parse Plan → Execute Tasks Sequentially → Summary Report
  • Tasks that involve Subagents
  • SKILL.md covers Usage, Why This Works, Process Flow and Step 1 -- Read and Parse Plan, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Subagent Development is an agent skill from softspark/ai-toolkit. Executes plans via fresh subagents per task with two-stage review (spec → quality). Triggers: subagent execution, execute plan, fresh agent per task, spec compliance review.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `reference/code-quality-reviewer-prompt.md`, `reference/implementer-prompt.md` and `reference/spec-reviewer-prompt.md`).

It sits in Agent Workflows, covering Subagents, Planning and Regulatory compliance. The repository describes itself as: Professional-grade AI coding toolkit: 94 skills, 44 agents, multi-platform (Claude, Cursor, Windsurf, Copilot, Gemini, Cline, Roo Code, Aider, Augment, Antigravity, Codex CLI… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Subagents
  • Tasks that involve Planning
  • Tasks that involve Regulatory compliance

Example prompts

  • “Use the subagent-development skill to execute plans via fresh subagents per task with two-stage review (spec → quality)”
  • “/subagent-development”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash, Agent, TaskCreate, TaskList, TaskUpdate, TaskGet

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Read and Parse Plan
  2. Execute Tasks Sequentially
  3. Summary Report

What it can do on your machine

Read from SKILL.md and the folder at commit d64db2b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash
    • Agent
    • TaskCreate
    • TaskList
    • TaskUpdate

    …and 1 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

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

  • Network

    No URLs in SKILL.md.

    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

Subagent Development loads about 2.2k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 887 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Grep, Glob, Bash, Agent, TaskCreate, TaskList, TaskUpdate, TaskGet

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from softspark/ai-toolkit at commit d64db2b, republished under its Apache-2.0 licence (© softspark). 887 words, ~2,201 tokens.

Download SKILL.mdSave it as .claude/skills/subagent-development/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
subagent-development
description
Executes plans via fresh subagents per task with two-stage review (spec → quality). Triggers: subagent execution, execute plan, fresh agent per task, spec compliance review.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash, Agent, TaskCreate, TaskList, TaskUpdate, TaskGet
user-invocable
true
effort
high
argument-hint
[plan file or task description]

Subagent Development

$ARGUMENTS

Execute implementation plans by dispatching fresh subagents per task, then running a two-stage review gate: spec compliance first, code quality second. Fresh context per subagent prevents accumulated confusion. Two-stage review catches different failure modes: spec review catches wrong behavior, quality review catches bad structure.

Usage

/subagent-development [plan file or task description]

Why This Works

PropertyBenefit
Fresh subagent per taskNo accumulated context drift or confusion
Spec review firstCatches wrong behavior before quality review wastes time on wrong code
Quality review secondCatches structural issues after behavior is confirmed correct
Sequential tasksNo merge conflicts, each task builds on verified previous work

Process Flow

Read plan
    |
    v
Extract ordered task list
    |
    v
For each task:
    |
    +---> [1] Dispatch IMPLEMENTER subagent
    |         |
    |         v
    |     Handle status (see Status Protocol)
    |         |
    |         v
    +---> [2] Dispatch SPEC REVIEWER subagent
    |         |
    |         v
    |     APPROVED? --no--> fix issues, re-review
    |         |
    |        yes
    |         |
    |         v
    +---> [3] Dispatch QUALITY REVIEWER subagent
    |         |
    |         v
    |     Critical issues? --yes--> fix, re-review
    |         |
    |         no
    |         v
    |     Mark task complete
    |
    v
Next task (or done)

Step 1 -- Read and Parse Plan

Read the plan file. Extract:

  1. Ordered task list -- each task with description, acceptance criteria, file scope
  2. Global constraints -- what must NOT change, architecture rules, shared conventions
  3. Dependencies -- which tasks depend on which (execute in dependency order)

Present the task list to the user. Wait for approval before proceeding.

Step 2 -- Execute Tasks Sequentially

<!-- CLAUDE_CODE_ONLY_START -->

Only in Claude Code, apply the model-routing-patterns skill when choosing executors or creating agent definitions. Delegate to codex:codex-rescue only when its plugin is installed, enabled and callable in this session. Otherwise use the installed native agents and their configured models. A context without the Agent tool returns the dispatch decision to its supervisor; it does not invent a tool or bypass the client. Preserve explicit user choices and verify actual completion before accepting a delegated result.

<!-- CLAUDE_CODE_ONLY_END -->

For each task in order:

2a. Gather Context

Before dispatching the implementer, gather:

  • Relevant source files the task will read or modify
  • Related test files
  • Any output/artifacts from previously completed tasks
  • Global constraints from the plan
2b. Dispatch Implementer

Use the Agent tool with the implementer prompt template.

Keep the agent's configured model and effort by default. If the user has approved model routing, select an available, capability-compatible route using this workload guide; do not infer permission to change tier or spending from task size:

Task TypeModelExamples
MechanicalApproved low-latency routeRename, move, config change, 1-2 files with clear spec
IntegrationApproved balanced routeWire up existing components, add endpoint using established patterns
Design/ComplexApproved higher-capability route, if evals justify itNew architecture, complex algorithms, cross-cutting concerns
2c. Handle Implementer Status

The implementer reports one of four statuses:

StatusHandling
DONEProceed to spec review
DONE_WITH_CONCERNSRead concerns. If they relate to correctness or scope violations, address them before review. If observational only (style preference, future improvement), note them and proceed to spec review
NEEDS_CONTEXTProvide the missing context the implementer identified. Re-dispatch with the same task plus the additional context
BLOCKEDAssess the blocker. Context problem: re-dispatch with better context. Capability problem: use a compatible route only within an approved model/budget policy, otherwise ask. Plan is wrong or ambiguous: escalate to user for clarification
2d. Spec Review (Stage 1)

Use the Agent tool with the spec reviewer prompt template.

The spec reviewer checks:

  • All requirements from the task are implemented
  • Nothing extra was added beyond the spec
  • Nothing is missing
  • Behavior matches acceptance criteria

If APPROVED: proceed to quality review.

If issues found: fix the issues (re-dispatch implementer with specific fix instructions or fix inline if trivial), then re-run spec review. Do not proceed to quality review until spec review passes.

Show full SKILL.md (343 more words)Show less
2e. Quality Review (Stage 2)

Use the Agent tool with the quality reviewer prompt template.

The quality reviewer categorizes findings:

CategoryAction
CriticalMust fix before proceeding. Re-dispatch implementer or fix inline
ImportantShould fix. Fix now unless time-boxed, then document for follow-up
SuggestionsNice to have. Note for future improvement, do not block progress

After fixing any Critical issues, re-run quality review to confirm.

2f. Mark Task Complete

Record:

  • Task ID and description
  • Files modified
  • Commit SHA (if commits were made)
  • Any concerns or suggestions deferred for later

Step 3 -- Summary Report

After all tasks complete, produce:

markdown
## Subagent Development Report

### Plan
[Plan file or description]

### Tasks Completed
| # | Task | Files Modified | Status | Notes |
|---|------|---------------|--------|-------|
| 1 | ... | ... | Done | ... |
| 2 | ... | ... | Done | ... |

### Deferred Items
- [Any "Important" or "Suggestion" issues not addressed]

### Verification
- [ ] All tasks implemented
- [ ] All spec reviews passed
- [ ] All quality reviews passed (no Critical issues remaining)
- [ ] Tests pass

Model Selection Guidance

Before dispatching, check the configured agent model, user choice, client availability, tool/output capabilities and budget. Keep those settings unless an override is explicitly approved. An API model ID is not an editor picker label, and a Claude alias is not a portable identifier for another provider.

For an approved routing experiment, compare representative mechanical, integration, design and review tasks at fixed acceptance criteria. Review quality depends on evidence and coverage, not simply the highest-priced model. Use model-routing-patterns and kb/reference/model-compatibility.md; do not select a universally cheapest or most capable model from a static prompt table.

Review Order Rule

Spec compliance review MUST pass BEFORE quality review starts.

Rationale: quality-reviewing code that does the wrong thing wastes everyone's time. Confirm the code does what was asked first, then confirm it does it well.

Red Flags -- STOP Immediately

If you observe any of these, stop and correct course:

  • Skipping reviews: Every task gets both reviews. No exceptions, regardless of task size
  • Proceeding with unfixed Critical issues: Critical means critical. Fix before moving on
  • Dispatching multiple implementers in parallel: Tasks are sequential. The next implementer needs to see the previous task's completed state
  • Implementer modifying files outside its scope: Re-dispatch with explicit constraints
  • Review rubber-stamping: If a reviewer approves in under 10 seconds with no specifics, the review is suspect. Re-dispatch with instructions to be thorough
  • Accumulated context: If you find yourself passing growing context between tasks, you are doing it wrong. Each subagent gets fresh context relevant to its task only

Checklist Per Task

[ ] Context gathered for this specific task
[ ] Implementer dispatched with correct model
[ ] Implementer status handled per protocol
[ ] Spec review passed (all requirements met, nothing extra, nothing missing)
[ ] Quality review passed (no Critical issues)
[ ] Task marked complete with file list and notes

© softspark, Apache-2.0. 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 3 other files in app/skills/subagent-development of softspark/ai-toolkit.

  • SKILL.md
  • reference/code-quality-reviewer-prompt.md
  • reference/implementer-prompt.md
  • reference/spec-reviewer-prompt.md

Open the folder on GitHubat commit d64db2b

Compare with similar skills

Subagent 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.

Subagent Development compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Subagent Development this skillsoftspark/ai-toolkit179—~2.2kAutomated safety check: NotesApache-2.0
Subagent Driven DevelopmentAsvarox/allkaraoke26137 repos~1.2kAutomated safety check: PassNone
Executing PlansGanyuanRan/Aegis1.3k1 repos~2.3kAutomated safety check: PassMIT
Execumputun/cc-thingz483—~8kAutomated safety check: PassMIT
Autopilot End-to-End Buildernick-vels/skills403—~2.5kAutomated safety check: NotesMIT
Workflow Orchestrationvxcozy/workflow-orchestration115—~1kAutomated safety check: PassMIT

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Categories

Questions about Subagent Development

What does Subagent Development do?

Executes plans via fresh subagents per task with two-stage review (spec → quality). Subagent Development is an agent skill from softspark/ai-toolkit. Executes plans via fresh subagents per task with two-stage review (spec → quality).

When should I use Subagent Development?

Subagent Development fits situations like: tasks that involve Subagents; tasks that involve Planning; tasks that involve Regulatory compliance.

How do I install Subagent Development in Claude Code?

Run `npx skills add softspark/ai-toolkit --skill subagent-development -a claude-code`. Or copy the skill folder (app/skills/subagent-development in softspark/ai-toolkit) into .claude/skills/subagent-development in your project. Claude Code loads it when a task matches its description.

How do I install Subagent Development in Codex?

Run `npx skills add softspark/ai-toolkit --skill subagent-development -a codex`. Or copy the skill folder (app/skills/subagent-development in softspark/ai-toolkit) into .agents/skills/subagent-development in your project. Codex loads it when a task matches its description.

Can I use Subagent 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 softspark/ai-toolkit --skill subagent-development -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/subagent-development, .gemini/skills/subagent-development, .github/skills/subagent-development and .opencode/skills/subagent-development in your project.

What does Subagent Development need to run?

SKILL.md names no scripts, command-line tools or credentials: Subagent Development is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash, Agent, TaskCreate, TaskList, TaskUpdate, TaskGet.

Does Subagent Development access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Subagent Development safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Subagent Development use?

Subagent Development is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Subagent Development use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Subagent Development?

Skills that share tags, products or a category with Subagent Development: Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Executing Plans (GanyuanRan/Aegis, 1.3k stars), Exec (umputun/cc-thingz, 483 stars) and Autopilot End-to-End Builder (nick-vels/skills, 403 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Subagent Development?

softspark (a GitHub user) maintains it in softspark/ai-toolkit, which has 179 GitHub stars. The repository holds 112 skills in this directory. The repository was last updated on October 7, 2026.

Source: softspark/ai-toolkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.