Agent skill

Spec Driven Develop

by zhu1090093659 in zhu1090093659/spec_driven_develop

Automates pre-development workflow for large-scale complex tasks.

MITAuto-check passedDevelopment

Install Spec Driven Develop

skills CLI
$ npx skills add zhu1090093659/spec_driven_develop --skill spec-driven-develop -a claude-code

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

GitHub CLI
$ gh skill install zhu1090093659/spec_driven_develop spec-driven-develop --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/zhu1090093659/spec_driven_develop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/spec-driven-develop/skills/spec-driven-develop .claude/skills/spec-driven-develop && 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
spec-driven-develop
GitHub stars
984
Token cost
~5.1k tokens
SKILL.md length
2,443 words
Files
11 (incl. references)
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Automates pre-development workflow for large-scale complex tasks.

  • Works in 7 steps: Quick Intent Capture → Deep Project Analysis → Intent Refinement & Confirmation → …
  • The user mentions rewrite
  • SKILL.md covers Configuration, Before You Begin:…, Phase 0: Quick Intent Capture and Phase 1: Deep Project Analysis, plus 5 more sections
  • Calls gh

What it does

Spec Driven Develop is an agent skill from zhu1090093659/spec_driven_develop. Automates pre-development workflow for large-scale complex tasks. Use when the user mentions "rewrite", "migrate", "overhaul", "refactor entire project", "transform", "rebuild in [language]", "spec-driven", or describes any large-scale project transformation that requires planning before coding. Also triggers on Chinese keywords: "改造", "重写", "迁移", "重构", "大规模", "规范驱动". Performs full project analysis, task decomposition, documentation generation, project-level instruction and native memory surface resolution…

Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `references/adaptive-control.md`, `references/behavioral-rules.md` and `references/github-integration.md`).

It sits in Development, covering Spec-driven development, Task breakdown and Task management. It works with GitHub. The repository describes itself as: Spec-driven development workflow for AI coding agents: architecture-first planning, task decomposition, GitHub Issue/PR tracking, Deep Discuss, and adaptive control for Claude… The licence is MIT.

When your agent uses it

  • The user mentions rewrite
  • Refactor entire project
  • Rebuild in [language]
  • Describes any large-scale project transformation that requires planning before coding

Example prompts

  • “rewrite”
  • “migrate”
  • “overhaul”
  • “/spec-driven-develop”

Workflow steps

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

  1. Quick Intent Capture
  2. Deep Project Analysis
  3. Intent Refinement & Confirmation
  4. Task Decomposition
  5. Progress Tracking Documentation
  6. Confirm & Execute
  7. Archive

What it can do on your machine

Read from SKILL.md and the folder at commit 14f8c0f. 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

    Shell commands in SKILL.md call:

    • gh

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

  • Network

    No URLs in SKILL.md. Its commands use gh, 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 Develop loads about 5.1k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 178 tokens; SKILL.md has 2,443 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~178
When it runs · the whole SKILL.md, loaded when a task matches
~5.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~23k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from zhu1090093659/spec_driven_develop at commit 14f8c0f, republished under its MIT licence (© zhu1090093659). 2,443 words, ~5,145 tokens.

Download SKILL.mdSave it as .claude/skills/spec-driven-develop/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
spec-driven-develop
description
Automates pre-development workflow for large-scale complex tasks. Use when the user mentions "rewrite", "migrate", "overhaul", "refactor entire project", "transform", "rebuild in [language]", "spec-driven", or describes any large-scale project transformation that requires planning before coding. Also triggers on Chinese keywords: "改造", "重写", "迁移", "重构", "大规模", "规范驱动". Performs full project analysis, task decomposition, documentation generation, project-level instruction and native memory surface resolution, progress tracking setup, and then executes the plan within the same session. Keeps Issues as task-tracking units while batching related implementation into coherent reviewable PRs.
metadata.version
1.15.0

Spec-Driven Develop

You are executing the Spec-Driven Development workflow — a seven-phase pipeline (Phases 0-6) for large-scale complex tasks. Complete preparation phases (analysis, planning, progress setup), then execute the plan — all within a single session.

Behavioral rules: references/behavioral-rules.md — read and follow them in every phase; they are non-negotiable.

Configuration

PathDefault ValuePurpose
Analysis outputdocs/analysis/Phase 1 analysis documents
Plan outputdocs/plan/Phase 3 planning documents
Progress outputdocs/progress/Phase 4 tracking documents (incl. MASTER.md)
Instruction surfacesResolved per projectProject-level constraints for agents (see Phase 4)
Memory surfaceNative firstDurable facts via the agent's native memory when available; repo fallback only when explicitly selected
Archive outputdocs/archives/<project>/Phase 6 archived artifacts
Task tracking modeAuto-detectGITHUB_FULL, GITHUB_STANDARD, or LOCAL_ONLY
Delivery batchingPhase-firstIssues track tasks; PRs integrate coherent task batches
Adaptive controlEnabledDrift thresholds: annotate=20%, replan=40%, rescope=60% of phase tasks

Canonical references (each topic has exactly one home — cite it, never re-explain it):

ReferenceOwns
references/behavioral-rules.mdAll behavioral rules (1-19)
references/github-integration.mdTracking modes, pre-flight check, all gh commands and Issue/PR body templates
references/adaptive-control.mdTelemetry collection, drift calculation, response actions, state storage, controller activation
references/parallel-protocol.mdDispatch/review admission (tiers), lane/worktree protocol, review loop, merge risk, post-integration checks
references/super-philosophy.mdS.U.P.E.R principles + the 10-check review checklist
references/templates/Schemas for every generated document (analysis, plan, progress, governance, archive)

Task tracking modes (capabilities differ; detection and upgrade instructions live in references/github-integration.md § "Pre-flight Check"):

  • GITHUB_FULL: Issues + Milestones + Labels + Project board + worktrees + batch PRs
  • GITHUB_STANDARD: same minus the Project board
  • LOCAL_ONLY: original local-file workflow, no GitHub dependency

Before You Begin: Cross-Conversation Continuity Check

CRITICAL: Before starting any phase, inventory and read any existing project-level instruction and memory surfaces (AGENTS.md, CLAUDE.md, existing platform rule files, the active agent's native project memory, any repo-local fallback memory file already declared by the project or by an existing docs/progress/MASTER.md).

Then check if docs/progress/MASTER.md already exists:

  • If it exists: Read it immediately. You are resuming an in-progress task. Identify the tracking mode, current phase, and completed work; continue from the exact point where the previous conversation left off. Do NOT restart from Phase 0.
    • In GitHub modes: Also query GitHub for the latest task status — see references/github-integration.md § "Reading Progress from GitHub". Update MASTER.md if GitHub state is ahead of the local index.
  • If it does not exist: This is a fresh start. Proceed to Phase 0.

After loading your current state, populate the platform's native task tracking tool (e.g. TodoWrite) with the active phase's pending tasks: content = task description, status = in-progress for the active task, priority mapped P0=high, P1=medium, P2=low. If no native task tool is available, skip this step — MASTER.md alone is sufficient.


Phase 0: Quick Intent Capture

Goal: Capture the user's high-level transformation direction in 1-2 sentences — just enough to give Phase 1 analysis a focus.

Actions:

  1. Extract from the user's message: the transformation type, the rough target state, and any explicitly stated constraints.
  2. Summarize the direction back in 1-2 sentences. Do NOT ask deep clarifying questions here — Phase 1 analysis will reveal what to ask. Confirm: "I understand you want to [direction]. Let me first analyze the current project so I can ask you the right questions."
  3. If intent is completely unclear, ask ONE high-level question to determine the transformation type.

Output: A preliminary direction statement guiding Phase 1. NOT the final task definition — that comes in Phase 2.


Phase 1: Deep Project Analysis

Goal: Build a comprehensive understanding of the current codebase, informed by the Phase 0 direction.

Actions:

  1. Launch project-analyzer sub-agents in parallel, split by focus area:

    • Architecture & Stack: structure, directory layout, tech stack, entry points, build/run commands
    • Module Inventory: each module's responsibility, public API surface, size, dependencies — evaluated against all five S.U.P.E.R principles with a per-principle compliance rating
    • Risks, Tests & Governance: transformation risks, complexity hotspots, coding conventions, test coverage, instruction/memory surfaces — plus a S.U.P.E.R Architecture Health Summary with violation hotspots (priority targets for the plan)

    Give each agent the Phase 0 direction AND references/super-philosophy.md. If sub-agents are unavailable, perform the same analysis sequentially yourself.

  2. Consolidate outputs, resolve contradictions, and write docs/analysis/ documents from references/templates/analysis.md:

    • project-overview.md, module-inventory.md (with per-module S.U.P.E.R scores), risk-assessment.md (with the S.U.P.E.R health summary)
  3. GitHub Pre-flight Check: detect the tracking mode per references/github-integration.md § "Pre-flight Check". Report the detected mode; if it differs from user expectation, explain how to upgrade (e.g., gh auth refresh -s project).

Output: Complete docs/analysis/ (three documents) + detected tracking mode. The S.U.P.E.R assessment is the architectural baseline for all subsequent phases.


Phase 2: Intent Refinement & Confirmation

Goal: With the project analyzed, finalize the task definition through a grounded discussion.

Actions:

  1. Present key Phase 1 findings: brief architecture summary, notable S.U.P.E.R health issues, and coupling/complexity highlights relevant to the transformation.

  2. Ask targeted questions grounded in the analysis — specific and informed, not generic (e.g., about circular dependencies found, hardcoded environment assumptions, missing interface contracts). At minimum confirm:

    • Scope — which modules from the inventory are in scope
    • Target — target technology/architecture/state
    • Constraints — timeline, backward compatibility, libraries, deployment targets
    • Priorities — performance, maintainability, feature parity (use the risk assessment)
    • S.U.P.E.R priorities — which violations to fix now vs. defer
    • Testing policy — which test layers protect changes; whether to establish a minimal test harness if none exists
    • Project governance — canonical instruction surfaces; native memory surface or explicitly named repo fallback
  3. Summarize the refined understanding and get explicit confirmation.

Output: The authoritative, confirmed task definition guiding Phases 3-6.


Phase 3: Task Decomposition

Goal: Break the transformation into manageable, trackable tasks organized in phases, with parallel lanes and coherent delivery batches.

Actions:

  1. Launch task-architect sub-agents with the full Phase 1 analysis AND the confirmed Phase 2 definition. If multiple strategies are plausible, launch 2 agents exploring different approaches (e.g., bottom-up vs. strangler fig) and pick the better result. If sub-agents are unavailable, decompose yourself.
  2. The decomposition must produce:
    • Phases ordered by dependency; early phases prioritize fixing S.U.P.E.R violation hotspots before new features.
    • Tasks, each with: description, priority (P0/P1/P2), effort (S/M/L/XL), dependencies, S.U.P.E.R design drivers, acceptance criteria, test expectation, and memory/governance impact. Every task's acceptance criteria implicitly include passing the S.U.P.E.R Quick Check for its listed principles.
      • Testing is default: tasks changing user-visible features, behavior, API contracts, schemas, migrations, parsing, routing, permissions, caching, or persistence MUST add or update automated tests; documentation/config tasks may mark tests N/A with an explicit reason.
      • Governance is default: tasks introducing a stable rule, gotcha, or convention must include updating the resolved memory surface (and instruction surfaces if the rule affects future agents).
    • Parallel execution lanes per phase: group mutually independent tasks; assess merge risk (file overlap).
    • Delivery batches: after reviewing the complete phase task set (dependencies, file overlap, shared validation, rollout risk, rollback boundary), assign every task to exactly one batch. Default to one coherent PR batch per phase; split only for documented reviewability, release/rollback, ownership, risk-isolation, or policy reasons; a single-Issue batch needs explicit justification. Record per batch: ID, goal, task IDs, execution waves, lanes, integration branch, combined validation, dependency order, split rationale.
    • Dependency graph as a Mermaid diagram (subgraphs for batch boundaries and lanes) and milestones at phase boundaries.
  3. Write docs/plan/ documents from references/templates/plan.md: task-breakdown.md, dependency-graph.md, milestones.md.
  4. GitHub Resource Synchronization (skip in LOCAL_ONLY): create Labels → Milestones → Issues → [GITHUB_FULL only] Project board, in that order, using the exact commands and the Issue body template in references/github-integration.md. Add a 1-second delay between Issue creations. Record all URLs and the Task → Issue → Delivery Batch mapping for Phase 4. Issue creation does not imply PR creation.
  5. Initialize Adaptive Control State: for each Milestone, compute percentage-based drift thresholds and append the adaptive YAML block per references/adaptive-control.md § "Adaptive State Storage". In LOCAL_ONLY mode, the state goes to MASTER.md in Phase 4 instead.

Output: Complete docs/plan/ (three documents); in GitHub modes, all tasks exist as labeled, milestoned Issues with adaptive state initialized.


Phase 4: Progress Tracking Documentation

Goal: Create a progress tracking and governance system that survives across conversations.

Actions:

Use references/templates/progress.md for progress documents and references/templates/governance.md for governance records.

Project Governance Surface (all modes)
  1. Inventory existing surfaces: shared instruction files (AGENTS.md or equivalent), CLAUDE.md, existing platform rule files, the agent's native project memory, and repo-local fallback memory files only if they already exist or the user explicitly selects one.
  2. Update instruction surfaces without overwriting: shared cross-agent rules → AGENTS.md; Claude Code-specific → CLAUDE.md; platform rule files only when they already exist or are requested. Preserve user-written sections, local commands, and security constraints. If an existing rule conflicts with the plan, do not silently replace it — record the conflict in MASTER.md and ask the user at the next checkpoint.
  3. Resolve the memory surface: prefer native project memory; never silently create a Markdown memory file; use a repo-local fallback only on user confirmation or existing project declaration. Record the resolution in MASTER.md "Governance Status".

Do not create competing truth sources.

Show full SKILL.md (988 more words)Show less
In GITHUB_FULL or GITHUB_STANDARD mode:
  1. Create docs/progress/MASTER.md as a lightweight GitHub index: task name/description, tracking mode, repository, Project URL (GITHUB_FULL), links to analysis/plan documents, milestone table, Issue mapping table (Task → Issue → Batch → PR → status), delivery batch table, "Quick Status Commands", "Current Status", "Next Steps". Do NOT duplicate task details — those live in GitHub Issues.
  2. Add an "Execution Telemetry" reference noting where telemetry and drift state live per references/adaptive-control.md § "Adaptive State Storage" (Issue comments + Milestone descriptions).
  3. Per-phase detail files are optional and lightweight (Issue references only).
In LOCAL_ONLY mode:
  1. Create docs/progress/MASTER.md: task name/description, LOCAL_ONLY mode, links to analysis/plan documents, phase summary table, links to phase files, "Current Status", "Next Steps".
  2. Create one docs/progress/phase-N-<short-name>.md per phase: checkbox tasks with inline acceptance criteria plus a "Notes" section.
  3. Add the "Adaptive Control State" section and a "Task Telemetry Log" table to MASTER.md per references/adaptive-control.md § "Adaptive State Storage".
Common to all modes:
  • Phases use - [ ] Phase N: <name> (0/X tasks) linking to the phase file (LOCAL_ONLY) or milestone URL (GitHub modes); - [x] Phase N: <name> (X/X tasks) when done.
  • "Current Status" is updated at the start and end of each work session.

Output: Complete docs/progress/ with MASTER.md (plus phase files in LOCAL_ONLY).


Phase 5: Confirm & Execute

Goal: Present preparation artifacts, get confirmation, then execute the plan.

Actions:

5a. Summary & Confirmation
  1. Present: task definition (Phase 2), key findings (Phase 1), phased plan with task counts (Phase 3), delivery batch overview with PR count and split rationales (Phase 3), tracking mode and its implications, progress system description (Phase 4), and the execution model (tiered dispatch: orchestrator-direct by default; task-executor/code-reviewer sub-agents dispatched per references/parallel-protocol.md § "Dispatch Admission (Tiered Execution)").
  2. List all generated artifacts (analysis, plan, and progress documents; resolved instruction and memory surfaces; GitHub Project URL, Milestone URLs, Issue numbers, batch mapping in GitHub modes).
  3. Ask the user: "All preparation is complete. Ready to begin execution?"
5b. Execution
  1. Process each phase sequentially. Before editing, read every open Issue in the phase and revalidate the planned batches against current dependencies, affected files, review scope, and repository rules. If the mapping must change, update task-breakdown.md, MASTER.md, and the Delivery Batch field in every affected Issue body; comment the regrouping reason so all execution surfaces agree.

  2. Choose the execution tier for each delivery batch per the admission criteria in references/parallel-protocol.md § "Dispatch Admission (Tiered Execution)":

    • Tier 0 — orchestrator-direct (default): S/M effort, ≤ 3 files, context already held, or machine-verifiable acceptance. Execute directly on the batch integration branch. No sub-agents, no worktrees.
    • Tier 1 — single coder: L/XL bundles or context-heavy exploration. Delegate the complete batch to one task-executor.
    • Tier 2 — parallel lanes: only when ALL hold — disjoint lane file sets, ≥ L effort per lane, independent verifiability, ≤ 4 lanes. Launch one task-executor per dependency-ready lane in isolated worktrees, in waves, each with the full batch context plus its task/Issue subset. Lane agents never create PRs.
    • Branch convention: repository's own; otherwise batch/{batch_id}-{slug} (integration) and work/{batch_id}-{lane_id}-{slug} (Tier 2 lanes only).
  3. Review before integrating per references/parallel-protocol.md § "Review Admission (Tiered Review)":

    • L1 — machine validation (always): every task's targeted checks plus the batch's combined validation.
    • L2 — orchestrator diff review (default): personally read the diff against every Issue's acceptance criteria.
    • L3 — independent reviewer (reserved): one code-reviewer per lane, mandatory for Tier 2 lanes and high-risk work (contract/port formats, logic code, cross-surface semantic invariants). Verdict APPROVED | FIXED | ESCALATE; integrate only APPROVED or FIXED lanes; resolve ESCALATE yourself, with the user when needed.
    • Writer model: reviewers never write GitHub state, MASTER.md, drift state, or instruction/memory surfaces. You remain the acceptance-verification authority and single writer for all shared state.
  4. After each task completion — follow references/adaptive-control.md § "Controller Activation": collect telemetry, update cumulative drift_score, write telemetry to the Issue (GitHub modes) or MASTER.md (LOCAL_ONLY), and execute automatic threshold responses. For parallel lanes, lane agents return per-task telemetry and you record it once during batch integration.

  5. Integrate and validate each delivery batch per references/github-integration.md § "Delivery Batch Execution Workflow":

    • Consolidate reviewed lane branches onto the batch integration branch; reconcile overlaps.
    • Run per-task checks plus combined validation and post-integration architecture checks.
    • Verify every included Issue's acceptance criteria yourself (L2). Keep incomplete Issues out of closing keywords.
    • Create exactly one batch PR with one Closes #N per completed Issue. Immediately write the PR number and in review state to the batch row and every completed Issue row in MASTER.md; keep partial Issues open with explicit partial status.
    • A single-Issue PR is allowed only for a documented exception or a one-Issue phase.
  6. Progress updates:

    • GitHub modes: merged batch PRs auto-close their Issues; update MASTER.md "Current Status", "Issue Mapping", "Delivery Batches".
    • LOCAL_ONLY: check off tasks in phase files; update MASTER.md counts.
    • All modes: durable knowledge → resolved memory surface; agent-behavior changes → resolved instruction surfaces.
  7. When all tasks are complete (all Issues closed or all checkboxes checked): proceed to Phase 6.

Output: All planned tasks implemented and verified.


Phase 6: Archive

Trigger: All tasks complete — all Issues closed (GitHub modes) or all checkboxes [x] (LOCAL_ONLY).

Goal: Archive all workflow artifacts for traceability, then clean up working directories.

Actions:

  1. Announce completion to the user.
  2. Determine the archive directory name from the Phase 2 task name (lowercase, hyphens, no special characters): docs/archives/<project-name>/. Target structure and index template: references/templates/archive.md.
  3. Move docs/analysis/, docs/plan/, and docs/progress/ into the archive; copy snapshots or export references for the resolved instruction and memory surfaces into docs/archives/<project-name>/governance/; move any other temporary workflow files.
  4. [GitHub modes] Close each phase's Milestone (if not already closed); optionally close the Project board. These remain on GitHub as a permanent record.
  5. Create or update docs/archives/README.md with an entry: project name, one-line description, date range, link to archived MASTER.md, and (GitHub modes) the Project URL.
  6. Remove the now-empty docs/analysis/, docs/plan/, docs/progress/ directories. Keep active instruction and memory surfaces in place; only their snapshots live under the archive.
  7. Suggest the user commit the archive to version control.

Output: All artifacts under docs/archives/<project-name>/ with an updated docs/archives/README.md index.

© zhu1090093659, MIT. 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 10 other files (references) in plugins/spec-driven-develop/skills/spec-driven-develop of zhu1090093659/spec_driven_develop.

  • SKILL.md
  • references/adaptive-control.md
  • references/behavioral-rules.md
  • references/github-integration.md
  • references/parallel-protocol.md
  • references/super-philosophy.md
  • references/templates/analysis.md
  • references/templates/archive.md
  • references/templates/governance.md
  • references/templates/plan.md
  • references/templates/progress.md

Open the folder on GitHubat commit 14f8c0f

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Works with

Questions about Spec Driven Develop

What does Spec Driven Develop do?

Automates pre-development workflow for large-scale complex tasks. Spec Driven Develop is an agent skill from zhu1090093659/spec_driven_develop. Automates pre-development workflow for large-scale complex tasks.

When should I use Spec Driven Develop?

Spec Driven Develop fits situations like: the user mentions rewrite; refactor entire project; rebuild in [language]; describes any large-scale project transformation that requires planning before coding.

How do I install Spec Driven Develop in Claude Code?

Run `npx skills add zhu1090093659/spec_driven_develop --skill spec-driven-develop -a claude-code`. Or copy the skill folder (plugins/spec-driven-develop/skills/spec-driven-develop in zhu1090093659/spec_driven_develop) into .claude/skills/spec-driven-develop in your project. Claude Code loads it when a task matches its description.

How do I install Spec Driven Develop in Codex?

Run `npx skills add zhu1090093659/spec_driven_develop --skill spec-driven-develop -a codex`. Or copy the skill folder (plugins/spec-driven-develop/skills/spec-driven-develop in zhu1090093659/spec_driven_develop) into .agents/skills/spec-driven-develop in your project. Codex loads it when a task matches its description.

Can I use Spec Driven Develop 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 zhu1090093659/spec_driven_develop --skill spec-driven-develop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spec-driven-develop, .gemini/skills/spec-driven-develop, .github/skills/spec-driven-develop and .opencode/skills/spec-driven-develop in your project.

What does Spec Driven Develop need to run?

Going by SKILL.md and its folder, Spec Driven Develop needs the command-line tools its instructions call (gh).

Does Spec Driven Develop access the network?

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

Is Spec Driven Develop 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. Review the folder before installing.

What licence does Spec Driven Develop use?

Spec Driven Develop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Spec Driven Develop use?

About 5.1k tokens (SKILL.md is roughly 21k 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 18k tokens, read only when the agent opens those files.

What are the alternatives to Spec Driven Develop?

Skills that share tags, products or a category with Spec Driven Develop: CCPM Project Management (automazeio/ccpm, 8.4k stars), PRP Plan (Wirasm/prp, 2.3k stars), Protheus Spec-Driven Development (totvs/engpro-advpl-tlpp-skills, 143 stars) and Conductor Track Management (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spec Driven Develop?

zhu1090093659 (a GitHub user) maintains it in zhu1090093659/spec_driven_develop, which has 984 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on July 26, 2026.

Source: zhu1090093659/spec_driven_develop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.