agentmods.dev

spec-driven-implementation

A workflow that plans a substantial coding task in a technical specification, gets the plan reviewed, and then implements it step by step while keeping the plan and code aligned.

In plain words
What is it for?
Use it for requests to work spec-first or follow a spec-driven process, especially for architectural changes and shared infrastructure.
Why use it?
It adds checkpoints before and during implementation when a task is large, crosses several modules, or could cause costly rework.

Skill for Claude CodeCodex

11 4mo ago A scan 69 tokens original Apache-2.0
View the source file qinghaoliqaq/AutoCoder
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/qinghaoliqaq/autocoder/spec_driven_implementation
Any agent
npx skills add qinghaoliqaq/AutoCoder --skill spec_driven_implementation
Clone the repo
git clone --depth 1 https://github.com/qinghaoliqaq/AutoCoder

Made for: Claude Code, Codex.

Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,015 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00069 $0.01015
Opus 5 $0.00034 $0.00508
Sonnet 5 $0.00014 $0.00203
Haiku 4.5 $0.00007 $0.00102

Measured 2d ago against content hash bcfe561ac57b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spec-driven-implementation scanned grade A with 0 findings 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 2d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src-tauri/src/bundled_skills/spec_driven_implementation/SKILL.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Spec-Driven Implementation

This skill is the entrypoint for the spec-driven workflow. It does not write code or specs itself — it sequences other skills and enforces the gates between them.

When To Use

Use when:

  • The task is large enough that diving straight into code would risk rework (touches 3+ modules, has architectural choices, or affects shared infrastructure)
  • The user explicitly asks for "spec-driven", "with a spec", or references the workflow by name
  • You're unsure whether to spec or just code — err on the side of spec'ing if a wrong implementation would cost more than 30 minutes to redo

Don't use for:

  • One-line fixes / typo corrections
  • Routine refactors with obvious scope
  • Exploration ("just look at how X works") — use direct tool calls

The Three Phases

Phase 1 — Spec

Invoke the write-tech-spec skill (via the Skill tool, or by following its instructions inline if you've already loaded it).

Outcome: specs/<id>/TECH.md exists with all required sections, file:line citations verified, and 80–150 lines.

Phase gate: stop and surface the spec to the user before implementing. Do this by:

  1. Telling the user where the spec was written.
  2. Highlighting the most important Risks & Open Questions.
  3. Asking via AskUserQuestion whether to proceed, revise, or abandon.

If the user requests revisions, loop back to write-tech-spec with their feedback as additional input. Do NOT silently proceed to Phase 2 without explicit go-ahead.

Phase 2 — Implement

Invoke the implement-specs skill against the now-approved spec.

Outcome: code changes committed in step-sized commits, spec updated in the same commits whenever the implementation diverges from what the spec proposed, optional PROJECT_LOG.md / DECISIONS.md maintained.

Phase 3 — Verify & Document

After the last Implementation Plan step:

  1. Run the full test scenarios in TECH.md's Testing section.
  2. Invoke the verify skill for a build / test sanity sweep.
  3. Append a final PROJECT_LOG.md entry: "spec landed, PRs: …".
  4. Confirm TECH.md's Current State now describes the new state, not the pre-change state.

Read the full file on GitHub · 113 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. 2d ago First seen · 113 lines · 69 tokens per session scan A bcfe561ac57b

Subscribe to this mod's changes

spec-driven-implementation is a skill published in the GitHub repository qinghaoliqaq/AutoCoder (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,015 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

LLMMulti-AgentPlanningagentaidesktop-appdeveloper-tools
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