Agent skill

Spec Driven Loop

by sickn33 in sickn33/agentic-awesome-skills

Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence.

MITAuto-check passedProduct & Project Management

Install Spec Driven Loop

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill spec-driven-loop -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills spec-driven-loop --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spec-driven-loop .claude/skills/spec-driven-loop && 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-loop
GitHub stars
47k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,636 words
Files
3 (incl. references)
Skills in repo
1,394
Repo updated
First seen
Licence
MIT

At a glance

Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence.

  • Works in 10 steps: Inspect the Current System → Draft PRD.md → Grill Product Decisions → …
  • Tasks that involve Spec-driven development
  • SKILL.md covers When to Use, Quick Example, Limitations and Operating Invariants, plus 11 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Spec Driven Loop is an agent skill from sickn33/agentic-awesome-skills. Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/agent-and-judge-contracts.md` and `references/document-templates.md`).

It sits in Product & Project Management, covering Spec-driven development, PRD writing and User stories. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Spec-driven development
  • Tasks that involve PRD writing
  • Tasks that involve User stories

Example prompts

  • “/spec-driven-loop”

Workflow steps

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

  1. Inspect the Current System
  2. Draft PRD.md
  3. Grill Product Decisions
  4. Draft and Grill TECH_DESIGN.md
  5. Freeze ACCEPTANCE.md and Request Approval
  6. Create AGENT_PLAN.md
  7. Create and Maintain LOOP.md
  8. Execute Approved Work
  9. Judge Independently
  10. Deliver

What it can do on your machine

Read from SKILL.md and the folder at commit 1e53ce2. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Spec Driven Loop loads about 3.4k tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 1,636 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.7k

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 sickn33/agentic-awesome-skills at commit 1e53ce2, republished under its MIT licence (© sickn33). 1,636 words, ~3,356 tokens.

Download SKILL.mdSave it as .claude/skills/spec-driven-loop/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
spec-driven-loop
description
Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence.
category
development
risk
safe
source
self
source_repo
Linji-x/spec-driven-loop
source_type
self
license
MIT
license_source
https://github.com/Linji-x/spec-driven-loop/blob/v1.0.0/LICENSE
date_added
2026-08-25
author
Linji-x
tags
codex, spec-driven-development, multi-agent, agent-orchestration, acceptance-testing

Spec-Driven Loop

Turn an uncertain software request into an approved specification, a controlled implementation, and evidence-backed acceptance. Keep project documents in the repository's established location; otherwise use docs/spec-driven/<feature-slug>/.

When to Use

Use this skill for new products, medium-to-large features, cross-module changes, or requests that need PRD/technical design, active clarification, multi-agent execution, or a main-agent judge. Do not use it for a small single-file change, a tiny bug fix, code explanation, review-only or diagnostic work, pure research, or a simple task whose specification is already complete.

Quick Example

text
$spec-driven-loop Build a multi-tenant job dashboard with role-based access and evidence-backed acceptance.

Limitations

  • Not intended for small isolated edits, review-only work, diagnosis, or pure research.
  • Does not replace environment-specific testing or grant authorization for unrelated changes or external actions.
  • Production implementation cannot start until the user explicitly approves the frozen specification and acceptance contract.

Follow repository instructions and authorization boundaries throughout. Match generated project documents to the user's language or the repository's existing documentation language; keep identifiers such as FR-001 and AC-001 stable.

Operating Invariants

  • Facts are the agent's responsibility. Decisions belong to the user.
  • Investigate discoverable facts before asking questions. Ask only for real product decisions or consequential technical tradeoffs.
  • Never write or modify production code until the user explicitly approves the specification, scope, and acceptance contract for implementation.
  • Never disguise uncertainty. Mark it TBD, ASSUMPTION, or BLOCKED.
  • A subagent's completion report is evidence, not acceptance. The main agent owns integration and the final judgment.
  • Freeze shared interfaces, data structures, and public types before parallel work. Assign non-overlapping file ownership; serialize overlapping work.
  • Update the durable documents after every decision or implementation loop. A chat transcript is not the source of truth.
  • If implementation reveals a requirement change rather than a code defect, stop affected work, revise the specification, obtain renewed user approval, and then resume.
  • Preserve the user's authorization scope. A specification approval authorizes the approved implementation, not unrelated changes or external actions.

The requirements-grilling stage is informed by Matt Pocock's MIT-licensed grill-me / grilling decision-tree and frontier method.

Document Boundaries

Keep each fact in one authoritative document and reference its stable ID elsewhere:

  • PRD.md: why and what the product must do; owns scope, user behavior, business rules, assumptions, and product decisions.
  • TECH_DESIGN.md: how the approved product behavior will work; owns architecture, contracts, data, operations, security, and technical decisions.
  • ACCEPTANCE.md: observable proof that frozen requirements are met; owns pass/fail criteria and required evidence.
  • AGENT_PLAN.md: who performs approved implementation work; owns dependencies, file ownership, validation, and agent task contracts.
  • LOOP.md: current recoverable execution state and append-only loop history; owns attempts, evidence, judgments, rework, risks, and next action.

Read references/document-templates.md when creating or updating these five documents. Read references/agent-and-judge-contracts.md before assigning implementation tasks, integrating agent work, judging acceptance, or issuing rework.

1. Inspect the Current System

Before asking the user questions:

  1. Read applicable AGENTS.md, project instructions, existing specifications, and repository conventions.
  2. Inspect the relevant architecture, modules, interfaces, database, tests, deployment method, and code conventions.
  3. Identify established domain terms and documentation locations.
  4. Resolve facts from code, files, tools, and documentation. Record findings and sources in the draft rather than asking the user to rediscover them.
  5. Separate product choices from technical choices and note decision dependencies.

If frozen, approved PRD, Tech Design, and Acceptance documents already exist, verify their status, consistency, and applicability. Resume from planning or the current LOOP.md instead of repeating resolved grilling. If approval is absent or the request changes frozen behavior, return to the appropriate specification stage.

2. Draft PRD.md

Create the best initial PRD from the request and inspected system. Include:

  • problem and context;
  • users and stakeholders;
  • product goals and measurable success metrics;
  • user flows;
  • functional requirements with stable IDs (FR-001, FR-002, ...);
  • business rules and data lifecycle;
  • in scope, out of scope, and non-goals;
  • assumptions;
  • open product decisions;
  • decision log.

Do not turn unknowns into requirements. Label each unresolved item TBD, ASSUMPTION, or BLOCKED, and show which FRs it affects.

3. Grill Product Decisions

Represent unresolved decisions as a dependency tree. The current frontier contains only high-impact questions whose upstream decisions are resolved.

For each round:

  1. Select one to three independent frontier questions that the user can answer now.
  2. For every question, state why it matters, concrete options, the impact of each option, a recommended option, and the reason for that recommendation.
  3. Prefer a reversible explicit assumption for a low-risk issue that does not affect acceptance behavior.
  4. After the answer, immediately update PRD.md and its decision log, then recompute the frontier.
  5. Continue until no important unresolved branch remains. Do not dump a backlog of dependent questions or repeat resolved questions.

Never auto-assume core product behavior, data ownership, permission or security behavior, migrations, external compatibility, payments or money movement, destructive actions, explicit performance targets, or behavior that changes final acceptance. Keep these as blockers.

When the product frontier is clear, summarize confirmed decisions, accepted assumptions, non-goals, deferred items, and remaining risks. Ask the user to confirm that the PRD reflects the shared product understanding before treating it as frozen.

4. Draft and Grill TECH_DESIGN.md

After product behavior is understood, document:

  • current system state and overall approach;
  • module boundaries and responsibilities;
  • interface contracts;
  • data models and migrations;
  • state transitions;
  • concurrency, consistency, and idempotency;
  • authentication, authorization, privacy, and security;
  • failures, retries, recovery, and degradation;
  • performance and capacity;
  • logs, metrics, and alerts;
  • compatibility;
  • release and rollback;
  • test boundaries;
  • alternatives and technical decision log;
  • technical issues blocked by product decisions.

Mark a design item BLOCKED when it depends on an unresolved product decision. Grill consequential technical choices with the same decision-tree/frontier method: one to three answerable questions per round, options and impacts, a recommendation with rationale, immediate document updates, and no hidden high-risk assumptions. Resolve ordinary implementation facts by inspecting the system.

Show full SKILL.md (692 more words)Show less

5. Freeze ACCEPTANCE.md and Request Approval

After the PRD and Tech Design share a stable understanding, write the acceptance contract. Give each criterion a stable ID (AC-001, AC-002, ...), link it to one or more FRs, and specify:

  • scenario and preconditions;
  • action or event;
  • observable expected result;
  • required evidence;
  • whether it is release-blocking.

Cover applicable happy paths, boundary and invalid inputs, permissions, failure and recovery, repeated requests and idempotency, concurrency, compatibility, performance and capacity, migration, rollback, regression, and existing quality gates. Distinguish In Scope, Out of Scope, Non-goals, Deferred, Assumptions, Release Blockers, and Definition of Done.

Do not accept subjective criteria such as "good experience", "good performance", "high code quality", or "mostly works". Every blocking AC needs observable evidence such as automated tests, API responses, database state, logs, metrics, screenshots, performance results, or a precise manual check.

Show the user a concise specification summary and ask: The specification, scope, and acceptance conditions are defined. Do you approve implementation? Record the answer. Do not write production code without explicit approval.

6. Create AGENT_PLAN.md

Only after implementation approval, split work into independently verifiable vertical slices rather than mechanically separating frontend, backend, and tests. For each task include:

  • Task ID and objective;
  • linked FR and AC IDs;
  • inputs and dependencies;
  • exact allowed files or directories;
  • exact forbidden files or directories;
  • required code, tests, or documentation;
  • required checks and evidence;
  • stop-and-report conditions.

Before parallel delegation, freeze shared interfaces, schemas, and public types. Confirm that writes do not overlap. Serialize any tasks with overlapping ownership or unresolved dependencies. Use multiple agents only when at least two tasks are truly independent and delegation is available and authorized; do not create agents merely to display parallelism.

The main agent maintains the specification, approves ownership changes, handles dependencies and conflicts, integrates results, runs system-level verification, and judges final acceptance. Subagents may not expand scope, change acceptance criteria, unilaterally change shared contracts, cross ownership boundaries, lower test requirements, or declare the whole project complete.

7. Create and Maintain LOOP.md

Create LOOP.md before production implementation. Its top section must expose enough state for a new session to resume after reading only the top status and current loop. Use exactly one current state:

drafting, grilling, awaiting-spec-approval, ready, implementing, judging, changes-requested, blocked, or accepted.

Each loop records its Loop ID, objective, FR/AC IDs, assignments, dependencies, outputs, changed files, commands/checks, results, evidence, main-agent judgment, failure conditions, rework requirements, unresolved risks, next state, and next action. Update the top state when reality changes. Never delete or overwrite a failed loop; append the next attempt.

8. Execute Approved Work

Give each subagent only the context required by its task contract. Require the completion-report format from references/agent-and-judge-contracts.md. Treat contract deviations, new blockers, interface changes, and ownership conflicts as stop-and-report events.

Integrate in dependency order. Inspect actual changes instead of relying on summaries. Keep LOOP.md current with files, checks, results, evidence, risks, and status.

9. Judge Independently

The main agent must:

  1. Compare the actual diff and behavior with the frozen specification.
  2. Check ownership compliance and scope expansion.
  3. Check cross-module contracts and data structures.
  4. Run the highest feasible end-to-end validation plus necessary unit, integration, and regression tests.
  5. Exercise applicable failure, permission, idempotency, concurrency, migration, rollback, and recovery behavior.
  6. Produce evidence for every blocking AC.
  7. Record an acceptance matrix: Acceptance ID | Result | Evidence | Defect/Caveat.

Allowed conclusions are ACCEPTED, CHANGES_REQUESTED, BLOCKED, and ACCEPTED_WITH_CAVEATS. Missing evidence for a blocking AC is a failure. Existing code, passing unit tests, a subagent's claim, or majority agreement is never sufficient by itself; only the frozen acceptance contract determines the result.

For CHANGES_REQUESTED, append a new loop for only the failed ACs, include reproduction evidence, constrain the minimum repair scope, and require regression coverage. Never weaken acceptance to manufacture a pass. After the same AC fails judgment in three consecutive loops, stop automatic rework and ask the user to choose redesign, scope change, accepted limitation, or termination of that part.

10. Deliver

Lead with the result, then report completed scope, incomplete or deferred scope, the acceptance matrix, test and validation evidence, key design decisions, remaining risks, accepted assumptions, and suggested next steps. Ensure the final LOOP.md state matches the real outcome.

© sickn33, 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 2 other files (references) in skills/spec-driven-loop of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/agent-and-judge-contracts.md
  • references/document-templates.md

Open the folder on GitHubat commit 1e53ce2

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Spec Driven Loop 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 Loop compared with similar skills
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Spec Driven Loop this skillsickn33/agentic-awesome-skills47k1 repos~3.4kAutomated safety check: PassMIT
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CCPM Project Managementautomazeio/ccpm8.4k—~1.1kAutomated safety check: PassMIT
Project Planneradrianpuiu/claude-skills-marketplace1001 repos~6kAutomated safety check: PassNone
User Alignment and Agent-Ready PRDstryproduck/produck-skills511—~5.3kAutomated safety check: PassApache-2.0
Feature ForgeJeffallan/claude-skills12k—~1.1kAutomated safety check: PassMIT

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Questions about Spec Driven Loop

What does Spec Driven Loop do?

Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence. Spec Driven Loop is an agent skill from sickn33/agentic-awesome-skills. Freeze PRD, technical design, and acceptance criteria before medium-to-large Codex work; coordinate agents with explicit ownership, then judge delivery from diffs, tests, and evidence.

When should I use Spec Driven Loop?

Spec Driven Loop fits situations like: tasks that involve Spec-driven development; tasks that involve PRD writing; tasks that involve User stories.

How do I install Spec Driven Loop in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill spec-driven-loop -a claude-code`. Or copy the skill folder (skills/spec-driven-loop in sickn33/agentic-awesome-skills) into .claude/skills/spec-driven-loop in your project. Claude Code loads it when a task matches its description.

How do I install Spec Driven Loop in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill spec-driven-loop -a codex`. Or copy the skill folder (skills/spec-driven-loop in sickn33/agentic-awesome-skills) into .agents/skills/spec-driven-loop in your project. Codex loads it when a task matches its description.

Can I use Spec Driven Loop 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 sickn33/agentic-awesome-skills --skill spec-driven-loop -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-loop, .gemini/skills/spec-driven-loop, .github/skills/spec-driven-loop and .opencode/skills/spec-driven-loop in your project.

What does Spec Driven Loop need to run?

SKILL.md names no scripts, command-line tools or credentials: Spec Driven Loop is instructions for the agent only.

Does Spec Driven Loop 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 Spec Driven Loop 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 Loop use?

Spec Driven Loop is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Spec Driven Loop use?

About 3.4k tokens (SKILL.md is roughly 13k 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 2.4k tokens, read only when the agent opens those files.

What are the alternatives to Spec Driven Loop?

Skills that share tags, products or a category with Spec Driven Loop: Spec Workflow (hashgraph-online/awesome-codex-plugins, 1.2k stars), CCPM Project Management (automazeio/ccpm, 8.4k stars), Project Planner (adrianpuiu/claude-skills-marketplace, 100 stars) and User Alignment and Agent-Ready PRDs (tryproduck/produck-skills, 511 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spec Driven Loop?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,304 GitHub stars. The repository holds 1,394 skills in this directory. The repository was last updated on October 6, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.