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Speq Implement

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marconae
speq-implement

Implements a reviewed plan: task breakdown, TDD sub-agents, code review, and a verification report. Use to build or execute a plan under specs/_plans/, after /speq-plan and before /speq-record. Arg: plan-name.

Overview

Publishermarconae
Repositoryspeq-skill
Skill namespeq-implement
Stars
54
Forks
10
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by marconae on GitHub. Read the source before you install it.

Installation

Install the Speq Implement AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/marconae/speq-skill/tree/main/.claude/skills/speq-implement
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/marconae/speq-skill.git /tmp/speq-skill
mkdir -p .claude/skills
cp -r /tmp/speq-skill/.claude/skills/speq-implement .claude/skills/speq-implement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speq Implement in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Speq Implement on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Speq Implement is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Spec Implementer (Orchestrator)

Orchestrate implementation of the plan in specs/_plans/<plan-name>. Get the plan name from the user prompt. If none is given, ask.

Sub-agents do the heavy work. Each pins its own model and effort in its frontmatter:

Sub-agentWhen used
implementer-agentGroups with no [expert] task (default)
implementer-expert-agentGroups with at least one [expert] task
code-reviewerFinal review of all changed files

Required Skills

Invoke before starting:

  • /speq-cli: spec discovery
  • /speq-writing-guardrails: prose style for artifacts and GitHub text

Do not invoke coding skills (/speq-code-tools, /speq-ext-library, /speq-code-guardrails) yourself: the orchestrator never writes code. Sub-agents invoke their own required skills.

Orchestrator Role

  • Create and maintain tasks.md for persistence.
  • Spawn sub-agents for parallel task groups.
  • Update task status after each sub-agent completes.
  • Never implement directly. Delegate all coding work.
  • Rotate sub-agents to keep context windows fresh.
  • Update tasks.md before the runtime task, so the persistent state is never behind.
  • If a sub-agent fails, keep its tasks [~] and report the error. Never mark [x] while a test fails.

Rotation rule: when a sub-agent has completed max_tasks_per_agent (default 5) tasks, or returns ROTATION NEEDED: read tasks.md for current state, note the completed tasks from the return, and spawn a fresh agent of the SAME type with the remaining tasks. Repeat until the group is complete.

Rotation hand-off: the outgoing agent writes a hand-off note to specs/_plans/<plan-name>/notes/<group-letter>.md (its own duty, per its Early Termination section). <group-letter> is the group's letter only — the token before the : in its Parallelization-table Group entry (group A: plan-log validation → notes/A.md), never the full group name and never a slug of it. Add one line to the fresh agent's brief: Orientation: read specs/_plans/<plan-name>/notes/<group-letter>.md first. If the note is absent, omit the line. The note is working state inside the plan directory. It is never committed evidence, and it moves into the archive with the plan directory.

Workflow

Phase 0: Load Project Hook (orchestrator)

Check for .speq/implement-hook.md in the repo root.

  • Present: read it. Announce "Loaded project hook: .speq/implement-hook.md". Its content is authoritative: it can add to, change, or override any part of this workflow. If the hook conflicts with this workflow, the hook wins.
  • Absent: continue, no mention.

Add a Project Hook: line (path only, not content) to every sub-agent brief below. implementer-agent, implementer-expert-agent, and code-reviewer read the hook themselves from that path.

Phase 1: Load Plan

Read: specs/_plans/<plan-name>/plan.md
Read: specs/_plans/<plan-name>/decision-log.md

Extract: feature specs, implementation tasks, parallelization groups, verification commands from plan.md, and the ## Design Decisions entries from decision-log.md.

Phase 2: Create Tasks

Decompose the plan into a Work Breakdown Structure in specs/_plans/<plan-name>/tasks.md.

If tasks.md already exists, resume per Context Recovery instead of creating it again.

Format:

markdown
# Tasks: <plan-name>

## Phase 2: Implementation (Group A)
- [ ] 2.1 <task from plan>
- [ ] 2.2 <task from plan> [expert]

## Phase 2: Implementation (Group B)
- [ ] 2.3 <task from plan>

## Phase 3: Verification
- [ ] 3.1 Run test suite
- [ ] 3.2 Run linter

Status markers:

  • [ ] pending
  • [~] started
  • [x] completed

Difficulty tags:

  • [expert]: tagged by planner-agent during planning. Routes the task's whole group to implementer-expert-agent (Phase 3 routing rule). Preserve the tag through every status transition.
  • untagged: a group with only untagged tasks routes to implementer-agent.

If the plan tagged no tasks and a task clearly needs expert reasoning (for example concurrency, a cross-file refactor, a novel algorithm), you MAY add [expert] when materializing tasks.md. Tag sparingly: over-tagging wastes tokens.

Also create runtime tasks:

For each task in tasks.md:
  TaskCreate(subject, description, activeForm)

Phase 3: Implement (Orchestrated)

For each parallel group in plan's ## Parallelization:

  1. Route the whole group by its hardest task. Any [expert] task routes the whole group to implementer-expert-agent. Otherwise the whole group goes to implementer-agent. One agent per group. Never split a group by tag: a group is one knowledge cluster, and each extra agent rebuilds the same mental model, which costs more than the model-price difference.
  2. Mark started: update tasks.md, [ ] → [~].
  3. Spawn one sub-agent for the group with the invocation template below. If the plan's Parallelization table has a Knowledge column, copy the group's entry into the brief's Knowledge: line. If decision-log.md's ## Design Decisions has entries that touch this group's tasks, paste their Decision and Rationale verbatim into the brief's ## Rationale section. The orchestrator already read the plan and its decision log once in Phase 1, so the sub-agent does not reread them for this. Omit ## Rationale entirely when no entry touches this group.
  4. Await completion: the sub-agent returns results or a rotation signal.
  5. Handle rotation: apply the Rotation rule and Rotation hand-off above.
  6. Mark completed: update tasks.md, [~] → [x] (preserve the [expert] tag).
  7. Update TaskTools: TaskUpdate(taskId, status: "completed").
  8. Next group: proceed once its dependencies are complete.

Subagent invocation (implementer-agent, or implementer-expert-agent for a group with an [expert] task):

Delegate to <implementer-agent | implementer-expert-agent>: Implement <group-name>

## Your Tasks (the whole group)

{group_task_list}

## Rationale

<verbatim Decision and Rationale of the decision-log.md ## Design Decisions entries that touch this group's tasks; omit this section if none do>

## Context

- Plan: specs/_plans/{plan_name}/plan.md
- Tasks file: specs/_plans/{plan_name}/tasks.md
- Knowledge: <the group's Knowledge entry: read these spec deltas and files first; omit if the plan has no Knowledge column>
- Orientation: read specs/_plans/{plan_name}/notes/<group-letter>.md first <rotation respawns only>
- Update tasks.md after each task completion (preserve task numbering)
- The untagged tasks in the list are yours too: the group routes as one unit <expert groups only>
- Preserve the [expert] tag when updating status markers <expert groups only>
- Report the invariants the code now enforces <expert groups only>
- Project Hook: <if active, ".speq/implement-hook.md: read it and apply it"; otherwise omit this line>

Phase 4: Code Review

Review all changed files after implementation completes. Code review runs ONCE per implementation: after fix tasks complete, proceed to Phase 5 (its checks verify the fixes). Do not respawn code-reviewer for a second round.

  1. Collect changed files: git diff --name-only <base> for tracked changes plus git ls-files --others --exclude-standard for new files. Implementation work is uncommitted at this point, so diff against the working tree. A commit-range diff (<base>...HEAD) would miss all of it.
  2. Spawn code-reviewer agent:
    Delegate to code-reviewer — Review implementation quality
    
    ## Changed Files
    
    {changed_files_list}
    
    ## Context
    
    - Plan: specs/_plans/{plan_name}/plan.md
    - Review for: guardrail violations, dead code, test quality, bad comments, optimizations, YAGNI/over-engineering, error handling, design depth
    - Write findings to specs/_plans/{plan_name}/review-findings.md per your output format, partitioned into `## Standard fixes` and `## Expert fixes`; return only the one-line verdict.
    - Project Hook: <if active, ".speq/implement-hook.md — read it and apply it"; otherwise omit this line>
    It returns one line: CODE REVIEW: <n> findings — standard: <n>, expert: <n> — <path>. You never see the findings themselves.
  3. Process findings: branch on the two counts in the verdict. If both are 0, go to Phase 5. One agent applies the whole fix pass, routed by its hardest finding: the findings cluster on the files just written, and a second agent there re-orients into the same code and can collide with the first.
    • standard > 0, expert == 0: spawn implementer-agent:
      Delegate to implementer-agent — Apply standard review fixes
      
      ## Your Assignment (fix-task mode)
      
      Read specs/_plans/{plan_name}/review-findings.md, section `## Standard fixes`.
      Append one fix task per finding to specs/_plans/{plan_name}/tasks.md under a
      `## Phase 4: Review Fixes` group, deriving each task line from the finding's
      `Fix:` field, then execute them.
      
      ## Context
      
      - Plan: specs/_plans/{plan_name}/plan.md
      - Project Hook: <if active, ".speq/implement-hook.md — read it and apply it"; otherwise omit this line>
    • expert > 0: spawn implementer-expert-agent with the same brief shape, naming section ## Expert fixes, and also ## Standard fixes when standard > 0. It tags only the tasks derived from ## Expert fixes with [expert].
  4. Proceed to Phase 5.

Phase 5: Verification

5a. Automated Checks

Execute commands from plan's ## Verification > Checklist. Redirect each command's output to a log: mkdir -p target && <command> > target/speq-<suite>.log 2>&1, then branch on the exit code. If a report quotes output, quote at most tail -n 30 of the log. Raw build or test output never lands verbatim in the transcript.

  • Build → exit 0
  • Test → 0 failures
  • Lint → 0 errors
  • Format → no changes
5b. Scenario Coverage Audit

Cross-reference plan's ## Verification > Scenario Coverage against test results:

  • Every listed scenario → corresponding test exists and passes
  • Flag any scenario without a passing test as incomplete
5c. Manual Verification

Execute each step from plan's ## Verification > Manual Testing:

  • Run documented commands against the built software
  • Capture actual output as evidence for the verification report
  • Mark pass/fail per feature

Update tasks.md verification tasks as completed.

Phase 6: Verification Report

Generate using references/verification-template.md. Structure the report BLUF (Bottom Line Up Front): verdict and summary first, evidence after. Fill the Verdict table's Code review row from Phase 4's verdict line: a later session's condensed PR comment quotes the Verdict table, and that row is how the counts survive.

Save to: specs/_plans/<plan-name>/verification-report.md

Phase 7: Completion

Print the summary per references/implementation-summary-template.md, built from the verification-report.md Phase 6 just wrote, ending:

Ready for: /speq-record <plan-name>

Context Recovery

If context is lost or compacted:

  1. Read specs/_plans/<plan-name>/tasks.md
  2. Find incomplete tasks ([ ] or [~])
  3. Resume from the first incomplete task. A [~] task means its sub-agent was interrupted: respawn that group's agent
  4. Continue the orchestration workflow

References

FileUse When
references/verification-template.mdPhase 6 report generation
references/implementation-summary-template.mdPhase 7 summary

Anti-Patterns

PatternWhy Wrong
Orchestrator invokes coding skillsThe orchestrator never writes code, and that text dilutes the most expensive session for work it never does
Splitting one group between two agents by tagThe group is one knowledge cluster; each extra agent re-derives the same mental model
Marking [x] without a sub-agent completion returnOnly verified completions are done
Skipping the verification report/speq-record gates on it
Pointing a sub-agent at decision-log.md for rationale instead of inlining the group's excerptFull-file reads repeat per sub-agent; the orchestrator already read the decision log once in Phase 1

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Speq Implement AI skill do?

Implements a reviewed plan: task breakdown, TDD sub-agents, code review, and a verification report. Use to build or execute a plan under specs/_plans/, after /speq-plan and before /speq-record. Arg: plan-name.

Why use Speq Implement on TypingMind?

Because you install it once and use it with any model. Speq Implement is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Speq Implement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marconae/speq-skill/tree/main/.claude/skills/speq-implement. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Speq Implement?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Speq Implement?

As many as you like. As long as a model supports skills, you can use Speq Implement with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Speq Implement AI skill free?

Yes. It is published on GitHub by marconae under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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