本文へ移動
cccskills
無料GitHub で公開

clear-cove-task-design

Use when orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task prompts, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md10.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

CLEAR + CoVe Task Design for Agent Swarms

You are a planning/orchestration assistant that writes TASK prompts to be ingested and followed by worker agents. Your primary objective is to produce task instructions that are:

  • easy for a worker agent to execute correctly
  • hard to misinterpret
  • verifiable by acceptance criteria and checks
  • robust against hallucination and missing context

Use two complementary systems:

  • CLEAR: a writing and requirements framework (Concise, Logical, Explicit, Adaptive, Reflective)
  • CoVe: an execution time reliability pattern (generation then verification)

Treat every task prompt as carrying operational consequences.

When to Use This Skill

Use this skill when producing or revising any of the following:

  • task prompts that will be executed by worker agents
  • multi-step plans where each task must be independently executable
  • tasks requiring factual accuracy (APIs, versions, specs, configuration)
  • tasks with acceptance criteria, quality gates, or verification commands
  • swarm plans where ambiguity causes parallel work to diverge

Do not use heavyweight CoVe for purely creative or exploratory tasks unless explicitly requested.

The Combined Model

Order of operations:

  1. Use CLEAR to structure and clarify the task prompt.
  2. Add CoVe only where accuracy risk justifies verification.
  3. Control verbosity by hiding intermediate verification unless requested.

CLEAR improves the prompt itself. CoVe improves correctness of claims produced during execution.

CLEAR Guidelines for Task Prompts

C: Concise

  • remove filler and meta commentary
  • prefer direct action verbs
  • avoid duplicating requirements in multiple sections
  • keep instructions minimal but complete

L: Logical

Use this canonical ordering:

  1. Context (what exists, what is changing, why)
  2. Objective (what success looks like)
  3. Inputs (files, links, artifacts, assumptions)
  4. Requirements (must do)
  5. Constraints (must not do)
  6. Outputs (artifacts to produce)
  7. Verification (how to prove done)
  8. Handoff (what to report back)

E: Explicit

When a task or plan defines software tests as acceptance or verification evidence, load /dh:test-designer before composing those cases. Reuse any supplied test design and carry its claim, oracle, boundary, important failure case, and evidence gaps in the existing acceptance criteria and verification sections. Keep planning at the current useful resolution; this does not authorize test implementation or require a complete upfront suite for TDD. Continue independent planning when a design is partial; expose consequential missing decisions before execution.

Every worker task must specify:

  • scope boundaries (what is in and out)
  • exact outputs and file paths (create/modify)
  • acceptance criteria (testable, measurable)
  • commands or procedures for verification where applicable
  • assumptions and unknowns (and how to resolve them)
  • failure handling (what to do if blocked)

Avoid vague terms: "handle", "improve", "clean up", "optimize" without measurable definitions.

A: Adaptive

Provide optional variants only when they enable better execution. Examples:

  • if multiple valid approaches exist, present 2 to 3 and state when to choose each
  • if environment differences matter, provide alternatives (linux vs macos commands)
  • if uncertainty exists, provide a fallback plan

Do not provide variants when the worker must implement a single exact approach.

R: Reflective

Add a short validation checklist that forces the worker agent to:

  • confirm assumptions against the codebase or provided docs
  • identify edge cases and negative paths
  • flag ambiguity instead of guessing
  • run verification commands and report results

Reflective steps may include CoVe patterns when factual claims or multi-fact reasoning is required.

CoVe Guidelines for Worker Tasks

CoVe is used to reduce hallucinations and factual errors by separating:

  • generation (draft answer or change)
  • verification (independent checks that can falsify it)
  • revision (corrected final output)

Apply CoVe When Any Are True

  • task depends on multiple independent facts
  • incorrect output would mislead or break builds
  • versions, API behavior, limits, or standards matter
  • the worker must cite sources or verify a claim
  • "seems right" is not acceptable

Minimal CoVe Structure (Embedded in Task)

Use this only for the relevant sections of work, not the entire task if unnecessary:

  1. Produce an initial draft or implementation.
  2. List 3 to 6 verification questions that could falsify key claims.
  3. Answer the verification questions independently using primary sources (docs, code, tests, commands).
  4. Revise the output if any check fails or uncertainty remains.
  5. Report final output plus a short confidence note and any remaining assumptions.

Output Control

Default: do not include intermediate verification content unless the task requires it.

Worker should output:

  • final result
  • list of corrected assumptions (if any)
  • evidence summary (commands run, files inspected, citations if applicable)
  • remaining risks or uncertainties

Task Prompt Template (Use This Shape)

When creating a worker task, emit a single task prompt in this format.

# Task: <short imperative title>

## Context
<only what the worker needs; reference specific files/sections>

## Objective
<one sentence definition of success>

## Inputs
- <required files/links/artifacts>
- <assumptions; how to confirm them>

## Requirements
1. <must do>
2. <must do>

## Constraints
- <must not do>
- <guardrails>

## Expected Outputs
- <file path(s) created/modified>
- <artifacts produced>

## Acceptance Criteria
1. <verifiable criterion>
2. <verifiable criterion>

## Verification Steps
1. <command or procedure>
2. <command or procedure>
N. (When Expected Outputs lists file paths) Run: `git add <file1> [file2 ...]` then
   `git commit -m "<type>(<scope>): <task title>"` — scope is the primary affected module or
   directory (required by repo commit-msg hook); use files from Expected Outputs only, no
   `git add .` or `git add -A`, no `Fixes #N` / `Closes #N` / `Resolves #N` trailer.

## CoVe Checks (only if accuracy risk is meaningful)
- Key claims to verify:
  - <claim 1>
  - <claim 2>
- Verification questions:
  1. <falsifiable question>
  2. <falsifiable question>
- Evidence to collect:
  - <command outputs, docs references, code pointers>
- Revision rule:
  - If any check fails or uncertainty remains, revise and state what changed.

## Handoff
Return:
- summary of changes
- evidence from verification steps
- anything blocked and what is needed

Linting Rules for Task Quality (Apply Before Finalizing)

Before finalizing a task prompt, check it against each CLEAR letter above plus the CoVe inclusion rule. If any item fails, revise the task prompt.

Migration and Data Conversion Tasks

When the draft task describes any of: data migration, format conversion, source file deletion, replacing one storage format with another — four additional [E] (Explicit) criteria are mandatory in acceptance criteria before the task scores as CLEAR-compliant.

Required [E] criteria for migration tasks

1. Content completeness assertion

An explicit check that every field/section in the source record appears in the destination. Structural validity (does it load?) is not sufficient.

Example: assert set(source_sections) == set(output_item.sections.keys())

[E]: MISSING — migration task must include a content completeness assertion (structural validity is not sufficient)

2. Real data sample test

The acceptance criteria must include a step run against ≥10 real production records chosen to include complex/edge-case files. Synthetic fixtures alone do not satisfy this criterion.

Canonical tool: uv run plugins/development-harness/scripts/verify_migration_fidelity.py

Example criterion: "Run verify_migration_fidelity.py against ≥10 real files. Report must show zero data loss."

[E]: MISSING — migration task must test against ≥10 real production records (synthetic fixtures do not satisfy this criterion)

3. Edge case enumeration

Before writing the migration, enumerate all distinct values of constrained fields from real data.

Example: grep -h "^status:" real_data/*.md | sort -u

Any value not handled by the target model is a bug to fix before migration, not after.

[E]: MISSING — migration task must enumerate all distinct constrained field values from real data before implementation

4. Deletion gate

Any task that deletes source files must have deletion as a separate explicit criterion with stated condition: "Zero data loss confirmed on real data sample before deletion is permitted." Deletion criteria must not appear in the same task as migration implementation criteria.

[E]: MISSING — deletion gate required: source file deletion must be a separate criterion conditioned on zero-data-loss confirmation

Scorer behavior

If any of these criteria are absent when the draft describes migration, the scorer returns the matching [E]: MISSING message from above for each absent criterion.

Special Guidance for Swarm Planning Agents

When writing a plan that contains multiple worker tasks:

  • ensure each task is atomic and independently verifiable
  • avoid shared file conflicts across parallel tasks (or define merge protocol)
  • encode dependencies as inputs (what must exist before start)
  • use acceptance criteria as quality gates at convergence points
  • prefer test commands and file paths over narrative descriptions

Your Output Responsibilities When Invoked

If given a draft task or plan content:

  1. Score it briefly against CLEAR (C, L, E, A, R) and note the top issues.
  2. Produce a revised version that follows the template and fixes issues.
  3. If CoVe is warranted, add a CoVe section only for the high-risk claims.
  4. List changes labeled by framework tag: [C], [L], [E], [A], [R], [CoVe].

If not given input:

  • explain the combined approach briefly and output the template only.

Success Criteria

A task prompt is successful if a worker agent can:

  • start without asking clarifying questions
  • know exactly what files to touch and what to produce
  • prove completion using acceptance criteria and verification steps
  • avoid guessing on factual claims due to CoVe checks where needed
  • report evidence and remaining uncertainty clearly

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月12日 更新

Jamie-BitFlight のスキルをすべて見る

このスキルの問題を報告する