Agent skill

Piv Plan Implementation

by coleam00 in coleam00/skills

Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research.

MITAuto-check passedAgent Workflows

Install Piv Plan Implementation

skills CLI
$ npx skills add coleam00/skills --skill piv-plan-implementation -a claude-code

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

GitHub CLI
$ gh skill install coleam00/skills piv-plan-implementation --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/coleam00/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/piv-plan-implementation .claude/skills/piv-plan-implementation && 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
piv-plan-implementation
GitHub stars
670
Token cost
~4.9k tokens
SKILL.md length
1,334 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research.

  • Works in 5 steps: Feature Understanding → Codebase Intelligence Gathering → External Research & Documentation → …
  • You have a ticket
  • SKILL.md covers Feature: $ARGUMENTS, Resolve the input first, Mission and Planning Process, plus 4 more sections
  • Calls gh

What it does

Piv Plan Implementation is an agent skill from coleam00/skills. Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research. Accepts a tracker ticket (a Jira/Linear/GitHub key or URL, fetched from the tracker) or a free-form feature request. Use when you have a ticket or feature and need a one-pass-ready plan before writing any code.

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Planning and Codebase onboarding. It works with GitHub and Jira. The repository describes itself as: The agent skills I actually use to build software with coding agents. The PIV loop, planning, worktrees, and the meta-skills for building your own AI Layer. The licence is MIT.

When your agent uses it

  • You have a ticket
  • Feature and need a one-pass-ready plan before writing any code

Example prompts

  • “Use the piv-plan-implementation skill to create a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying…”
  • “/piv-plan-implementation”

Workflow steps

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

  1. Feature Understanding
  2. Codebase Intelligence Gathering
  3. External Research & Documentation
  4. Deep Strategic Thinking
  5. Plan Structure Generation

What it can do on your machine

Read from SKILL.md and the folder at commit 847be08. 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

Piv Plan Implementation loads about 4.9k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 1,334 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~4.9k

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 coleam00/skills at commit 847be08, republished under its MIT licence (© coleam00). 1,334 words, ~4,905 tokens.

Download SKILL.mdSave it as .claude/skills/piv-plan-implementation/SKILL.md (or your agent's skills folder).
name
piv-plan-implementation
description
Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research. Accepts a tracker ticket (a Jira/Linear/GitHub key or URL, fetched from the tracker) or a free-form feature request. Use when you have a ticket or feature and need a one-pass-ready plan before writing any code.
argument-hint
[ticket key/URL (fetched from your tracker), or a free-form feature description]

Plan a new task

Feature: $ARGUMENTS

Resolve the input first

$ARGUMENTS is either a tracker ticket (a key like ACC-30, or a Jira / Linear / GitHub issue URL) or a free-form feature description. Tell them apart and handle each:

  • A ticket (a key such as ABC-123, or an issue URL): fetch it from the tracker before you plan (Jira via the Atlassian MCP, GitHub via gh issue view, etc.). Read its summary, acceptance criteria, and per-ticket context. Then follow its links up to the epic and the epic's linked architecture page (Confluence via the Atlassian MCP) and inherit those decisions (see "Inherit, don't re-decide" below). Never plan from the bare key; the ticket body plus its epic and architecture are the real input.
  • A free-form description: plan directly from it (greenfield or ad-hoc), asking clarifying questions as needed.

Mission

Transform a feature request into a comprehensive implementation plan through systematic codebase analysis, external research, and strategic planning.

Core Principle: We do NOT write code in this phase. Our goal is to create a context-rich implementation plan that enables one-pass implementation success for ai agents.

Key Philosophy: Context is King. The plan must contain ALL information needed for implementation - patterns, mandatory reading, documentation, validation commands - so the execution agent succeeds on the first attempt.

Inherit, don't re-decide: This is a per-ticket plan. If the ticket belongs to an epic that already has architecture decisions — a linked architecture page (e.g. a Confluence page from the plan-architecture skill, reached from the ticket's epic), an ## Architecture / ## Engineering section on the epic, or a local architecture.md / engineering-plan.md — read it first and treat its cross-cutting calls (stack & versions, data model, security boundaries, the seams new code plugs into) as already decided. Inherit them; don't reopen them. Plan only what's left at the ticket level: the specific files, the local patterns to mirror, the tests. If a ticket genuinely needs to break an epic-level decision, flag it in Open Questions rather than silently diverging.

Planning Process

Phase 1: Feature Understanding

Deep Feature Analysis:

  • Extract the core problem being solved
  • Identify user value and business impact
  • Determine feature type: New Capability/Enhancement/Refactor/Bug Fix
  • Assess complexity: Low/Medium/High
  • Map affected systems and components

Create User Story Format Or Refine If Story Was Provided By The User:

As a <type of user>
I want to <action/goal>
So that <benefit/value>
Phase 2: Codebase Intelligence Gathering

Use specialized agents and parallel analysis:

1. Project Structure Analysis

  • Detect primary language(s), frameworks, and runtime versions
  • Map directory structure and architectural patterns
  • Identify service/component boundaries and integration points
  • Locate configuration files (pyproject.toml, package.json, etc.)
  • Find environment setup and build processes

2. Pattern Recognition (Use specialized subagents when beneficial)

  • Search for similar implementations in codebase
  • Identify coding conventions:
    • Naming patterns (CamelCase, snake_case, kebab-case)
    • File organization and module structure
    • Error handling approaches
    • Logging patterns and standards
  • Extract common patterns for the feature's domain
  • Document anti-patterns to avoid
  • Check CLAUDE.md for project-specific rules and conventions

3. Dependency Analysis

  • Catalog external libraries relevant to feature
  • Understand how libraries are integrated (check imports, configs)
  • Find relevant documentation in docs/, ai_docs/, .claude/references or ai-wiki if available
  • Note library versions and compatibility requirements

4. Testing Patterns

  • Identify test framework and structure (pytest, jest, etc.)
  • Find similar test examples for reference
  • Understand test organization (unit vs integration)
  • Note coverage requirements and testing standards

5. Integration Points

  • Identify existing files that need updates
  • Determine new files that need creation and their locations
  • Map router/API registration patterns
  • Understand database/model patterns if applicable
  • Identify authentication/authorization patterns if relevant

Clarify Ambiguities — GATE:

Codebase analysis is done, so the open questions are now specific. This is the one moment where you know enough to ask well and have not yet written anything. GATE means: post the questions, then stop. End the turn and wait for the answers. Do not ask and answer in the same breath, and do not roll into Phase 3.

Ask in one cluster, numbered, 3-6 questions max, each carrying a recommended default so answering is cheap ("I'll mirror the first unless you say otherwise"). Draw them only from what the analysis actually left open:

  1. Scope boundary — the adjacent thing a reasonable reader would assume is in scope. Confirm it is out.
  2. Pattern fork — two existing patterns both fit. Name both with file:line and ask which to mirror.
  3. Contract shape — the API surface, payload, or data-model change the ticket implies but never states.
  4. Failure behavior — what happens on the error path the ticket is silent about.
  5. Preference — a library or trade-off with no precedent in this codebase to inherit.
  6. Done — an acceptance criterion that is missing, or written so that it cannot be checked.

Skip any category with nothing genuinely open; never manufacture questions to fill the list. If the ticket, its epic and the architecture doc genuinely settle everything, say so in one line and proceed. Silence is not the same as clearance.

Thin answers: reflect a vague answer back as the concrete choice it leaves open ("'handle errors gracefully' — a 4xx with a message, or retry then 503?") and ask once more. Never upgrade a vague answer into a confident plan.

If they decline ("just write it"): honour it, but name what you are guessing. Every unanswered item becomes an Assumed — <the assumption>, confirm before execution line in OPEN QUESTIONS / ASSUMPTIONS, and the task it affects carries a **GOTCHA** naming it. Never guess silently.

Already settled upstream: anything the ticket, its epic, or the linked architecture page already answers is not open. Inherit it and skip (see "Inherit, don't re-decide").

Show full SKILL.md (426 more words)Show less
Phase 3: External Research & Documentation

Use specialized subagents when beneficial for external research:

Documentation Gathering:

  • Research latest library versions and best practices
  • Find official documentation with specific section anchors
  • Locate implementation examples and tutorials
  • Identify common gotchas and known issues
  • Check for breaking changes and migration guides

Technology Trends:

  • Research current best practices for the technology stack
  • Find relevant blog posts, guides, or case studies
  • Identify performance optimization patterns
  • Document security considerations

Compile Research References:

markdown
## Relevant Documentation

- [Library Official Docs](https://example.com/docs#section)
  - Specific feature implementation guide
  - Why: Needed for X functionality
- [Framework Guide](https://example.com/guide#integration)
  - Integration patterns section
  - Why: Shows how to connect components
Phase 4: Deep Strategic Thinking

Think Harder About:

  • How does this feature fit into the existing architecture?
  • What are the critical dependencies and order of operations?
  • What could go wrong? (Edge cases, race conditions, errors)
  • How will this be tested comprehensively?
  • What performance implications exist?
  • Are there security considerations?
  • How maintainable is this approach?

Design Decisions:

  • Choose between alternative approaches with clear rationale
  • Design for extensibility and future modifications
  • Plan for backward compatibility if needed
  • Consider scalability implications
Phase 5: Plan Structure Generation

Create comprehensive plan with the following structure:

Whats below here is a template for you to fill for the implementation agent:

markdown
# Feature: <feature-name>

The following plan should be complete, but its important that you validate documentation and codebase patterns and task sanity before you start implementing.

Pay special attention to naming of existing utils types and models. Import from the right files etc.

## Feature Description

<Detailed description of the feature, its purpose, and value to users>

## User Story

As a <type of user>
I want to <action/goal>
So that <benefit/value>

## Problem Statement

<Clearly define the specific problem or opportunity this feature addresses>

## Solution Statement

<Describe the proposed solution approach and how it solves the problem>

## Out of Scope / Non-Goals

<Explicitly bound the work: what this feature does NOT include. Name the things a reasonable reader might assume are in scope but aren't — this is what stops the agent from gold-plating or solving the wrong problem.>

- Not included: <thing> (defer to <later / separate ticket>)
- Not changing: <existing behavior to leave alone>

## Feature Metadata

**Feature Type**: [New Capability/Enhancement/Refactor/Bug Fix]
**Estimated Complexity**: [Low/Medium/High]
**Primary Systems Affected**: [List of main components/services]
**Dependencies**: [External libraries or services required]

## Related Work

<Links between this plan and the work around it. Distinct from CONTEXT REFERENCES below (which lists files/docs to read for *this* implementation) — this is the plan's place in the larger graph.>

**Implements**: <ticket id / link>   ·   **Epic**: <engineering-plan.md path or epic link — if this ticket inherits an epic's engineering plan (see Mission), record it here>

**Back-references** (plans this builds on or inherits decisions from):

- `.claude/plans/<prior-plan>.md` - Why: shares the auth seam / reuses the X service

**Forward-references** (plans that extend or supersede this — append as follow-ups get created):

- (none yet)

---

## CONTEXT REFERENCES

### Relevant Codebase Files IMPORTANT: YOU MUST READ THESE FILES BEFORE IMPLEMENTING!

<List files with line numbers and relevance>

- `path/to/file.py` (lines 15-45) - Why: Contains pattern for X that we'll mirror
- `path/to/model.py` (lines 100-120) - Why: Database model structure to follow
- `path/to/test.py` - Why: Test pattern example

### New Files to Create

- `path/to/new_service.py` - Service implementation for X functionality
- `path/to/new_model.py` - Data model for Y resource
- `tests/path/to/test_new_service.py` - Unit tests for new service

### Relevant Documentation YOU SHOULD READ THESE BEFORE IMPLEMENTING!

- [Documentation Link 1](https://example.com/doc1#section)
  - Specific section: Authentication setup
  - Why: Required for implementing secure endpoints
- [Documentation Link 2](https://example.com/doc2#integration)
  - Specific section: Database integration
  - Why: Shows proper async database patterns

### Patterns to Follow

<Specific patterns extracted from codebase - include actual code examples from the project>

**Naming Conventions:** (for example)

**Error Handling:** (for example)

**Logging Pattern:** (for example)

**Other Relevant Patterns:** (for example)

---

## IMPLEMENTATION PLAN

Phases run **top to bottom by default** — each assumes the phase above it is done. Where that is NOT the true dependency, make it explicit with a `**Depends on:**` line under the phase header, and a `**Independent of:**` line where two phases don't block each other. Independent phases are candidates to run in **parallel** (e.g. separate worktrees / parallel loops). Only annotate where it changes execution order or unlocks parallelism — skip the obvious sequential case.

### Phase 1: Foundation

<Describe foundational work needed before main implementation>

**Tasks:**

- Set up base structures (schemas, types, interfaces)
- Configure necessary dependencies
- Create foundational utilities or helpers

### Phase 2: Core Implementation

**Depends on:** Phase 1 (needs the base schemas/types)

<Describe the main implementation work>

**Tasks:**

- Implement core business logic
- Create service layer components
- Add API endpoints or interfaces
- Implement data models

### Phase 3: Integration

<Describe how feature integrates with existing functionality>

**Tasks:**

- Connect to existing routers/handlers
- Register new components
- Update configuration files
- Add middleware or interceptors if needed

### Phase 4: Testing & Validation

<Describe testing approach>

**Tasks:**

- Implement unit tests for each component
- Create integration tests for feature workflow
- Add edge case tests
- Validate against acceptance criteria

---

## STEP-BY-STEP TASKS

IMPORTANT: Execute every task in order, top to bottom. Each task is atomic and independently testable.

### Task Format Guidelines

Use information-dense keywords for clarity:

- **CREATE**: New files or components
- **UPDATE**: Modify existing files
- **ADD**: Insert new functionality into existing code
- **REMOVE**: Delete deprecated code
- **REFACTOR**: Restructure without changing behavior
- **MIRROR**: Copy pattern from elsewhere in codebase

### {ACTION} {target_file}

- **IMPLEMENT**: {Specific implementation detail}
- **PATTERN**: {Reference to existing pattern - file:line}
- **IMPORTS**: {Required imports and dependencies}
- **GOTCHA**: {Known issues or constraints to avoid}
- **VALIDATE**: `{executable validation command}`
- **SATISFIES**: {which acceptance criterion this task advances — e.g. AC #2 — so every task traces to a criterion}

<Continue with all tasks in dependency order...>

---

## TESTING STRATEGY

<Define testing approach based on project's test framework and patterns discovered during research>

### Unit Tests

<Scope and requirements based on project standards>

Design unit tests with fixtures and assertions following existing testing approaches

### Integration Tests

<Scope and requirements based on project standards>

### Edge Cases

<List specific edge cases that must be tested for this feature>

---

## VALIDATION COMMANDS

<Define validation commands based on project's tools discovered in Phase 2>

Execute every command to ensure zero regressions and 100% feature correctness.

### Level 1: Syntax & Style

<Project-specific linting and formatting commands>

### Level 2: Unit Tests

<Project-specific unit test commands>

### Level 3: Integration Tests

<Project-specific integration test commands>

### Level 4: Manual Validation

<Feature-specific manual testing steps - API calls, UI testing, etc.>

### Level 5: Additional Validation (Optional)

<MCP servers or additional CLI tools if available>

---

## ACCEPTANCE CRITERIA

<List specific, measurable criteria that must be met for completion>

- [ ] Feature implements all specified functionality
- [ ] All validation commands pass with zero errors
- [ ] Unit test coverage meets requirements (80%+)
- [ ] Integration tests verify end-to-end workflows
- [ ] Code follows project conventions and patterns
- [ ] No regressions in existing functionality
- [ ] Documentation is updated (if applicable)
- [ ] Performance meets requirements (if applicable)
- [ ] Security considerations addressed (if applicable)

---

## COMPLETION CHECKLIST

- [ ] All tasks completed in order
- [ ] Each task validation passed immediately
- [ ] All validation commands executed successfully
- [ ] Full test suite passes (unit + integration)
- [ ] No linting or type checking errors
- [ ] Manual testing confirms feature works
- [ ] Acceptance criteria all met
- [ ] Code reviewed for quality and maintainability

---

## OPEN QUESTIONS / ASSUMPTIONS

<Surface anything still uncertain instead of silently guessing. List the assumptions this plan makes, and any question that — if answered differently — would change the plan. Flag unresolved critical questions for the user before execution.>

## NOTES (open canvas)

<No fixed shape. Reason freely here: alternatives you weighed and rejected and why, a tradeoff matrix, a sequencing or rollout risk, a data-flow sketch, open threads, links — whatever serves the plan. The sections above template the plan's *shape* so the trifecta and the implementation agent can consume it; this section keeps your *reasoning* unconstrained. Prose, lists, tables, code blocks all welcome.>

## AMENDMENTS

<Append-only history of changes made to this plan AFTER it was first approved/executed. Leave empty at creation; newest entry at the bottom. Each entry: date — what changed and why.>

- <ISO date> — <what changed and why, e.g. "scope cut: deferred bulk-import to a follow-up ticket after AC review">

Output Format

Filename: .claude/plans/{kebab-case-descriptive-name}.md

  • Replace {kebab-case-descriptive-name} with short, descriptive feature name
  • Examples: add-user-authentication.md, implement-search-api.md, refactor-database-layer.md

Directory: Create .claude/plans/ if it doesn't exist

Quality Criteria

Context Completeness ✓
  • All necessary patterns identified and documented
  • External library usage documented with links
  • Integration points clearly mapped
  • Gotchas and anti-patterns captured
  • Every task has executable validation command
  • Phase 2's clarifying cluster was asked and answered, or explicitly recorded as nothing open
Implementation Ready ✓
  • Another developer could execute without additional context
  • Tasks ordered by dependency (can execute top-to-bottom)
  • Each task is atomic and independently testable
  • Pattern references include specific file:line numbers
Pattern Consistency ✓
  • Tasks follow existing codebase conventions
  • New patterns justified with clear rationale
  • No reinvention of existing patterns or utils
  • Testing approach matches project standards
Information Density ✓
  • No generic references (all specific and actionable)
  • URLs include section anchors when applicable
  • Task descriptions use codebase keywords
  • Validation commands are non interactive executable

Success Metrics

One-Pass Implementation: Execution agent can complete feature without additional research or clarification — clarification the user owes the plan belongs in Phase 2's gate, not deferred to the execution agent

Validation Complete: Every task has at least one working validation command

Context Rich: The Plan passes "No Prior Knowledge Test" - someone unfamiliar with codebase can implement using only Plan content

Confidence Score: #/10 that execution will succeed on first attempt

Report

After creating the Plan, provide:

  • Summary of feature and approach
  • Full path to created Plan file
  • Complexity assessment
  • Key implementation risks or considerations
  • Estimated confidence score for one-pass success

© coleam00, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/piv-plan-implementation of coleam00/skills.

Open the folder on GitHubat commit 847be08

Compare with similar skills

Piv Plan Implementation 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.

Piv Plan Implementation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Piv Plan Implementation this skillcoleam00/skills670—~4.9kAutomated safety check: PassMIT
Run JiraDataDog/datadog-agent3.8k—~635Automated safety check: PassApache-2.0
PRP Implementation PlannerWirasm/prp2.3k—~4.1kAutomated safety check: PassMIT
PRP PlanWirasm/prp2.3k—~4kAutomated safety check: PassMIT
sfdx-hardis Architecture Guidehardisgroupcom/sfdx-hardis401—~2.1kAutomated safety check: PassAGPL-3.0
Repomix Codebase Packeryamadashy/repomix29k—~1.3kAutomated safety check: NotesMIT

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Categories

Questions about Piv Plan Implementation

What does Piv Plan Implementation do?

Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research. Piv Plan Implementation is an agent skill from coleam00/skills. Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research.

When should I use Piv Plan Implementation?

Piv Plan Implementation fits situations like: you have a ticket; feature and need a one-pass-ready plan before writing any code.

How do I install Piv Plan Implementation in Claude Code?

Run `npx skills add coleam00/skills --skill piv-plan-implementation -a claude-code`. Or copy the skill folder (.claude/skills/piv-plan-implementation in coleam00/skills) into .claude/skills/piv-plan-implementation in your project. Claude Code loads it when a task matches its description.

How do I install Piv Plan Implementation in Codex?

Run `npx skills add coleam00/skills --skill piv-plan-implementation -a codex`. Or copy the skill folder (.claude/skills/piv-plan-implementation in coleam00/skills) into .agents/skills/piv-plan-implementation in your project. Codex loads it when a task matches its description.

Can I use Piv Plan Implementation 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 coleam00/skills --skill piv-plan-implementation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/piv-plan-implementation, .gemini/skills/piv-plan-implementation, .github/skills/piv-plan-implementation and .opencode/skills/piv-plan-implementation in your project.

What does Piv Plan Implementation need to run?

Going by SKILL.md and its folder, Piv Plan Implementation needs the command-line tools its instructions call (gh).

Does Piv Plan Implementation 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 Piv Plan Implementation 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 Piv Plan Implementation use?

Piv Plan Implementation 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 Piv Plan Implementation use?

About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Piv Plan Implementation?

Skills that share tags, products or a category with Piv Plan Implementation: Run Jira (DataDog/datadog-agent, 3.8k stars), PRP Implementation Planner (Wirasm/prp, 2.3k stars), PRP Plan (Wirasm/prp, 2.3k stars) and sfdx-hardis Architecture Guide (hardisgroupcom/sfdx-hardis, 401 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Piv Plan Implementation?

coleam00 (a GitHub user) maintains it in coleam00/skills, which has 670 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on October 7, 2026.

Source: coleam00/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.