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.
npx agentmods add skills/mountainunicorn/add/plannpx skills add MountainUnicorn/add --skill plangit clone --depth 1 https://github.com/MountainUnicorn/addWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00014 | $0.03885 |
| Opus 5 | $0.00007 | $0.01943 |
| Sonnet 5 | $0.00003 | $0.00777 |
| Haiku 4.5 | $0.00001 | $0.00388 |
Grade A, and why
plan 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 544 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADD Plan Skill v{{VERSION}}
Create a detailed implementation plan from a feature specification. This skill analyzes acceptance criteria, breaks down work into manageable tasks, identifies parallelizable work, and estimates effort.
Overview
The Plan skill transforms a specification into an actionable implementation roadmap. It produces a plan document (docs/plans/{feature}-plan.md) that guides development work and enables:
- Task breakdown and prioritization
- Effort estimation
- Dependency identification
- Parallelization opportunities
- Risk assessment
- Resource allocation
The plan bridges the gap between "what to build" (spec) and "how to build it" (implementation).
Pre-Flight Checks
-
Verify spec exists and is complete
- Read spec file from argument
- Verify YAML frontmatter is valid
- Extract feature name, version, status
- Verify acceptance criteria are defined
- Verify user test cases are defined (if applicable)
-
Load project configuration
- Read .add/config.json
- Load team size (solo, small, large)
- Load tech stack
- Load constraints (timeline, budget, etc.)
- Load parallelization preferences
-
Analyze dependencies
- Identify upstream work needed
- Check for blockers
- Identify integration points
-
Check for existing plan
- Look for docs/plans/{feature}-plan.md
- If exists, ask user: overwrite or update?
- Preserve previous efforts if updating
-
Check for session handoff — per the Session-Handoff Preflight in
${CLAUDE_PLUGIN_ROOT}/references/skill-epilogue.md
Execution Steps
Step 1: Analyze Acceptance Criteria
For each acceptance criterion:
-
Understand the requirement
- Read AC text carefully
- Identify what "done" means
- Note any explicit constraints
-
Decompose into tasks
- What code needs to be written?
- What tests need to be written?
- What configuration changes?
- What documentation?
- What other work (DB migrations, etc.)?
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.
- yesterday First seen · 544 lines · 14 tokens per session scan A b8b1f88eff26
plan is a skill published in the GitHub repository MountainUnicorn/add (11 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 3,885 once invoked, about $0.0001 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.
Other skills, from other repositories
swarm
Launching multi-agent parallel work with the Agentic SDLC. Use when a task benefits from decomposition into parallel subtasks.
finish
Completing a development branch for merge readiness. Use when implementation and tests pass and the branch needs formal preparation for review and merge.
grill
Interrogating requirements to validate before building. Use before swarm decomposition, design decisions on ambiguous features, or when scope creep risk is high.
team
Referencing the agent roster, roles, coordination model, and dispatch modes. Use when spawning agents or checking permissions.
ticket
Associate every PDS task with a GitHub issue. Orchestrator finds or creates the ticket, posts plan and acceptance criteria as a checkbox list, updates it as work progresses. Use at Phase 1 of every swarm.
triage
Triage insights into actionable GitHub issues across repos. Use after running /insights to convert analysis into tracked work.