next-phase-plan

next-phase-plan is a skill for Claude Code from TonyWu20/fortran-dev-pipeline. It costs 119 tokens per session (1,409 once invoked), scanned A, original, MIT.

A conversational planning workflow for defining the next phase of a Fortran scientific project. It gathers project context and produces a high-level Markdown plan, rather than a task-level TOML plan.

In plain words
What is it for?
Use it to discuss goals, review recent project history and plans, and create the next phase's initial plan document.
Why use it?
It gives an unfinished project phase a documented scope and design before detailed task breakdown and implementation.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the fortran-dev-pipeline plugin — 7 skills, 6 agents, 2 hooks shipped together

Good fit Use it to discuss goals, review recent project history and plans, and create the next phase's initial plan document.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tonywu20/fortran-dev-pipeline/next-phase-plan
Install

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.

Any agent
npx skills add TonyWu20/fortran-dev-pipeline --skill next-phase-plan
Clone the repo
git clone --depth 1 https://github.com/TonyWu20/fortran-dev-pipeline

Made for: Claude Code.

Or install fortran-dev-pipeline, the plugin that ships this one along with the rest of its 7 skills, 6 agents, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for next-phase-plan

README.md
[![agentmods](https://agentmods.dev/badge/skills/tonywu20/fortran-dev-pipeline/next-phase-plan/github.svg)](https://agentmods.dev/skills/tonywu20/fortran-dev-pipeline/next-phase-plan)
Your own site
<a href="https://agentmods.dev/skills/tonywu20/fortran-dev-pipeline/next-phase-plan"><img src="https://agentmods.dev/badge/skills/tonywu20/fortran-dev-pipeline/next-phase-plan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for next-phase-plan

Your own site · 80×15
<a href="https://agentmods.dev/skills/tonywu20/fortran-dev-pipeline/next-phase-plan"><img src="https://agentmods.dev/badge/skills/tonywu20/fortran-dev-pipeline/next-phase-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,409 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00119 $0.01409
Opus 5 $0.00060 $0.00705
Sonnet 5 $0.00024 $0.00282
Haiku 4.5 $0.00012 $0.00141

Measured 12d ago against content hash dee3988e67bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

next-phase-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 12d ago.

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.

skills/next-phase-plan/SKILL.md · 182 lines

How it starts

The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Next Phase Plan

Facilitates a structured discussion to define the next phase of work. Produces a high-level plan document — not a TOML task breakdown. The plan document then goes through /plan-review (architectural gate) and /enrich-phase-plan (TOML elaboration) before implementation.

Trigger

/next-phase-plan

No arguments. This skill is conversational — it gathers context and discusses with the user.

Command-line Tools

  • use fd instead of find
  • use rg instead of grep

Process

Step 1: Gather Context

Automatically collect background before engaging the user:

  1. Project memory:

    MEMORY_DIR="$HOME/.claude/projects/$(pwd | sd '/' '-')/memory"
    

    Read $MEMORY_DIR/MEMORY.md and every linked memory file.

  2. Recent git history (last 10 commits on main):

    git log --oneline -10 main
    
  3. Existing plan files (if any):

    fd -e md -e toml . plans/
    

    Read the most recent plan file to understand what was last planned.

  4. Deferred improvements from prior review rounds:

    fd deferred.md notes/pr-reviews/
    

    Read all deferred.md files found — these are improvements the reviewer identified but the plan didn't commission, and they are candidates for this phase.

  5. Execution reports (if any):

    fd -e md . execution_reports/
    

    Skim the most recent report to understand what was completed and what failed.

Step 2: Propose Phase Goals

Invoke the fortran-dev-pipeline:fortran-architect agent to synthesize the context and propose a set of goals for the next phase:

You are helping define the next phase of a Fortran scientific project.

<project_memory>
{{MEMORY_CONTENTS}}
</project_memory>

<recent_git_history>
{{GIT_LOG}}
</recent_git_history>

<last_plan>
{{LAST_PLAN_CONTENTS — or "No prior plan found"}}
</last_plan>

<deferred_improvements>
{{DEFERRED_CONTENTS — or "None"}}
</deferred_improvements>

<execution_report>
{{LAST_REPORT_SUMMARY — or "No prior execution report"}}
</execution_report>

Propose candidate goals for the NEXT phase. For each goal:
- State what it achieves and why it's the right next step
- Estimate whether it is a small, medium, or large effort
- Note any dependencies on prior work or on other goals in this list

Also flag any deferred improvements that are now appropriate to incorporate.

Keep the list focused — 3 to 7 goals is ideal. Do not decompose into tasks.

Read the full file on GitHub · 182 lines

Changes

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.

  1. 12d ago First seen · 182 lines · 119 tokens per session scan A dee3988e67bc

Subscribe to this mod's changes

next-phase-plan is a skill published in the GitHub repository TonyWu20/fortran-dev-pipeline (5 stars, last pushed 4mo ago), licensed MIT. It adds 119 tokens to every session and 1,409 once invoked, about $0.0006 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 2026-08-31.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens