pm

pm is an agent for Claude Code from avelikiy/great_cto. It costs 55 tokens per session (7,861 once invoked), scanned A, original, MIT.

A project-planning agent that turns an approved technical architecture into tasks, dependencies, timings, and assigned workers.

In plain words
What is it for?
Use it to break architecture into tasks, identify dependencies and parallel work, estimate the schedule, assign agents, and create a Mermaid Gantt plan for approval.
Why use it?
It makes the build sequence and parallel work visible before implementation starts.

Agent for Claude Code

Written for Claude Code: effort in frontmatter. Also seen: model in frontmatter; reads .claude/ paths; mentions subagents.

Not installable on its own: it runs a file from its repository that does not travel with it. Clone the repository, or install whatever ships that file. The line is node scripts/lib/impl-brief.mjs validate docs/impl-briefs/IMPL-BRIEF-<id>.md.

Part of the great-cto plugin — 40 skills, 44 commands, 72 agents shipped together

Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add avelikiy/great_cto
Claude Code
/plugin install great-cto

Made for: Claude Code.

Or install great-cto, the plugin that ships this one along with the rest of its 40 skills, 44 commands, 72 agents.

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 pm

README.md
[![agentmods](https://agentmods.dev/badge/agents/avelikiy/great_cto/pm.svg)](https://agentmods.dev/agents/avelikiy/great_cto/pm)
Your own site
<a href="https://agentmods.dev/agents/avelikiy/great_cto/pm"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/pm.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 7,861 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00055 $0.07861
Opus 5 $0.00028 $0.03930
Sonnet 5 $0.00011 $0.01572
Haiku 4.5 $0.00006 $0.00786

Measured 2d ago against content hash d85275f59c35, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

pm 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 2d 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.

agents/pm.md · 661 lines

How it starts

The opening of the file, as written. The whole thing — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Project Manager. You turn architecture into an executable plan: dependency graph, parallelism analysis, agent allocation, time estimates, and a Mermaid Gantt chart. You close with a gate:plan human checkpoint.

You do not write code. You do not modify the ARCH doc. You read it, extract tasks, and produce docs/plans/PLAN-<slug>.md.


Phase task tracking (mandatory)

Follow the canonical block in agents/_shared/phase-task.md with <agent-name> = pm. Open at phase start, close with --verdict ok|fail at phase end. The Beads-unavailable fallback is defined there.

Step 0a — Feature prioritisation (run when multiple features compete)

If the CTO provides a list of features or initiatives (not a single feature with an ARCH doc), prioritise BEFORE decomposing. Apply the right framework based on context:

Choosing a framework

Context Framework Formula
Prioritising customer problems / opportunity space Opportunity Score Importance × (1 − Satisfaction) — normalise both to 0–1
Quick prioritisation of ideas with risk/confidence factor ICE Impact × Confidence × Ease — score each 1–10
Larger team, need to weight reach separately RICE (Reach × Impact × Confidence) / Effort
Stakeholder alignment needed across competing requirements MoSCoW Must / Should / Could / Won't — use for scope conversations

Applying the framework

Opportunity Score (recommended for product problems):

For each opportunity, gather from user interviews or surveys:
  Importance:   How important is solving this? (0–1)
  Satisfaction: How satisfied are users with current alternatives? (0–1)
  Score:        Importance × (1 − Satisfaction)

High importance + low satisfaction = highest score = best opportunity.

ICE (fast, for initiatives and ideas):

  Impact (1–10):     What's the expected outcome if it works?
  Confidence (1–10): How confident are we? (reduces overconfidence on risky bets)
  Ease (1–10):       How easy to implement? (10 = trivial, 1 = very hard)
  Score:             I × C × E — higher = prioritise first

Read the full file on GitHub · 661 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. 2d ago Changed · +11 lines d85275f59c35
  2. 6d ago First seen · 650 lines · 55 tokens per session scan A 95004ba774d1

Subscribe to this mod's changes

pm is an agent published in the GitHub repository avelikiy/great_cto (89 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 7,861 once invoked, about $0.0003 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.

Related

Other agents, from other repositories

design-rules

Condensed 10 Golden Rules from the Agent Design Bible.

jmagly/aiwg · 0 tokens

plan-creation-eng-lead

Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.

QBall-Inc/the-bulwark · 48 tokens

product-ideation-segment-analyzer

Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.

QBall-Inc/the-bulwark · 63 tokens

skill-eval-grader

Artifact-based grader for subjective skill evaluations. Reads evidence files (generated SKILL.md, templates, run traces) against a rubric and returns PASS/FAIL with structured reasoning. Used by grade.ts for fuzzy assertions where deterministic checks cannot apply.

QBall-Inc/the-bulwark · 53 tokens

consistency-and-history

Analyze git history and cross-file consistency — stale references, dead code, broken importers after renames/removals, established-convention enforcement.

ncoevoet/claude-review-all · 34 tokens

market-analyst

You are market-analyst — great-pm's always-on competitive-intelligence analyst. You know the competitors, the size of the prize, and the gaps no rival has filled — and you keep that current.

VandanaAjayDubey111/great-pm · 49 tokens