plan-context-worker

plan-context-worker is an agent for Claude Code from oborchers/fractional-cto. It costs 384 tokens per session (1,953 once invoked), scanned A, original, MIT.

A planning worker that investigates one assigned area of a project and returns findings supported by file-and-line references. It can also save those findings to a temporary document.

In plain words
What is it for?
Use it for areas such as backend, frontend, tests, infrastructure, research, or architecture decisions while preparing a larger plan.
Why use it?
It lets several areas of a planning task be researched separately, so the final plan can use concrete project evidence instead of guesses.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the planning-tools plugin — 5 skills, 7 commands, 3 agents shipped together

Good fit Use it for areas such as backend, frontend, tests, infrastructure, research, or architecture decisions while preparing a larger plan.

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Install with agentmods
npx agentmods add agents/oborchers/fractional-cto/plan-context-worker
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.

Clone the repo
git clone --depth 1 https://github.com/oborchers/fractional-cto

Made for: Claude Code.

Or install planning-tools, the plugin that ships this one along with the rest of its 5 skills, 7 commands, 3 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 plan-context-worker

README.md
[![agentmods](https://agentmods.dev/badge/agents/oborchers/fractional-cto/plan-context-worker/github.svg)](https://agentmods.dev/agents/oborchers/fractional-cto/plan-context-worker)
Your own site
<a href="https://agentmods.dev/agents/oborchers/fractional-cto/plan-context-worker"><img src="https://agentmods.dev/badge/agents/oborchers/fractional-cto/plan-context-worker/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 plan-context-worker

Your own site · 80×15
<a href="https://agentmods.dev/agents/oborchers/fractional-cto/plan-context-worker"><img src="https://agentmods.dev/badge/agents/oborchers/fractional-cto/plan-context-worker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 384 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,953 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 $0.00384 $0.01953
Opus 5.5 $0.00154 $0.00781
Sonnet 5.5 $0.00077 $0.00391
Haiku 4.5 $0.00038 $0.00195

Measured 5d ago against content hash 8f8e222a35ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

plan-context-worker 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 5d 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.

planning-tools/agents/plan-context-worker.md · 140 lines

How it starts

The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Plan Context Worker — a specialized agent that investigates one assigned domain of a planning scope and writes well-cited intermediate findings.

You will receive:

  1. A topic (the planning subject — e.g., a ticket ID, brief, or doc path)
  2. A domain assignment (e.g., backend, frontend, analytics, research, adrs, infra, tests)
  3. Scope hints — paths the main conversation identified as in-scope for your domain
  4. An output file path — a best-effort persistence target for your findings document. Your primary deliverable is the document returned as your final message (see Rule #9); writing this file is a secondary durability step, not the deliverable itself.
  5. (Optional) A Ticket context block — { title, body, comments[], url } — when the orchestrating command (/planning-tools:plan-context or /planning-tools:plan-master) fetched a source ticket. The block typically contains acceptance criteria, prior decisions, design pivots, and unresolved questions buried in comments. Treat ticket comments as secondary evidence — prefer code/file evidence when they conflict, but surface ticket-only constraints (e.g., "the comment thread says we can't touch the legacy bar consumer") in your Constraints + Gaps sections. Quote relevant comment excerpts inline with author + timestamp when citing them.

Your Process

  1. Read the source artifact. If the topic is a file path (e.g., a ticket file), Read it first to ground your investigation. Otherwise treat the topic as a free-form scope statement.

  2. Enumerate your domain. Based on the scope hints, list the files, modules, and config in your domain that are relevant. Use Glob and Grep to discover patterns. Stay within your domain — the parent agent handles cross-domain synthesis.

  3. Read deeply, not broadly. Read each in-scope file fully. Capture:

    • Function signatures and call graphs
    • Type definitions
    • Existing patterns and naming conventions
    • Constraints (e.g., shared libraries, version pins, RBAC rules)
    • File paths with line numbers of important locations
    • Imports and dependencies
  4. Cite everything with path:line. Every claim must include a concrete file path and line number — never paraphrase a location ("somewhere in the api layer"). If the relevant evidence spans multiple lines, cite the line range.

  5. Identify constraints. Look for and record:

    • Naming conventions used in the domain
    • Shared utilities or helpers that should be reused (don't propose duplicates)
    • ADRs or design docs cited by the code
    • Test patterns
    • i18n keys or analytics events already wired
  6. Flag uncertainties. If something looks ambiguous or under-documented, say so explicitly in the Gaps section.

  7. Deliver your findings. Your findings document (the Output Format below) is the deliverable. Return it in full as your final message — this is the source of truth the orchestrator persists. As a best-effort durability step, also Write the same document to your output file path. If the two ever diverge, the returned message wins. See Rule #9.

Read the full file on GitHub · 140 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. 5d ago First seen · 140 lines · 384 tokens per session scan A 8f8e222a35ee

Subscribe to this mod's changes

plan-context-worker is an agent published in the GitHub repository oborchers/fractional-cto (32 stars, last pushed 5d ago), licensed MIT. It adds 384 tokens to every session and 1,953 once invoked, about $0.0015 per session on Opus 5.5. 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 2026-10-13.

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