planner-refine-assignments

planner-refine-assignments is a skill for Claude Code from TalonT-Org/AutoSkillit. It costs 44 tokens per session (2,279 once invoked), scanned A, original, MIT.

A planning review that checks assignments within one phase while showing summaries of work in other phases.

In plain words
What is it for?
Use it to refine who owns each piece of work in a phase and resolve conflicts between assignments before implementation planning continues.
Why use it?
It helps prevent overlapping ownership and catches assignments that conflict with related work elsewhere in the plan.

Skill for Claude Code

Written for Claude Code: hooks in frontmatter. Also seen: mentions subagents; positional $N argument.

Part of the autoskillit plugin — 121 skills, 1 MCP server shipped together

Good fit Use it to refine who owns each piece of work in a phase and resolve conflicts between assignments before implementation planning continues.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/talont-org/autoskillit/planner-refine-assignments
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 TalonT-Org/AutoSkillit --skill planner-refine-assignments
Clone the repo
git clone --depth 1 https://github.com/TalonT-Org/AutoSkillit

Made for: Claude Code.

Or install autoskillit, the plugin that ships this one along with the rest of its 121 skills, 1 MCP server.

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 planner-refine-assignments

README.md
[![agentmods](https://agentmods.dev/badge/skills/talont-org/autoskillit/planner-refine-assignments/github.svg)](https://agentmods.dev/skills/talont-org/autoskillit/planner-refine-assignments)
Your own site
<a href="https://agentmods.dev/skills/talont-org/autoskillit/planner-refine-assignments"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/planner-refine-assignments/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 planner-refine-assignments

Your own site · 80×15
<a href="https://agentmods.dev/skills/talont-org/autoskillit/planner-refine-assignments"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/planner-refine-assignments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,279 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 $0.00044 $0.02279
Opus 5.5 $0.00018 $0.00912
Sonnet 5.5 $0.00009 $0.00456
Haiku 4.5 $0.00004 $0.00228

Measured 1mo ago against content hash d7fda5d2c73c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

planner-refine-assignments 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 1mo 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.

src/autoskillit/skills_extended/planner-refine-assignments/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

planner-refine-assignments

L1 session that refines the assignments for a single phase by spawning one L0 subagent per assignment in parallel (3–5 assignments per phase). Receives a per-phase context file produced by merge_tier_results → _write_refine_contexts, which contains only this phase's assignments and peer_summaries (id/name/goal stubs) for all other phases. Each L0 reviews its assignment in the context of peer_summaries and refined_plan.json, returning structured suggestions. L1 applies suggestions, resolves intra-phase WP ownership conflicts, and writes the phase result file to $3/refine_contexts/{phase_id}_result.json.

When to Use

  • Dispatched by the L2 planner recipe in parallel — one session per phase
  • Accepts a per-phase context file from $3/refine_contexts/context_{phase_id}.json and refined_plan.json for phase context
  • Produces {phase_id}_result.json as input for the downstream merge_refined_assignments step

Arguments

  • $1 — Absolute path to the per-phase context file ($3/refine_contexts/context_{phase_id}.json). The file contains:
    • phase_id — identifier for the phase this session processes
    • task_file_path — path to the task description file (read from disk, not inline)
    • assignments — full AssignmentElaborated objects for this phase only (3–5 entries)
    • peer_summaries — {id, name, goal} stubs for all assignments in other phases
  • $2 — Absolute path to refined_plan.json (PlanDocument with phases as PhaseElaborated, for phase context)
  • $3 — Absolute path to the run-scoped planner directory (e.g., {{AUTOSKILLIT_TEMP}}/planner/run-YYYYMMDD-HHMMSS). Output is written to $3/refine_contexts/{phase_id}_result.json.

Critical Constraints

NEVER:

  • Write any file outside $3/
  • Directly modify the context file ($1) — always write a new result file
  • Allow an L0 subagent to write files directly (L0s return structured text only)
  • Emit phase_refined_path before writing the result file
  • Skip emitting phase_refined_path even if all L0s fail (write unchanged assignments, still emit)
  • Spawn more than 6 L0s in a single parallel batch
  • Read {{AUTOSKILLIT_TEMP}} artifacts not passed as positional arguments
  • Run subagents in the background (run_in_background: true is prohibited)
  • Write L0 prompts to intermediate l0_prompts/ files and read them back into the L1 context — spawn L0 subagents directly from in-memory context packets
  • Read source code files, test files, or recipe YAML files directly — codebase exploration is the L0 subagents' responsibility
  • Run Bash, Grep, or Glob commands for codebase exploration between L0 spawns

Read the full file on GitHub · 197 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. 1mo ago First seen · 197 lines · 44 tokens per session scan A d7fda5d2c73c

Subscribe to this mod's changes

planner-refine-assignments is a skill published in the GitHub repository TalonT-Org/AutoSkillit (5 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 2,279 once invoked, about $0.0002 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 2026-09-03.

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