flight-plan-parser

flight-plan-parser is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 93 tokens per session (805 once invoked), scanned A, original, Apache-2.0.

A parser that turns written drone flight commands into waypoints, arrival times, and segment types such as takeoff, hover, fly, and land.

In plain words
What is it for?
Use it to process commands describing heights, coordinates, durations, and movement segments.
Why use it?
It converts human-readable flight plans into structured input that a drone simulator can follow.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to process commands describing heights, coordinates, durations, and movement segments.

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Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/flight-plan-parser
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,760 stars · on GitHub · skillsbench.ai

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 benchflow-ai/skillsbench --skill flight-plan-parser
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

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 flight-plan-parser

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/flight-plan-parser/github.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/flight-plan-parser)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/flight-plan-parser"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/flight-plan-parser/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 flight-plan-parser

Your own site · 80×15
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/flight-plan-parser"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/flight-plan-parser.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 805 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00093 $0.00805
Opus 5 $0.00046 $0.00402
Sonnet 5 $0.00019 $0.00161
Haiku 4.5 $0.00009 $0.00081

Measured 7d ago against content hash 74acf1d6c9d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

flight-plan-parser 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 7d 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.

tasks/drone-planning-control/environment/skills/flight-plan-parser/SKILL.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Flight Plan Parser

Overview

Converts human-readable flight commands into structured waypoints compatible with the drone simulator's trajectory planner. Each command maps to one flight segment with a mode tag (takeoff, hover, fly, land).

Output Format

waypoints      : (4 x n) numpy array  — rows are [x, y, z, yaw]
waypoint_times : (n,)    numpy array  — arrival time for each waypoint [seconds]
modes          : list of (n-1) strings — one mode per segment between waypoints

Supported Commands

Command pattern Mode
Take off to <h> m height in <t> seconds 'takeoff'
Hover at <h> m height for <t> seconds 'hover'
Fly from (<x>,<y>,<z>) to (<x'>,<y'>,<z'>) in <t> seconds 'fly'
Land from <h> m height in <t> seconds 'land'

Implementation Logic

Use a stateful parser class that accumulates waypoints, times, and modes as it processes each command line:

  • Maintain internal state: current position, current time, list of waypoints, list of arrival times, and list of mode strings.
  • On the first command, auto-insert a starting waypoint at the current position and t=0 if the list is empty.
  • Each command handler extracts numeric values via regex, advances the time accumulator, updates the current position, appends the end waypoint, and appends the mode string.
  • After all commands are processed, convert the lists to a (4 × n) numpy array (rows: x, y, z, yaw) and a (n,) time array.
  • Expose a top-level parse_flight_plan(text) function that instantiates the class, feeds it each line, and returns (waypoints, waypoint_times, modes).

Regex Strategy

Write one case-insensitive pattern per command type. Each pattern captures only the numeric fields:

  • Takeoff / Hover / Land: capture height and duration (2 groups).
  • Fly: capture start coordinates (x, y, z) and end coordinates (x', y', z') plus duration (7 groups). The from (...) and to (...) parts must handle optional whitespace around commas.

Read the full file on GitHub · 66 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. 7d ago First seen · 66 lines · 93 tokens per session scan A 74acf1d6c9d8

Subscribe to this mod's changes

flight-plan-parser is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 805 once invoked, about $0.0005 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 2026-09-03.

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