astro-dso-doc

astro-dso-doc is a skill for Claude Code, Codex from jjmartres/ai-coding-agents. It costs 244 tokens per session (12,145 once invoked), scanned A, original, MIT.

A generator for documenting deep-sky astrophotography projects, which are photographs of faint objects outside the Solar System. It creates web documentation, processing checklists, AstroBin upload data, PixInsight process icons, and a project description.

In plain words
What is it for?
Use it when documenting a deep-sky object project, preparing an AstroBin post, making a PixInsight workflow checklist, or generating related PixInsight project files.
Why use it?
It turns the details of an astronomy image-processing project into organized files and reusable upload and processing information.

Skill for Claude CodeCodex

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

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is cat > ./doc/processing-checklist.html << 'EOF'.

Good fit Use it when documenting a deep-sky object project, preparing an AstroBin post, making a PixInsight workflow checklist, or generating related PixInsight project files.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/jjmartres/ai-coding-agents
agentmods
npx agentmods add skills/jjmartres/ai-coding-agents/astro-dso-doc

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 astro-dso-doc

README.md
[![agentmods](https://agentmods.dev/badge/skills/jjmartres/ai-coding-agents/astro-dso-doc.svg)](https://agentmods.dev/skills/jjmartres/ai-coding-agents/astro-dso-doc)
Your own site
<a href="https://agentmods.dev/skills/jjmartres/ai-coding-agents/astro-dso-doc"><img src="https://agentmods.dev/badge/skills/jjmartres/ai-coding-agents/astro-dso-doc.svg" alt="Measured on agentmods" height="20"></a>
Per session 244 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 12,145 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 $0.00244 $0.12145
Opus 5 $0.00122 $0.06073
Sonnet 5 $0.00049 $0.02429
Haiku 4.5 $0.00024 $0.01214

Measured 8d ago against content hash 39b3c3187e81, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

astro-dso-doc 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 8d 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.

shared/.ai-agents/skills/astro-dso-doc/SKILL.md · 899 lines

How it starts

The opening of the file, as written. The whole thing — 899 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Astro DSO Documentation Generator

Generates five deliverables for a deep-sky object (DSO) astrophotography project:

  1. project.json — a flat JSON file containing the PixInsight project description data (copy the description field value into the Description box of .xosm)
  2. doc/index.html — a rich, self-contained HTML documentation page (path goes into the Documentation field of .xosm), using the Catppuccin flavor palette with a theme switcher.
  3. doc/processing-checklist.html — an interactive step-by-step PixInsight processing checklist adapted to the target's filter set (LRGB, HOO, SHO, RGB-only, etc.), using the same Catppuccin design system.
  4. astrobin.json — a structured JSON file containing all AstroBin image post fields, ready to copy-paste into the AstroBin upload form.
  5. Process Icons - <common_name> - <workflow_type> workflow.xpsm — a PixInsight process icon set tailored to the detected workflow (LRGB, RGB, HOO, SHO), generated from the processing checklist phases.

Workflow

Step 1 — Identify the Target

Extract the DSO name/catalog number from the user's message. If ambiguous or missing, ask for it before proceeding.

Common catalog prefixes: NGC, IC, M (Messier), Sh2 (Sharpless), B (Barnard), vdB, LBN, LDN, Ced, RCW.


Step 2 — Collect Acquisition Data (interactive)

Ask the user the following questions one block at a time (don't dump all at once). Wait for answers before proceeding.

Block A — Instrument:

What instrument did you use?
  (e.g. telescope model, focal length, f-ratio, camera, built-in filter)

Block B — Site:

Where did you image from?
  (location name, lat/lon if known, approximate altitude)

Block C — Session files:

Please provide the path to the folder or list of session files.
  (e.g. /path/to/sessions/ or paste filenames directly)

If the user gives a directory path, scan it with Bash:

ls -1 /path/to/sessions/ | grep -iE '\.(tiff?|fits?|xisf|cr2|nef|dng|raf|jpg|jpeg|png)$' | sort

Read the full file on GitHub · 899 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 8d ago First seen · 899 lines · 244 tokens per session scan A 39b3c3187e81

Subscribe to this mod's changes

astro-dso-doc is a skill published in the GitHub repository jjmartres/ai-coding-agents (45 stars, last pushed 2mo ago), licensed MIT. It adds 244 tokens to every session and 12,145 once invoked, about $0.0012 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 skills, from other repositories

cocoplus-config

CocoPlus configuration SSOT — $cocoplus sync propagates cocoplus.toml into downstream artifacts; $cocoplus migrate-config converts legacy safety-config.json. Invoked via $cocoplus sync and $cocoplus migrate-config.

Snowflake-Labs/cocoplus · 56 tokens

collect-preserve-specimens

Collect and preserve insect specimens following museum-grade standards including legal compliance, collection methods, humane dispatch, dry pinning, wet preservation, labeling, storage, and curation. Covers permit requirements, protected species regulations, sweep nets, beating trays, pitfall traps, light traps…

pjt222/agent-almanac · 139 tokens

analyze-diffusion-dynamics

Analyze the dynamics of diffusion processes using stochastic differential equations, Fokker-Planck equations, first-passage time distributions, and parameter sensitivity analysis. Use when deriving probability density evolution for a continuous-time diffusion process, computing mean first-passage times for bounded…

pjt222/agent-almanac · 80 tokens

analyze-tensegrity-system

Analyze a tensegrity system by identifying compression struts and tension cables, classifying type (class 1/2, biological/architectural), computing prestress equilibrium, verifying stability via Maxwell's rigidity criterion, and mapping biological tensegrity (microtubules, actin, intermediate filaments). Use when…

pjt222/agent-almanac · 94 tokens

assess-holistic-health

Conduct temperament-based health assessment from Hildegard von Bingen's Causae et Curae. Evaluates the four temperaments (sanguine, choleric, melancholic, phlegmatic), elemental correspondences (air, fire, earth, water), and provides dietary and lifestyle recommendations for rebalancing. Use when understanding…

pjt222/agent-almanac · 113 tokens

consult-natural-history

Reference Hildegard von Bingen's Physica natural history knowledge. Covers classification of plants, stones, animals, fish, birds, elements, and trees with their medicinal, symbolic, and practical properties. Enables cross- referencing between categories and application guidance. Use when exploring a specific plant…

pjt222/agent-almanac · 102 tokens