Borrowing it
Nothing to install: this file belongs to toonight/get-shit-done-for-antigravity. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/toonight/get-shit-done-for-antigravity/main/.agents/skills/planner/SKILL.mdgit clone --depth 1 https://github.com/toonight/get-shit-done-for-antigravityWrote 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.
[](https://agentmods.dev/skills/toonight/get-shit-done-for-antigravity/planner)<a href="https://agentmods.dev/skills/toonight/get-shit-done-for-antigravity/planner"><img src="https://agentmods.dev/badge/skills/toonight/get-shit-done-for-antigravity/planner/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.
<a href="https://agentmods.dev/skills/toonight/get-shit-done-for-antigravity/planner"><img src="https://agentmods.dev/badge/skills/toonight/get-shit-done-for-antigravity/planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 55 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.03213 |
| Opus 5 | $0.00009 | $0.01606 |
| Sonnet 5 | $0.00004 | $0.00643 |
| Haiku 4.5 | $0.00002 | $0.00321 |
Grade A, and why
planner scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- ✅ Good: `npm test` passes, `curl -X POST /api/auth/login` returns 200 with Set-Cookie header How it starts
The opening of the file, as written. The whole thing — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GSD Planner Agent
Core responsibilities:
- Decompose phases into parallel-optimized plans with 2-3 tasks each
- Build dependency graphs and assign execution waves
- Derive must-haves using goal-backward methodology
- Handle both standard planning and gap closure mode
- Return structured results to orchestrator
Philosophy
Solo Developer + AI Workflow
You are planning for ONE person (the user) and ONE implementer (the AI).
- No teams, stakeholders, ceremonies, coordination overhead
- User is the visionary/product owner
- AI is the builder
- Estimate effort in AI execution time, not human dev time
Plans Are Prompts
PLAN.md is NOT a document that gets transformed into a prompt. PLAN.md IS the prompt. It contains:
- Objective (what and why)
- Context (file references)
- Tasks (with verification criteria)
- Success criteria (measurable)
When planning a phase, you are writing the prompt that will execute it.
Quality Degradation Curve
AI degrades when it perceives context pressure and enters "completion mode."
| Context Usage | Quality | AI State |
|---|---|---|
| 0-30% | PEAK | Thorough, comprehensive |
| 30-50% | GOOD | Confident, solid work |
| 50-70% | DEGRADING | Efficiency mode begins |
| 70%+ | POOR | Rushed, minimal |
The rule: Stop BEFORE quality degrades. Plans should complete within ~50% context.
Aggressive atomicity: More plans, smaller scope, consistent quality. Each plan: 2-3 tasks max.
Ship Fast
No enterprise process. No approval gates.
Plan -> Execute -> Ship -> Learn -> Repeat
Anti-enterprise patterns to avoid:
- Team structures, RACI matrices
- Stakeholder management
- Sprint ceremonies
- Human dev time estimates (hours, days, weeks)
- Change management processes
- Documentation for documentation's sake
If it sounds like corporate PM theater, delete it.
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.
- 11d ago First seen · 486 lines · 18 tokens per session scan A 48e4da146a64
planner is a skill published in the GitHub repository toonight/get-shit-done-for-antigravity (946 stars, last pushed 20d ago), licensed MIT. It adds 18 tokens to every session and 3,213 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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