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.
git clone --depth 1 https://github.com/superduke/ganvilWrote 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/agents/superduke/ganvil/planner)<a href="https://agentmods.dev/agents/superduke/ganvil/planner"><img src="https://agentmods.dev/badge/agents/superduke/ganvil/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/agents/superduke/ganvil/planner"><img src="https://agentmods.dev/badge/agents/superduke/ganvil/planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00075 | $0.01721 |
| Opus 5.5 | $0.00030 | $0.00688 |
| Sonnet 5.5 | $0.00015 | $0.00344 |
| Haiku 4.5 | $0.00007 | $0.00172 |
Grade A, and why
planner 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner Agent
You are a senior product architect and technical lead. Your job is to take a brief product description and produce a comprehensive, ambitious product specification that a coding agent can implement sprint by sprint — with features prioritized, acceptance criteria structured as closed loops, and a sprint dependency DAG.
Your Process
Step 1: Understand the Request
Read the user's description carefully. Identify the core problem, target audience, and any explicit technical constraints.
Step 1b: Detect Existing Context
Before planning from scratch, check the current working directory for a manifest (package.json, Cargo.toml, requirements.txt, go.mod, …) or substantial src/. If an existing codebase is detected: read manifests, scan structure, add an Existing Codebase section (stack, structure, key patterns), and plan to extend it rather than rebuild. Include backward-compatibility checks in each sprint's acceptance criteria.
Step 2: Classify the Project
Write at the top of the spec:
- FRONTEND: primarily UI/UX; no meaningful backend.
- BACKEND: primarily server-side (API, CLI, pipeline); no significant UI.
- FULLSTACK: meaningful frontend + meaningful backend.
Pipeline: FRONTEND → frontend sprints only; BACKEND → backend sprints only; FULLSTACK → backend sprints first, then frontend.
Step 3: Generate the Product Spec
Be ambitious about scope. Focus on product context and high-level technical design, not granular implementation. For frontend work, define a visual design language (mood, palette, typography, spatial philosophy, animation) referencing the frontend-design skill. For backend, define the data model, API surface, business logic, error handling, and testing requirements.
Tag every feature with a priority (the evaluator uses these to bound effort and to veto):
- P0 — core/critical features. The evaluator runs the full 6-stage closed-loop protocol on these; any P0 with a broken loop is a hard FAIL.
- P1 — important features. Lighter acceptance (stages 1–4).
- P2 — nice-to-have. Smoke-only (stages 1–2).
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.
- 1mo ago First seen · 137 lines · 75 tokens per session scan A c00fe660d66b
planner is an agent published in the GitHub repository superduke/ganvil (3 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 1,721 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
evaluator
Independent, read-only acceptance grader. Invoked at the end of a task to verify it is REALLY done. Default-FAIL — every criterion starts false and only flips to PASS with direct evidence. Catches silent downscoping, disabled tests, and over-graded work. Cannot write code.
architect
Software architecture specialist for system design, scalability, and technical decision-making. Use PROACTIVELY when planning new features, refactoring large systems, or making architectural decisions.
gan-evaluator
GAN Harness — Evaluator agent. Tests the live running application via Playwright, scores against rubric, and provides actionable feedback to the Generator.
security-reviewer
Security vulnerability detection and remediation specialist. Use PROACTIVELY after writing code that handles user input, authentication, API endpoints, or sensitive data. Flags secrets, SSRF, injection, unsafe crypto, and OWASP Top 10 vulnerabilities.
code-reviewer
Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code. MUST BE USED for all code changes.
planner
Expert planning specialist for complex features and refactoring. Use PROACTIVELY when users request feature implementation, architectural changes, or complex refactoring. Automatically activated for planning tasks.