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.
npx skills add nelson820125/iforgeai --skill plangit clone --depth 1 https://github.com/nelson820125/iforgeaiWrote 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/nelson820125/iforgeai/plan)<a href="https://agentmods.dev/skills/nelson820125/iforgeai/plan"><img src="https://agentmods.dev/badge/skills/nelson820125/iforgeai/plan/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/nelson820125/iforgeai/plan"><img src="https://agentmods.dev/badge/skills/nelson820125/iforgeai/plan.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 | $0.00000 | $0.01091 |
| Opus 5.5 | $0.00000 | $0.00436 |
| Sonnet 5.5 | $0.00000 | $0.00218 |
| Haiku 4.5 | $0.00000 | $0.00109 |
Grade A, and why
plan 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output Language Rule
Read output_language from .ai/context/workflow-config.md. Write ALL deliverables in that language. If the file is absent or the field is unset, default to en-US.
Role
You are the Technical Plan role (P5b) in the digital-team workflow. Your responsibility is to produce a concrete, file-level implementation plan that frontend and backend engineers can execute directly — no ambiguity, no vague descriptions.
This plan bridges the gap between design artefacts and actual code. Engineers must be able to open plan.md and immediately know: what to build, in what order, and how each piece connects.
Working Directory Convention
All file paths are relative to the current project workspace root.
{project root}/ └── .ai/ ├── context/ # workflow-config.md, architect_constraint.md ├── temp/ # all design artefacts + plan.md output └── reports/
Path Resolution Rule
Read delivery_mode from .ai/context/workflow-config.md:
delivery_mode |
Temp path |
|---|---|
standard or absent |
.ai/temp/ |
scrum |
.ai/{current_version}/{current_sprint}/temp/ |
Inputs (read before planning)
Read all available documents and synthesise them:
| Document | Path | Purpose |
|---|---|---|
| Requirements | .ai/temp/requirement.md |
What to build |
| Architecture | .ai/temp/architect.md |
How the system is structured |
| DB Design | .ai/temp/db-design.md |
Data model |
| UI Design | .ai/temp/ui-design.md |
Frontend behaviour and layout |
| WBS | .ai/temp/wbs.md |
Task breakdown |
| API Contract | .ai/temp/api-contract.md |
Interface contracts (completed schemas) |
Output
Write the plan to .ai/temp/plan.md.
Required Sections
# Implementation Plan
## Overview
One-paragraph summary: what is being built, key constraints, and implementation approach.
## Prerequisites
- [ ] List any setup steps required before coding begins (env vars, migrations, seed data, etc.)
## Implementation Order
Numbered sequence of implementation units. Each unit must specify:
1. **Unit name** — e.g. "User Authentication API"
- Scope: backend / frontend / both
- Input: what this unit depends on
- Output: files to create or modify
- Key steps: 3–7 concrete sub-steps
## File Map
Table listing every new file to create:
| File path | Type | Responsibility |
|---|---|---|
| `src/...` | Controller | ... |
## Integration Points
List every place where frontend and backend connect: API endpoints consumed, auth flow, WebSocket events, etc.
## Known Risks
Short list of uncertain areas that may require extra attention or spike work.
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 · 115 lines · 0 tokens per session scan A 91f6b849f3d5
plan is a skill published in the GitHub repository nelson820125/iforgeai (12 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,091 tokens. 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 skills, from other repositories
rowboat-spaces
How to work as a member of Rowboat Spaces, the team chat where people and agents work together. Load it once per session, before acting on any request that reaches you from Rowboat; it covers your setup, mentions and hand-offs, the tools, and files.
evolve
Research-driven multi-cycle improvement director. Forms causal hypotheses about why scores are low, validates them with scout agents before attacking, dispatches axis-parallel fleet attacks, extracts transferable patterns, and runs indefinitely within a budget envelope. Accumulates a persistent belief model and…
improve
Autonomous quality improvement loop. Scores a target against a rubric, selects the highest-leverage axis, attacks it, verifies, documents, and loops. No pre-planning between iterations — each loop re-scores from scratch.
watch
File sentinel that monitors the working directory for changes and marker comments, then auto-triggers appropriate skills. Poll-based via git diff against the last scan commit. Writes intake items for batch processing and routes marker actions through /do. Use for automatic reactions to file changes; do NOT use for…
pr-watch
Local PR watcher. Monitors CI status, automatically fixes failing checks by reading failure logs and applying targeted fixes, then optionally merges when all checks pass. Local CLI analog to Claude Code's cloud auto-fix feature.
qa
Browser-based QA verification. Launches a real browser, navigates the app, clicks buttons, fills forms, and tests user flows. Works as a standalone skill or as a phase end condition in campaigns. Requires Playwright (optional dependency, graceful skip if not installed).