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/yeaight7/agent-powerupsWrote 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/commands/yeaight7/agent-powerups/map-codebase)<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/map-codebase"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/map-codebase/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/commands/yeaight7/agent-powerups/map-codebase"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/map-codebase.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 | $0.00019 | $0.00787 |
| Opus 5.5 | $0.00008 | $0.00315 |
| Sonnet 5.5 | $0.00004 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00079 |
Grade A, and why
map-codebase 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Objective
Analyze existing codebase using parallel codebase-mapper agents to produce structured codebase documents.
Each mapper agent explores a focus area and writes documents directly to .planning/codebase/. The orchestrator only receives confirmations, keeping context usage minimal.
Output: .planning/codebase/ folder with up to 7 structured documents about the codebase state.
Flags
- --fast: Lightweight scan mode — spawns one mapper agent instead of four. Accepts an optional
--focusvalue:tech,arch,quality,concerns, ortech+arch(default). Faster and lower-context than the full map. - (no flag): Full parallel map — spawns 4 mapper agents to produce all 7 codebase documents.
Context
Arguments: $ARGUMENTS
Parse the first token of $ARGUMENTS:
- If it is
--fast: strip the flag, run lightweight scan (passing remaining args including optional --focus). - Otherwise: pass all of $ARGUMENTS as focus area hint to the full map workflow.
Load project state if exists:
Check for .planning/STATE.md — loads context if project is already initialized.
This command can run:
- Before
/new-project(brownfield codebases) — create codebase map first - After
/new-project(greenfield codebases) — update codebase map as code evolves - Anytime to refresh codebase understanding
When to Use
Use map-codebase for:
- Brownfield projects before initialization (understand existing code first)
- Refreshing codebase map after significant changes
- Onboarding to an unfamiliar codebase
- Before major refactoring (understand current state)
Skip map-codebase for:
- Greenfield projects with no code yet (nothing to map)
- Trivial codebases (fewer than 5 files)
Process
Full map (no flags):
- Check if
.planning/codebase/already exists — if yes, offer to refresh or skip. - Create
.planning/codebase/directory if it doesn't exist. - Spawn 4 parallel
codebase-mapperagents:- Agent 1:
techfocus → writesSTACK.md,INTEGRATIONS.md - Agent 2:
archfocus → writesARCHITECTURE.md,STRUCTURE.md - Agent 3:
qualityfocus → writesCONVENTIONS.md,TESTING.md - Agent 4:
concernsfocus → writesCONCERNS.md
- Agent 1:
- Wait for all agents to complete, collect confirmations (NOT document contents).
- Verify all 7 documents exist with line counts.
- Present summary and suggest next steps (typically:
/new-projector/plan-phase).
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 · 83 lines · 19 tokens per session scan A 54dc2ec19fe9
map-codebase is a command published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 19 tokens to every session and 787 once invoked, about $0.0001 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 commands, from other repositories
pn-distill
Remove content, sections, and features that don't earn their place — information architecture reduction. Use when there's too much stuff on the page. For visual decoration reduction, use pn-quieter.
gan-design
Run a generator/evaluator design loop for frontend or visual work with bounded iterations and scoring.
orchestrate
Legacy slash-entry shim for dmux-workflows and autonomous-agent-harness. Prefer the skills directly.
model-route
Recommend the best model tier for the current task based on complexity, risk, and budget.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.