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 agentmods add skills/melodic-software/claude-code-plugins/wayfindnpx skills add melodic-software/claude-code-plugins --skill wayfindgit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWrote 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/melodic-software/claude-code-plugins/wayfind)<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/wayfind"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/wayfind.svg" alt="Measured on agentmods" 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 | $0.00140 | $0.03525 |
| Opus 5 | $0.00070 | $0.01762 |
| Sonnet 5 | $0.00028 | $0.00705 |
| Haiku 4.5 | $0.00014 | $0.00352 |
Grade A, and why
wayfind scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-computed context
Current user: !gh api user --jq '.login' 2>/dev/null || echo "unknown"
Open maps: !gh issue list --label "$(l=$(jq -r '.config.container_label | if type=="string" then . else "" end' .work-item-tracker.json 2>/dev/null); echo "${l:-work-map}")" --state open --json number,title --jq '.[] | "#\(.number) \(.title)"' 2>/dev/null || echo "none"
Variables
Arguments: $ARGUMENTS
Purpose
Some efforts are too big to hold at once AND too foggy to ticket. You can't yet
phrase half the questions, let alone answer them. /planning:interview needs a coherent task;
/planning:plan needs a coherent plan; both presuppose you already know what you're deciding.
/planning:wayfind sits upstream of all of them: it turns a too-big-foggy effort into a shared
decision map on the work-item tracker, then works that map's frontier one decision at a
time until the fog burns off and a real destination (Brief / PRD / PLAN) can be handed onward.
Plan, don't do. A map holds decisions, not build work. Each decision item, once
resolved, either sharpens the map or graduates to the destination. The moment the destination
is coherent, the map closes and the normal pipeline (/planning:interview → /planning:design → /planning:plan → /implementation:implement) takes over. The map persists as native tracker primitives, each decision routes
to a first-party skill, and execution artifacts live in <memory_dir>/<slug>/ (default
.work/). The topic-docs convention's memory tier, slug spec and all (see
${CLAUDE_PLUGIN_ROOT}/reference/topic-docs.md)
never in the map itself.
Two modes. chart builds or extends a map (interactive only). work picks one item off
the map's frontier and drives it to resolution. The default action auto-detects: an existing
open map for the topic → work; nothing yet → chart.
The fog test (the one call that governs everything)
For every uncertainty, ask: can I phrase it as a sharp question?
What ships with it
3 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.
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.
- yesterday First seen · 209 lines · 140 tokens per session scan A c1ade3b596e1
wayfind is a skill published in the GitHub repository melodic-software/claude-code-plugins (15 stars, last pushed today), licensed MIT. It adds 140 tokens to every session and 3,525 once invoked, about $0.0007 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 2026-09-03.
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parallel-orchestrator
Manage parallel Claude Code workstreams using git worktrees. Use when: splitting large tasks across multiple workers, coordinating parallel development, monitoring worker progress, integrating completed work, analyzing work item documents (code reviews, issue lists). Triggers: parallel, orchestrator, worktrees…
parallel-worker
Execute focused implementation tasks in a parallel workflow. Use when: working on assigned files in a worktree, making checkpoint commits, signaling dependencies or blockers, completing orchestrator-assigned tasks. Triggers: worker, checkpoint, worktree, assigned scope, commit prefix, parallel task.
build-priority-queue
For ordered processing: A search, Dijkstra, event simulation, task scheduling. Efficient min/max extraction with heap-based queue.
catch-expected-errors
For iteration with errors: catch exceptions during exploration, skip invalid cases, continue to next attempt.
compose-small-helpers
For complex behavior: build from tiny functions, chain transformations, make code read like a pipeline of operations.
count-combinations
For probability and counting: permutations, combinations, sample spaces, Monte Carlo simulation, brute-force enumeration, card/dice problems.