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/wellapp-ai/well/dependency-mappingnpx skills add WellApp-ai/Well --skill dependency-mappinggit clone --depth 1 https://github.com/WellApp-ai/WellWrote 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/wellapp-ai/well/dependency-mapping)<a href="https://agentmods.dev/skills/wellapp-ai/well/dependency-mapping"><img src="https://agentmods.dev/badge/skills/wellapp-ai/well/dependency-mapping.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 | $0.00014 | $0.01154 |
| Opus 5 | $0.00007 | $0.00577 |
| Sonnet 5 | $0.00003 | $0.00231 |
| Haiku 4.5 | $0.00001 | $0.00115 |
Grade A, and why
dependency-mapping 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 4d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Mapping Skill
Map dependencies between implementation slices using Design Structure Matrix (DSM), calculate risk scores, and recommend implementation sequence.
When to Use
- During Ask mode Phase 2 (CONVERGE)
- When planning multi-slice features
- Before phasing to understand risk order
Instructions
Phase 1: Build DSM Matrix
Create a square matrix with slices on both axes. Mark dependencies with *:
| #1.1 | #1.2 | #2.1 | #2.2 | #2.3 | #3.1 |
---------+------+------+------+------+------+------+
#1.1 | - | | | | | |
#1.2 | * | - | | | | |
#2.1 | | * | - | | | |
#2.2 | | | * | - | | * |
#2.3 | | * | * | | - | |
#3.1 | | | | | | - |
Legend: * = row depends on column
Reading: Row #2.2 has * in columns #2.1 and #3.1 = #2.2 depends on #2.1 AND #3.1
Phase 2: Calculate Dependency Score
For each slice, count:
| Metric | Formula | Meaning |
|---|---|---|
| Fan-in | How many slices depend ON this? | High = blocker, ship early |
| Fan-out | How many slices does this DEPEND on? | High = risky, ship later |
| Dependency Score | Fan-out count | Lower = safer |
Phase 3: Calculate Leverage Score
Score each slice on reuse of existing patterns:
| Level | Score | Description |
|---|---|---|
| Full Reuse | 0 | Uses existing component from design system/Storybook as-is |
| Extend | 1 | Extends existing component with new props/variants |
| Compose | 2 | Composes multiple existing components |
| New Pattern | 3 | Creates new component following design system tokens |
| New System | 5 | Requires new patterns not in design system |
Check these sources before scoring:
/docs/design-system/components.md- Existing componentsGlob **/*.stories.tsx- Storybook patternsSemanticSearchfor similar implementations in codebase
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.
- 4d ago First seen · 138 lines · 14 tokens per session scan A e008baabd7f2
dependency-mapping is a skill published in the GitHub repository WellApp-ai/Well (339 stars, last pushed 27d ago), licensed MIT. It adds 14 tokens to every session and 1,154 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
auto-reader-ocr
Extract text from images/PDFs (Arabic-first, manga-aware Japanese, 13+ languages auto-detected), translate between 100+ languages, and pull structured fields from invoices/receipts/IDs via the Auto-Reader OCR API. Use when the user shares a document image or PDF to read, translate, or extract data from — or asks for…
nextjs-pages-router
Set up tRPC in Next.js Pages Router with createNextApiHandler, createTRPCNext, withTRPC HOC, SSR via ssr option and ssrPrepass, SSG via createServerSideHelpers with getStaticProps, and server-side helpers for getServerSideProps prefetching.
react-query-setup
Set up @trpc/tanstack-react-query with createTRPCContext(), TRPCProvider, useTRPC() hook, queryOptions/mutationOptions factories, query invalidation via queryClient.invalidateQueries with queryFilter, and type inference with inferInput/inferOutput.
non-json-content-types
Handle FormData, file uploads, Blob, Uint8Array, and ReadableStream inputs in tRPC mutations. Use octetInputParser from @trpc/server/http for binary data. Route non-JSON requests with splitLink and isNonJsonSerializable() from @trpc/client. FormData and binary inputs only work with mutations (POST).
openapi-glossary
Use consistent OpenAPI terminology and definitions when writing documentation, educational material, and tooling guidance.
scalar-docs
Skill for writing and updating scalar.config.json — Scalar Docs configuration reference for users and LLMs.