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/sananthanarayan/skilldrop/reverse-architecturenpx skills add sananthanarayan/skilldrop --skill reverse-architecturegit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/reverse-architecture)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/reverse-architecture"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/reverse-architecture.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 | $0.00121 | $0.02129 |
| Opus 5 | $0.00060 | $0.01064 |
| Sonnet 5 | $0.00024 | $0.00426 |
| Haiku 4.5 | $0.00012 | $0.00213 |
Grade A, and why
reverse-architecture 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reverse-architecture
You help the user produce an accurate "as-is" architecture diagram by reading what's actually in the repo or infra files — not by asking them to describe the system from memory.
This skill pairs with two others:
architecture-diagrams— takes the written description this skill emits and renders the final notation. You can either hand off, or render inline using its templates.design-doc— when the as-is diagram is going into a design doc's "Current state" section.
How to respond
-
Find the highest-signal source files first. Most systems leak their architecture through 4–6 files. Read these in order, stopping when you have enough:
Source What you learn Where to look Infrastructure-as-code Cloud topology, account/region/VPC layout, managed services in use *.tf,*.tfvars,cdk/*,serverless.yml,template.yaml,*.bicep,pulumi/*,cloudformation/*.yamlKubernetes manifests / Helm Service-to-service topology, ingress, sidecars k8s/,manifests/,*.yamlwithkind: Deployment|Service|Ingress,helm/*/templates/,kustomization.yamldocker-compose Local/small-deploy service graph docker-compose.y*ml,compose.y*mlPackage manifest Stack, frameworks, key libraries (auth, ORM, queue clients) package.json,pyproject.toml,requirements.txt,go.mod,pom.xml,Gemfile,Cargo.tomlTop-level source structure Module / service boundaries, internal vs external API split src/,services/,apps/,cmd/,internal/API surface External contracts, request flows openapi.y*ml,*.proto,*Controller.*,routes/,handlers/,gql/,*.graphqlDatabase schema Data model, key entities, FK relationships migrations/,schema.sql,prisma/schema.prisma,models/,*.entity.tsConfig / env External dependencies (queues, caches, third-party APIs) .env.example,config/*.y*ml,application*.properties,settings.pyCI / deploy Deploy targets, environments, build artifacts .github/workflows/*,.gitlab-ci.yml,Jenkinsfile,Dockerfile,buildspec.ymlREADME Author's intent (verify against the code, don't trust uncritically) README*,ARCHITECTURE*,docs/architecture*
What ships with it
6 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.
- 4d ago First seen · 104 lines · 121 tokens per session scan A 799c9433fa72
reverse-architecture is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 20d ago), licensed MIT. It adds 121 tokens to every session and 2,129 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
openlore-brainstorm
Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-execute-refactor
Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.
openlore-plan-refactor
Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.
openlore-analyze-codebase
Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.
team-repair
Re-index OKF v0.2 index.md/log.md files, derive CDR.md, rebuild .skills.json and AGENTS.md in team-ai-directives, migrate v0.1→v0.2 frontmatter, scan for rule conflicts, and verify directive freshness. Use when indexes are inconsistent, orphans are detected, after bulk changes, or for periodic team AI directives…