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 Kshitijpalsinghtomar/depth-skills --skill ds-invertgit clone --depth 1 https://github.com/Kshitijpalsinghtomar/depth-skillsWrote 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/kshitijpalsinghtomar/depth-skills/ds-invert)<a href="https://agentmods.dev/skills/kshitijpalsinghtomar/depth-skills/ds-invert"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-invert/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/kshitijpalsinghtomar/depth-skills/ds-invert"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-invert.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 | $0.00020 | $0.01553 |
| Opus 5 | $0.00010 | $0.00776 |
| Sonnet 5 | $0.00004 | $0.00311 |
| Haiku 4.5 | $0.00002 | $0.00155 |
Grade A, and why
invert 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 11d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
INVERT — Constraint & Belief Inversion
Two forces trap answers in small spaces: false constraints (walls that aren't walls) and untested beliefs (foundations that might not be solid). Both feel real from inside. Both can collapse when tested.
This skill flips things. It inverts constraints to find which are real and which are habits. It inverts beliefs to find which are load-bearing and which are replaceable. Then it solves the problem from the opposite world — to discover what only becomes visible from the other side.
Part 1: Constraint Inversion
Step 1 — WRITE THE CONSTRAINT MAP
List every constraint on THIS problem. For each, classify its type:
CONSTRAINT MAP
────────────────────────────────────────
C1: [constraint statement]
Type: [physical / legal / economic / policy / habit / assumption]
Source: [who stated this? user / me / industry convention / nobody-explicit]
Hardness:[hard — cannot violate / soft — could be negotiated or removed]
C2: [constraint statement]
Type: [type]
Source: [source]
Hardness:[hard / soft]
...
Bottleneck constraint: C[N] — [this is the one most limiting the solution space]
────────────────────────────────────────
Type definitions:
- Physical — speed of light, conservation laws. Cannot violate.
- Legal — regulations, contracts, compliance. Cannot violate (but can sometimes work around).
- Economic — money, resources, time. Can be traded.
- Policy — internal rules, team decisions. Can be renegotiated.
- Habit — "we always do it this way." Usually invertible without consequence.
- Assumption — inherited from pattern matching. Unknown truth value. Most dangerous type.
Key insight: Constraints labeled "physical" are sometimes misclassified "policy" or "habit." If you cannot explain WHY this constraint cannot be violated in terms of physics or law — it's probably softer than you think.
Artifact: The constraint map with types and bottleneck identified. Step 2 inverts the bottleneck.
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.
- 11d ago First seen · 186 lines · 20 tokens per session scan A 1a132b3b3a4b
invert is a skill published in the GitHub repository Kshitijpalsinghtomar/depth-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 1,553 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 2026-08-31.
Other skills, from other repositories
vox-video-director
Turn ONE topic into a finished Vox-style paper-collage explainer / ad video, end to end with Aliyun Bailian CLI + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Use this whenever the user wants a "Vox style" video, a paper/torn-paper collage animation, a "motion collage"…
bailian-train-deploy
A workflow for using Alibaba Cloud’s Bailian command-line tool to fine-tune or directly deploy AI models as callable services. It covers text, speech-synthesis, image-generation, and video-generation models.
api-database-mongodb
Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions.
ai-infrastructure-ollama
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint.
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
ai-provider-cohere-sdk
Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection.