resource-time-compression

resource-time-compression is a skill for Claude Code, Codex from deciqAI/knowledge-skills. It costs 139 tokens per session (2,078 once invoked), scanned A, original, MIT.

A planning approach for reaching a goal sooner by using existing capabilities, running tasks at the same time, and removing steps that do not need to be done internally. It treats time as something that can be shortened by assembling the right resources.

In plain words
What is it for?
Use it to compress product or company timelines, close capability gaps, and catch up when competitors are already ahead.
Why use it?
It prevents a slow, sequential plan from making a competitive opportunity disappear. It helps expose work that could be accelerated through partners, platforms, funding, or additional people.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to compress product or company timelines, close capability gaps, and catch up when competitors are already ahead.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/deciqai/knowledge-skills/resource-time-compression
Install

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.

Any agent
npx skills add deciqAI/knowledge-skills --skill resource-time-compression
Clone the repo
git clone --depth 1 https://github.com/deciqAI/knowledge-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for resource-time-compression

README.md
[![agentmods](https://agentmods.dev/badge/skills/deciqai/knowledge-skills/resource-time-compression/github.svg)](https://agentmods.dev/skills/deciqai/knowledge-skills/resource-time-compression)
Your own site
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/resource-time-compression"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/resource-time-compression/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.

agentmods 80×15 button for resource-time-compression

Your own site · 80×15
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/resource-time-compression"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/resource-time-compression.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 139 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,078 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00139 $0.02078
Opus 5 $0.00069 $0.01039
Sonnet 5 $0.00028 $0.00416
Haiku 4.5 $0.00014 $0.00208

Measured 9d ago against content hash 18ede388883e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

resource-time-compression 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 9d 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.

resource-time-compression/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Resource-Time Compression

Overview

Most plans estimate how long each step takes and add them up — producing a multi-year sequential path that reflects a solo, resource-constrained default. Resource-time compression redesigns that path: access capabilities that already exist rather than building them, run steps in parallel, and skip steps entirely using the right partners, capital, talent, or platforms. Arriving 3 years earlier than competitors is not merely a 3-year advantage — earlier arrival compounds through faster learning, stronger network effects, and earlier monetization.

Cross-skill composition: Use AFTER gap analysis. Use WITH [shi-momentum] (borrowing momentum phase). Use BEFORE [okr-goal-setting] — set goals after mapping what resources can realistically be assembled.


When to Use

Use when: default sequential path is too slow for the competitive window; competitors are ahead and sequential catch-up produces permanent second place; capability gaps would take 2+ years to build internally but external providers have them; capital available and the question is where to deploy it for time compression.

When NOT to use: bottleneck is a genuine sequential dependency that cannot be parallelized; required external resources do not yet exist; organization lacks bandwidth to integrate multiple external resources; "compression" is cover for a non-strategic acquisition or partnership.


Coaching Novices (Adaptive Front Door)

  • Engine mode: concrete case → run The Process directly.
  • Coach mode: user equates speed with effort or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

  1. What it is: "Resource-time compression redesigns the path to a goal using resources you don't have to build — arriving 2-3 years earlier than the default plan."
  2. Check fit: "Is there a goal mapped as a multi-year plan, with competitive pressure making the timeline too slow?" — If yes: applies.
  3. Elicit real case: "What are the 3 longest steps? For each: is there anyone who already has that capability?" > [WAIT — do not advance until user responds]
  4. Run The Process one step at a time starting with Step 1 — Default Path Mapping. > [WAIT — do not advance until user responds]
  5. Name the payoff: "Every year of earlier arrival compounds — market position, learning, and revenue against competitors still on the default path." > [WAIT — do not advance until user responds]

Read the full file on GitHub · 118 lines

Files

What ships with it

2 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.

Changes

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.

  1. 9d ago First seen · 118 lines · 139 tokens per session scan A 18ede388883e

Subscribe to this mod's changes

resource-time-compression is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 139 tokens to every session and 2,078 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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