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 deciqAI/knowledge-skills --skill resource-time-compressiongit clone --depth 1 https://github.com/deciqAI/knowledge-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/deciqai/knowledge-skills/resource-time-compression)<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.
<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>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 | $0.00139 | $0.02078 |
| Opus 5 | $0.00069 | $0.01039 |
| Sonnet 5 | $0.00028 | $0.00416 |
| Haiku 4.5 | $0.00014 | $0.00208 |
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.
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.
- 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."
- Check fit: "Is there a goal mapped as a multi-year plan, with competitive pressure making the timeline too slow?" — If yes: applies.
- 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]
- Run The Process one step at a time starting with Step 1 — Default Path Mapping. > [WAIT — do not advance until user responds]
- 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]
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.
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.
- 9d ago First seen · 118 lines · 139 tokens per session scan A 18ede388883e
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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