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 self-renewalgit 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/self-renewal)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/self-renewal"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/self-renewal/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/self-renewal"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/self-renewal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00115 | $0.02186 |
| Opus 5 | $0.00057 | $0.01093 |
| Sonnet 5 | $0.00023 | $0.00437 |
| Haiku 4.5 | $0.00012 | $0.00219 |
Grade A, and why
self-renewal 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Renewal
Overview
Self-renewal is the structured practice of identifying which mental models have passed their expiry date and genuinely updating — not merely annotating — them. It is not the same as learning new things: you can learn while remaining anchored to an obsolete core belief. The skill requires not addition but negation — actively denying what you previously held most confidently. Cross-skill composition: Use AFTER [metacognition] (diagnosis of which models are running); use BEFORE [first-principles] (cleared ground is required); use WITH circle-of-competence.
When to Use
- A strategy producing diminishing returns but the team keeps refining rather than questioning it
- A leader receives repeated feedback about a pattern that persists despite stated intention to change
- An organization is entering a new market or technology cycle that invalidates prior operating logic
- A team imports frameworks from past successes into situations where those frameworks are harmful
- A high performer is plateauing — the ceiling is the mental model, not the effort level
When NOT to use: Belief still valid / environment unchanged · Acute crisis · No psychological safety · The "outdated belief" is actually a core principle · Rationalizing discarding a good strategy because it's uncomfortable.
Coaching Novices (Adaptive Front Door)
Engine mode: user has a concrete case → run The Process directly. Coach mode: user is unfamiliar 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: "Self-renewal is finding your most confident beliefs and deliberately testing whether they still hold — not to destroy them, but to make sure they're earning their place."
- Check fit: "Is there a belief, habit, or strategy you've operated on for 2+ years that you've stopped questioning? Or feedback you've received more than twice that hasn't stuck?" If no, this skill may not fit right now.
- Elicit real case: "Tell me one thing you believe about your work / industry / leadership that you're very confident about. When did you last actively seek evidence against it?"
[WAIT — do not advance until user responds]
- One step at a time: Start with only Step 1 of The Process (inventory). Do not move to the experiment until the inventory surfaces something real.
[WAIT — do not advance until user responds]
- Close by naming the payoff: "In a changing environment, the person who updates fastest has a compounding advantage. Each belief you renew frees up processing capacity you were using to defend the old model."
[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 · 124 lines · 115 tokens per session scan A 3e4ef41f1eac
self-renewal is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 115 tokens to every session and 2,186 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 2026-09-03.
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