Borrowing it
Nothing to install: this file belongs to doncheli/don-cheli-sdd. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/doncheli/don-cheli-sdd/main/.agent/skills/doncheli-reasoning/SKILL.mdgit clone --depth 1 https://github.com/doncheli/don-cheli-sddWrote 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/doncheli/don-cheli-sdd/doncheli-reasoning)<a href="https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-reasoning"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-reasoning/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/doncheli/don-cheli-sdd/doncheli-reasoning"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-reasoning.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 | $0.00061 | $0.00594 |
| Opus 5 | $0.00030 | $0.00297 |
| Sonnet 5 | $0.00012 | $0.00119 |
| Haiku 4.5 | $0.00006 | $0.00059 |
Grade A, and why
doncheli-reasoning 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Don Cheli: Structured Reasoning Engine
Available Models
| ID | Best For |
|---|---|
| pre-mortem | Imagining failure before it happens; surfacing hidden risks |
| 5-whys | Root cause analysis of a known problem |
| pareto | Identifying the 20% of causes driving 80% of impact |
| inversion | Asking "what would guarantee failure?" to find blockers |
| second-order | Mapping downstream consequences of a decision |
| first-principles | Decomposing assumptions down to fundamental truths |
| minimize-regret | Choosing the option you will regret least in 10 years |
| opportunity-cost | Evaluating what you give up by choosing this option |
| circle-of-competence | Assessing whether you have the knowledge to decide well |
| map-territory | Separating your mental model from reality |
| probabilistic | Assigning probabilities to outcomes; expected-value calc |
| reversibility | Classifying decisions as one-way vs. two-way doors |
| rlm-chain-of-thought | Step-by-step explicit reasoning (RLM: PrimeIntellect style) |
| rlm-decomposition | Breaking a complex problem into atomic sub-problems (RLM) |
| rlm-verification | Self-checking the reasoning chain for contradictions (RLM) |
Instructions
- Read the problem or decision description
- If the user did not specify a model, suggest the 1–2 best-fit models with a one-line rationale; wait for confirmation or proceed with the top suggestion
- Apply the selected model(s) rigorously, showing all reasoning steps
- End with a clear recommendation or insight
Output Format
## Reasoning: <problem>
**Model applied:** <id> — <why this model fits>
### Analysis
<step-by-step application of the model>
### Insight / Recommendation
…
### Caveats
…
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 · 61 lines · 61 tokens per session scan A 64b4f39ac7a6
doncheli-reasoning is a skill published in the GitHub repository doncheli/don-cheli-sdd (57 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 61 tokens to every session and 594 once invoked, about $0.0003 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 2026-08-30.
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