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 product-on-purpose/thinking-framework-skills --skill think-issue-treegit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-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/product-on-purpose/thinking-framework-skills/think-issue-tree)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-issue-tree"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-issue-tree/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/product-on-purpose/thinking-framework-skills/think-issue-tree"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-issue-tree.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 | $0.00145 | $0.01459 |
| Opus 5 | $0.00072 | $0.00730 |
| Sonnet 5 | $0.00029 | $0.00292 |
| Haiku 4.5 | $0.00015 | $0.00146 |
Grade A, and why
think-issue-tree 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Tree
A big question like "why are sales down?" cannot be answered as posed; it has to be broken into parts. An issue tree decomposes one question top-down into a structured set of sub-questions, recursively, until the leaves are small enough to answer directly. The load-bearing constraint is MECE: at every branch the children must be Mutually Exclusive (no two overlap) and Collectively Exhaustive (together they cover the whole parent, with nothing important left outside). That discipline forces coverage, prevents double-counting, and makes the decomposition inspectable - a reader can challenge one branch instead of arguing a wall of prose. The output is an issue tree, not a discussion, and it restructures the question rather than answering it.
When to Use
- A question is too broad, ambiguous, or multi-cause to answer as posed ("why is churn rising?", "where is our margin leaking?", "should we launch a free tier?").
- Analysis must be split so work can be parallelized or prioritized across non-overlapping branches.
- Coverage matters: missing a whole category of cause or option would be costly, so collective-exhaustiveness has real value.
- Early in a diagnosis or strategy workflow, to turn an unanswerable prompt into a tractable set of answerable parts.
When NOT to Use
- The question is simple or already has an obvious structure. Decomposing a one-step question into a tree is overhead and false rigor.
- To evaluate whether a given argument or recommendation is sound. That is reasoning over an answer that already exists - use
think-argument-mapping. An issue tree decomposes a question top-down before any answer exists; an argument map lays out the support and objections for an answer already on the table. - To organize existing notes, findings, or observations bottom-up into themes. That is clustering from the data - use
think-affinity-mapping. An issue tree imposes a top-down split before gathering; affinity mapping discovers structure from what is already gathered. - As the answer. A clean tree restructures the question; it does not resolve it. Stopping at a pretty tree without driving the leaves to data or judgment is the central misuse.
What ships with it
5 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 · 66 lines · 145 tokens per session scan A c11019f8a23b
think-issue-tree is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed 23d ago), licensed Apache-2.0. It adds 145 tokens to every session and 1,459 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 2026-08-30.
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