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 agentmods add commands/nicoladevera/thinking-stack/succinctgit clone --depth 1 https://github.com/nicoladevera/thinking-stackWhat 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 | $0.00012 | $0.01063 |
| Opus 5 | $0.00006 | $0.00531 |
| Sonnet 5 | $0.00002 | $0.00213 |
| Haiku 4.5 | $0.00001 | $0.00106 |
Grade A, and why
succinct 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 yesterday.
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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Succinct — Compression to Executive Artifact
You are running a compression: taking the idea, proposal, or strategy in $ARGUMENTS and distilling it into a clean, executive-ready artifact. Your role is not to evaluate the idea — it's to make it land. You are enforcing compression discipline: one thesis, three load-bearing pillars, one tradeoff, one metric, one decision ask.
The user invoked this with: $ARGUMENTS
Phase 1: Intake & Readiness
Assess whether $ARGUMENTS gives you enough to compress meaningfully. You need: (1) a clear idea or proposal, (2) enough context to identify what's load-bearing vs. what's elaboration. You do not need a complete strategy doc — a few sentences of intent are sufficient.
If the input is too vague (no discernible claim, no domain, no direction):
Use AskUserQuestion to ask up to 3 targeted clarifying questions. Only ask what's actually missing — do not ask all 3 if 1-2 are already clear:
- What is the core idea or proposal you're trying to communicate?
- Who is the audience, and what do you need them to do after reading this?
- What's the desired outcome — a decision, alignment, funding, a green light?
If the input is ready: proceed directly to Phase 2.
Phase 2: Compression (Silent)
Before producing output, run the compression process internally. Do not show your work — produce the artifact directly.
Work through the following in order:
-
Find the thesis — strip the idea down to one sentence. Not a summary. Not a mission statement. The single claim the whole thing rests on.
-
Select three load-bearing pillars — identify the three reasons, facts, or arguments that most directly support the thesis. These are structural, not decorative. If there are five supporting points, pick the three without which the thesis collapses.
-
Name the tradeoff — every real proposal gives something up. Name it explicitly. If no tradeoff is visible, look harder — you are probably looking at a framing, not a decision.
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.
- yesterday First seen · 112 lines · 12 tokens per session scan A 7b1ccc68849a
succinct is a command published in the GitHub repository nicoladevera/thinking-stack (2 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 1,063 once invoked, about $0.0001 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 2026-08-31.
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adr
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routine
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tldr
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guide
Interactive guide to fellowship. Walks you through a real task using the structured research-plan-implement flow, then shows you what's next.