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 skills/tupe12334/instinct/smartnpx skills add tupe12334/instinct --skill smartgit clone --depth 1 https://github.com/tupe12334/instinctWhat 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 | $0.00019 | $0.01642 |
| Opus 5 | $0.00010 | $0.00821 |
| Sonnet 5 | $0.00004 | $0.00328 |
| Haiku 4.5 | $0.00002 | $0.00164 |
Grade A, and why
smart 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
smart Goals
Overview
smart is a criteria checklist for well-formed goals. A goal passing all 5 criteria is actionable, trackable, and achievable.
| Letter | Criterion | Question to test |
|---|---|---|
| S | Specific | What exactly will be accomplished? Who is involved? Where? |
| M | Measurable | How will you know when it's achieved? What metrics? |
| A | Achievable | Is it realistic given your resources and constraints? |
| R | Relevant | Does it align with broader objectives? Does it matter now? |
| T | Time-bound | By what date? What are the interim milestones? |
Criterion Breakdowns
S — Specific
Vague goals produce vague results. A specific goal answers:
- What needs to be accomplished?
- Who is responsible?
- Where does it happen?
- What constraints or conditions apply?
Bad: "Improve sales" Good: "Increase monthly recurring revenue from existing customers in the Enterprise segment"
M — Measurable
You can't manage what you can't measure. Measurable goals have:
- A numeric target or binary completion state
- A defined baseline (where you start from)
- Leading indicators you can track weekly, not just a lagging outcome
Bad: "Get more users" Good: "Reach 500 active users (up from 200 current) measured by weekly logins"
A — Achievable
Challenging but within reach. Test:
- Do you have or can you acquire the resources required?
- Have others achieved something comparable?
- What's the historical rate of progress on this metric?
Bad: "10× revenue in 30 days" (if not backed by a concrete plan) Good: "25% revenue growth this quarter, based on pipeline size and historical close rate"
R — Relevant
Aligned with what actually matters. Ask:
- Does this goal serve your top-level mission or strategy?
- Is this the right time for this goal?
- Does it conflict with other priorities?
Bad: "Optimize the onboarding flow" when the company has a retention crisis Good: "Increase 30-day retention rate, the primary driver of LTV"
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 · 155 lines · 19 tokens per session scan A eebea63ec56f
smart is a skill published in the GitHub repository tupe12334/instinct (1 stars, last pushed 16d ago), licensed MIT. It adds 19 tokens to every session and 1,642 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.
Other skills, from other repositories
thinking-model-router
When unsure which thinking skill fits, map domain and problem type, then return NONE or one primary skill by default (at most three complementary).
thinking-opportunity-cost
Before committing scarce time, people, or money, name the best forgone use of those resources and the value delta of the chosen path versus that alternative.
thinking-red-team
For authorized security review of code, auth, or APIs you control, model the attacker, map the attack surface, and report only findings with a reproducible exploit path and verified mitigation.
thinking-scientific-method
When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
thinking-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
thinking-circle-of-competence
Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.