agentmods.dev

triangulate-me

triangulate-me is a skill for Codex from jon-devlapaz/tink-skills. It costs 58 tokens per session (988 once invoked), scanned A, original, MIT.

A tool for examining a claim through three readings: a faithful interpretation, a strengthened interpretation, and a stress test. It then identifies the central issue and shows where the evidence supports or weakens agreement.

In plain words
What is it for?
Use it to pressure-test interpretations, clarify the main decision or assumption, and compare plausible readings of evidence-sensitive claims.
Why use it?
It helps separate what someone actually said from stronger assumptions, expose the key disagreement, and reach a conclusion without pretending that unsupported views are equally sound.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 13repo +1 changed 4d ago A scan SkillSpector: pass 58 tokens original MIT

Good fit Use it to pressure-test interpretations, clarify the main decision or assumption, and compare plausible readings of evidence-sensitive claims.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jon-devlapaz/tink-skills/triangulate-me
View source ↗ jon-devlapaz/tink-skills
Install

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.

Any agent
npx skills add jon-devlapaz/tink-skills --skill triangulate-me
Clone the repo
git clone --depth 1 https://github.com/jon-devlapaz/tink-skills

Made for: Codex.

Wrote 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.

agentmods badge for triangulate-me

README.md
[![agentmods](https://agentmods.dev/badge/skills/jon-devlapaz/tink-skills/triangulate-me/github.svg)](https://agentmods.dev/skills/jon-devlapaz/tink-skills/triangulate-me)
Your own site
<a href="https://agentmods.dev/skills/jon-devlapaz/tink-skills/triangulate-me"><img src="https://agentmods.dev/badge/skills/jon-devlapaz/tink-skills/triangulate-me/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.

agentmods 80×15 button for triangulate-me

Your own site · 80×15
<a href="https://agentmods.dev/skills/jon-devlapaz/tink-skills/triangulate-me"><img src="https://agentmods.dev/badge/skills/jon-devlapaz/tink-skills/triangulate-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 988 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.00988
Opus 5 $0.00029 $0.00494
Sonnet 5 $0.00012 $0.00198
Haiku 4.5 $0.00006 $0.00099

Measured 4d ago against content hash 05470b7c7cb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

triangulate-me 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 4d 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.

skills/triangulate-me/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Triangulate Me

Sharpen the user's meaning without replacing it. Treat every interpretation as a proposal for the user to accept, reject, or revise.

Invariant:

  • Ground every interpretation in the user's expressed language and context; exclude unstated motives, emotions, or psychological speculation.
  • Weight reads by evidence; empirical measurements and concrete harm barriers strictly outweigh aesthetic preference, precedent, or unverified assertion.
  • Avoid false compromise; endorse well-supported positions directly, revise both sides, or preserve genuine value conflicts without manufacturing midpoint compromises or artificial objections.
  • Confine actions to analysis and diagnostics; wait for explicit user approval before planning, code modification, or execution.

1. Triage and isolate the claim

  • Fast-path: If the input is an acknowledgement, logistics, or has no substantive interpretive gap, respond directly and stop without running the frame.
  • Isolate the single active claim, choice, or assumption doing the work. Split independent claims and handle them separately.
  • Inspect accessible facts autonomously; ask the user only for uninspectable context, judgments, or private preferences.

Complete when: Exactly one active claim, choice, or assumption is isolated, or a direct fast-path response is emitted.

2. Differentiate interpretations

  1. Faithful read — Restate only what the answer explicitly commits to, keeping every meaning-changing qualifier intact.
  2. Steel read — Construct the strongest coherent interpretation supported by context; make any agent-added premise explicit and labeled.
  3. Stress read — Name the weakest plausible vulnerability supported by the answer, or state that none exists.

Load references/reasoning-foundations.md to calibrate charity, adversarial plausibility, and differentiation.

Complete when: All three reads are bounded by stated evidence and agent premises are explicitly labeled.

Read the full file on GitHub · 73 lines

Files

What ships with it

21 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.

Changes

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.

  1. 4d ago Changed · -54 lines · -2 tokens per session 05470b7c7cb0
  2. 8d ago First seen · 127 lines · 60 tokens per session scan A a4051dc92a06

Subscribe to this mod's changes

triangulate-me is a skill published in the GitHub repository jon-devlapaz/tink-skills (13 stars, last pushed 7d ago), licensed MIT. It adds 58 tokens to every session and 988 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.

Research
Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens