aibot88 Triangulate
Verify a claim by checking 2+ independent sources, before committing to your first answer.
Why this exists
LLMs anchor on first instinct. When you produce output AND evaluate it, your evaluation runs on the same weights that produced it — same blind spots, same biases, same shortcuts. Self-review under those conditions is rubber-stamp review.
The mature move is to fork the question at specific trigger points: route it to a different model (different weights), a fresh subagent (different context), or external docs (different epistemology) BEFORE committing.
This skill is the discipline to know when to fork and what counts as an independent source.
Trigger points — fire AUTOMATICALLY when ANY hold
1. Compliance, legal, regulatory, or safety-critical copy
SEC/FINRA disclosures, OBA-walled bios, privacy policies, terms of service, medical/financial advice, liability-relevant language, public bios that will be cross-checked against authoritative records (BrokerCheck, court records, Form ADV).
Why: asymmetric cost. One bad sentence = regulator screenshot, reviewer rejection, lawsuit, BrokerCheck-falsifiable claim. Self-review here is malpractice.
Action: invoke second-opinion (Codex + Gemini cross-model review) OR spawn a fresh-context code-reviewer subagent with the SPECIFIC compliance constraints listed. NEVER skip.
2. Architectural decision with a viable alternative pattern
"Should I use SSE streaming or polling?" / "Class component or hook?" / "Fixed-duration cinematic or real progress events?" / "Server-rendered or client-rendered?" Whenever there's a genuinely real alternative, that alternative's existence IS the trigger.
Why: if the alternative exists, your first instinct may be the inferior instinct. The 12-seconds-of-animation-theater vs SSE-streaming-real-progress class of mistake.
Action: explicitly surface to the user: "I'm leaning toward A. Viable alternative B exists. Want me to spawn a planning agent / Codex session with the same problem statement and compare?" Don't ship the first design just because it's first.
3. Image / video / creative prompt engineering
Grok Imagine prompts, nano-banana prompts, hero images, brand assets, video generation. Anywhere prompt-engineering = search in latent space.
Why: one prompt is one sample. Variety produces better results.
Action: spawn 2-3 parallel agents with DIFFERENT prompt strategies (constraint-heavy / evocative / technical). Pick the winner. Cost: a few cents. Savings: hours of iteration.
4. Debug loop > 2 cycles on the same problem
Same test failing with marginally different errors after 3+ retries. Same UI bug surviving 3+ attempted fixes. Same deploy failing for unclear reasons.
Why: when you're stuck, the bias is to retry with marginal variation. Tunnel vision. Fresh context breaks it. Different weights break shared blind spots.
Action: spawn a fresh agent with a CLEAN problem statement (no debug history). Or invoke second-opinion to run the same problem through Codex / Gemini. Different inductive biases catch what same-model retries miss.
5. Cross-cutting decision affecting multiple stakeholders
API contract changes, schema migrations, brand-pixel decisions, hiring decisions surfaced as architectural choices, anything where multiple owners have legitimate input.
Why: consensus illusion. The agent who proposed the change can't see impact across all stakeholders.
Action: dispatch perspective-specific subagents (one per stakeholder concern) and synthesize. This is adversarial-reviewer for non-code surfaces.
6. Verifying async state changes after a triggering action
Deploys, publishes, DNS changes, cache invalidations, webhook propagation, replicated database writes — anywhere you triggered something and want to confirm "did it happen?"
Why: async systems have eventual consistency, not synchronous. Polling at T+5 seconds when propagation takes 2 minutes returns stale data, which produces wrong conclusions, which produce wrong actions. "Sometimes you have to go slow to go fast."
Action: before checking, estimate the system's propagation window. WAIT that window before drawing conclusions. If you must check early, label the result PROVISIONAL.
Reference table — typical propagation windows:
| Operation | First-check | Full-confidence |
|---|---|---|
| Vercel production deploy | 90-120s | 3-5 min |
| Vercel CDN edge invalidation | 60-120s | 2-3 min |
| GitHub deployments API record | 30-60s | 90s |
| npm registry availability | 30-60s | 90s |
| DNS TTL propagation | 60-300s | 5-15 min |
| Supabase RLS / migration | <1s | 5s |
| Database read replica lag | 100ms-1s | 2s |
| Stripe webhook delivery | 1-30s | 60s |
| Email delivery (transactional) | 30s-5min | 15 min |
The cache-buster trap: appending ?cb=... does NOT defeat server-side static-page generation. If X-Vercel-Cache: HIT after a cache-buster, the page is being served from a build that hasn't regenerated yet. Wait, don't keep busting.
Anti-pattern: polling in a tight loop to "race" propagation. That hits rate limits, produces false-failure conclusions, and burns trust with the user when you say "didn't work" before the system finished.
What counts as an "outside source"
Listed in increasing distance from your weights:
| Source | When to use | Tradeoff |
|---|---|---|
| External documentation / source code | API behavior, library specifics | Authoritative; settles vs. plausibly-hallucinated |
| Different model (Codex, Gemini, GPT) | Architectural alternatives, debug loops, prompt variety | Different inductive biases; orthogonal blind spots. The strongest fork. |
| Fresh subagent (same model, no context) | Compliance copy review, code review on isolated diff | Same weights but fresh context breaks anchoring on conversation |
| Different agent role / persona | Adversarial review, multi-perspective synthesis | Persona shift forces different priors |
| The user | Genuinely ambiguous design questions | Highest cost (their time), highest signal — reserve for actual ambiguity |
Principle: the further from your weights, the more independent the source.
A fresh Claude subagent ≠ a different model. Both have value, but they're not interchangeable. For genuinely cross-cutting decisions, prefer different-model. Codex + Gemini are 2 prompts away via second-opinion.
The maturity test
At any decision point, ask:
"Would another model — running the same prompt with no context from this conversation — produce a meaningfully different answer here?"
- Yes → fork the question. Use one of the actions above.
- No → ship.
This is the gating question. It costs 2 seconds. It catches the class of mistake where shipping the first answer feels productive but produces inferior output.
How to invoke (concrete actions)
| Trigger | Tool / Pattern |
|---|---|
| Compliance copy review | Skill: second-opinion with focus="general", scope="branch diff" |
| Code review before merge | Skill: adversarial-reviewer OR Skill: second-opinion (Codex+Gemini) |
| Architectural fork | Agent tool, subagent_type: feature-dev:code-architect with the same problem statement, no context from this conversation |
| Image prompt variety | 2-3 parallel Agent calls, each with a different prompt-engineering strategy |
| Debug loop break | Fresh Agent call with isolated problem statement, OR second-opinion for cross-model debug |
| Cross-cutting decision | Skill: dispatching-parallel-agents (one agent per stakeholder concern) |
Anti-patterns
- Forking on routine work — CSS edits, copy tweaks, dep bumps, single-line fixes. Reserved for trigger points above.
- Rubber-stamping outside review — if a different model disagrees with your approach, that disagreement is load-bearing input. Investigate, don't dismiss.
- Menu-padding — don't surface "want me to check with X?" as filler. Only when there's genuine value at stake.
- Outsourcing the decision — outside review is INPUT to your judgment, not a replacement. You synthesize.
- Forking after the user said ship — outside review BEFORE the ship decision; once shipping, ship.
- Over-forking on green-light architecture — if you're in a happy path with no viable alternative, no fork needed.
Origin — the moment that taught this skill
Real moment, Teranode AI project, 2026-04-29.
Built a 12-second fixed-duration cinematic for the reliability-graph Council run. Medallion pulses on each role for 2.4s in sequence, results show. Looked good. Shipped.
In parallel, a Codex session on the same code arrived at a different answer: replace the fixed cinematic with Server-Sent Events from the actual Council run. Pulse the medallion on the role that's actually running.
Codex's pattern was materially better. The fixed-duration cinematic was animation theater — the medallion lying about what was happening. SSE made it honest.
The fork-the-question moment I (Claude) missed: at the point of designing the timing model, there was a viable alternative (real progress events). Trigger #2 fired. I didn't fork. Codex did the work I should have done myself, and the resulting code was better.
The lesson: the cost of the 30-second pause to fork the question is much lower than the cost of shipping the inferior pattern.
Dan Zimon (founder, Teranode AI) named this skill that night. The pattern Teranode ships at the product level — five independent reasoning roles forking advisory questions — is the same pattern this skill ships at the developer level. Same principle, two scales.
Cross-references
second-opinion(trailofbits/skills) — when the trigger is "code-review before merge" → cross-model fork via Codex CLI + Gemini CLI. Triangulate is the meta-skill that fires it.adversarial-reviewer(engineering-team) — when the trigger is "code review with persona shift" → 3 hostile personas. Triangulate fires it on code; it doesn't cover non-code surfaces.dispatching-parallel-agents(superpowers) — when the fork produces 3+ independent agents → this skill governs how to dispatch them.brainstorming(superpowers) — when the trigger is "we don't yet know the design space" → brainstorm-first, then triangulate-on-finalists.
See also
references/teranode-routing-layer.md— strategic note on how this principle scales into a learning routing layer at the product level. Teranode AI productizes the pattern: which model excels at which question, learned from every API call.
License
MIT. Free to use, fork, modify, redistribute. Attribution appreciated but not required.