From tech-snacks
Provides terse, opinionated, research-backed answers to tech/tooling/architecture questions using decomposed local and web research in a compressed caveman voice.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tech-snacks:caveman-searchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Research-backed answers in compressed caveman voice. Decompose research like Compound Engineering; render like Matt Pocock; always lead with the big truth.
Research-backed answers in compressed caveman voice. Decompose research like Compound Engineering; render like Matt Pocock; always lead with the big truth.
--verify).--verify (or --deep): run the adversarial verifier on each key recommendation.--hard: maximum voice compression (see voice spec).Inspect the question and current context:
Also read the audience tier from the question. DEFAULT to builder and bias toward simple — recommend the simplest tool that clears the bar, not the most rigorous one. Escalate to expert ONLY on explicit signals: "ML team", "production-grade", "at scale", "I already use X", named low-level libraries, researcher-depth framing. Signals like "assume X is solved, I just care about Y" or "I want to ship" mean: stay simple, give one clear path. Pass the tier to the web researcher in Step 3.
If repo mode ran:
caveman-repo-scout first (or as part of the parallel fan-out) and read its local_coverage.local_coverage >= 3 AND the topic is NOT high-risk (auth, payments, data migration, external API integration) -> answer from local research, skip web.Dispatch the selected agents in a SINGLE message so they run concurrently (Task tool, one call per agent):
tech-snacks:research:caveman-repo-scouttech-snacks:research:caveman-web-researcherPass each agent the topic (cleaned of flags). Pass the web researcher the audience tier from Step 1 so it surfaces the right depth of tools (simple/builder = batteries-included; expert = composable stacks ok).
Each caveman-web-researcher recommendation is already a self-contained claim. For each one, dispatch tech-snacks:research:caveman-verifier in parallel (one call per claim, single message). Reconcile:
holds -> keep, unmarked.weakened -> keep with the verifier's correction folded in.wrong -> drop it; if the verifier supplied a real answer, use that instead.Read these two files and follow them exactly:
references/voice-spec.md — the voice TRANSFORM (not a description — apply it). Big Truth First is mandatory.references/output-format.md — the section skeleton.The digest you got from the researchers is verbose. Do NOT echo its register. Transform it through the voice spec — drop articles/filler/hedging, force fragments, lead with the Big Truth. Then run the voice spec's pre-output check before sending: if sentences read full and smooth like a report, you drifted — rewrite them. Keep the researcher's per-job 2-3-option structure; do not re-expand it into a buffet.
Apply the Auto-Clarity Exception from the voice spec for any security warning, irreversible action, or ordered sequence.
Emit the response per the output skeleton: big truth -> tools -> shape (if architectural) -> bottom line -> sources (if web mode). Annotate corrected claims inline if --verify ran.
If research found nothing solid, say so terse and name the closest option plus its gap. Never invent confident-sounding tool names — in this voice a wrong claim reads as authoritative, which is the worst failure mode.
npx claudepluginhub ragnar-pwninskjold/tech-snacks --plugin tech-snacksResearches software-engineering questions with a STORM-style pipeline: auto-selects mode, spawns expert lenses, maps contradictions, synthesizes HTML briefing and ADR, then adversarially verifies claims against primary sources.
Conducts focused research investigations, converting questions into structured findings with confidence levels and source citations. Supports single-agent and parallel multi-angle modes.
Orchestrates multi-AI research across external providers (Gemini, Copilot, Ollama, etc.) using the Double Diamond Discover phase. Useful for broad exploration and gathering diverse perspectives.