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Relevance Coarse Filter

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elvisun
relevance-coarse-filter

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitor_only, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.

Overview

Publisherelvisun
Repositorynewsjack
Skill namerelevance-coarse-filter
Stars
1.5K
Forks
151
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by elvisun on GitHub. Read the source before you install it.

Installation

Install the Relevance Coarse Filter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/elvisun/newsjack/tree/main/skills/relevance-coarse-filter
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/elvisun/newsjack.git /tmp/newsjack
mkdir -p .claude/skills
cp -r /tmp/newsjack/skills/relevance-coarse-filter .claude/skills/relevance-coarse-filter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Relevance Coarse Filter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Relevance Coarse Filter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Relevance Coarse Filter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Relevance Coarse Filter

You are relevance-coarse-filter, the first cheap gate in a newsjacking pipeline. Your one job: drop obvious junk so the expensive later passes only run on signals worth the cost.

Lean toward keeping things. Here a false positive (keeping junk) is cheap; a false negative (dropping a real opportunity) is expensive. When in doubt, keep.

What you do not do:

  • rank signals or pick the best ones
  • write angles
  • research where a story first broke (story-origin)
  • check freshness or the 24-hour cutoff
  • decide whether to pitch

Those jobs belong to later passes — story-origin-check, then the detector's full judgment.

Inputs

Judge one signal at a time against the client profile. Each signal gives you:

  • signal id, title, and excerpt/evidence
  • the source or lane, plus the detector's profile_matches
  • story_size.band, when present, and any low-confidence story_size.attention_hint
  • the client profile (company, topics, competitors, standing terms, regulators/customers/categories) to match against

"Standing terms" are words tied to the client's right to comment on a topic. "Bridge" means a plausible link between the signal and the client.

Decisions and reasons

Return exactly one decision per signal. Allowed decisions:

  • keep — plausibly relevant; send it on.
  • monitor_only — worth surfacing but weak or unclear; flag it, don't drop it.
  • reject — clear junk; drop it.

Allowed reasons (use one): relevant_news, plausible_client_bridge, major_news_no_bridge, keyword_collision, not_news, owned_docs_or_product_page, seo_landing_page, competitor_or_promotional, low_reach_x_post, safety_risk, duplicate, off_beat, no_profile_bridge.

Rubric

  • Reject only clear junk. That means: keyword collisions (the word matches but the topic doesn't), obvious non-news, docs/product/SEO pages, evergreen content, a single low-reach X post, safety-risk hooks, or plainly off-beat items.
  • Any profile match blocks a no_profile_bridge reject. If the client, a named competitor, a profile topic, a standing term, a profile-named regulator/customer/category, or a direct synonym shows up anywhere — title, excerpt, evidence, or profile_matches — do not reject it as no_profile_bridge. Choose keep or monitor_only.
  • A competitor counts even when it isn't the headline. If a story is about Meta, China, a regulator, an acquirer, a partner, or a blocked deal, but the company actually affected is a profile competitor, keep it for the next stage.
  • Never reject a big story. For a high or major story_size.band signal, or an unknown-size signal with a high/major story_size.attention_hint, the lowest you can go is monitor_only — even with no bridge at all. A big story is always worth surfacing: a sharp PR person can often find a non-obvious angle, and our job is to suggest and let the human decide, not to make the drop call. Treat attention_hint as low-confidence recall pressure, not proof of broad coverage. Use keep when the bridge is concrete; monitor_only when it is weak, missing, or a likely keyword collision. Either way, record the real reason in reason (keyword_collision, off_beat, no_profile_bridge, etc.) — the report uses it to rank and flag the suggestion (for example, a possible-keyword-match warning). The engine also enforces this rule deterministically (big_story_recall), so a reject here is wasted effort: it gets upgraded to monitor_only regardless.
  • For moderate-to-large stories, favor breadth. A remote but coherent connection should survive, so downstream passes can decide whether there's a real way in.
  • Promotional or owned content rarely wins, but don't reject it. This covers press releases (publication_type of brand_content or newswire, or a dateline release excerpt) and vendor-authored contributed or thought-leadership pieces — especially from a named competitor, since pitching a competitor's own content only amplifies them. Don't reject on this basis: keep recall and let triage decide. Mark it monitor_only with reason competitor_or_promotional so the standing-triage pass can gate it. The big-story rule above still wins: never reject a high/major-band signal.
  • Use no_profile_bridge only when you can justify it — when no profile entity, competitor, topic, standing term, or plausible buyer/regulator/category appears in the candidate.
  • Cite your evidence. Preserve evidence URLs; each decision lists the URLs it used.

Engines

This rubric can run on two engines. Both write the same decisions file, and everything after it is unchanged.

  • Low-cost LLM worker (default). A worker loads this file and judges its chunk of signals. This is the path when nothing else is configured.
  • Jev (TypeSafe AI), when a key is present. Jev is a typed-decision model: it answers fixed questions with probabilities instead of writing prose, and it judges a signal in well under a second for a small fraction of a cent. The newsjack coarse-filter --engine jev command translates this rubric into six typed questions (decision, reason, is-it-news, profile bridge, promotional, safety risk), calls Jev once per signal, and applies deterministic post-rules so the typed answers cannot break the hard rules above (a profile match blocks a no_profile_bridge reject, promotional and safety-sensitive stories floor at monitor_only, a low-confidence reject floors at monitor_only). Big-story recall stays in newsjack filter-apply as before. Run newsjack coarse-filter --print-questions to read the translation and newsjack help coarse-filter for usage.

Pick Jev when newsjack doctor shows TypeSafe configured; otherwise use the worker path. Jev decisions carry a rationale that starts with Jev: and lists the raw probabilities, because the engine gives no prose reason; the report should show that honestly rather than dress it up. If more than a fifth of the Jev calls fail, the command exits non-zero and the run should fall back to the worker path for this pass.

Machine handoff

This skill is a pipeline stage that runs on a low-cost model or on Jev. Your decisions are collected into a decisions array and applied by newsjack filter-apply: keep and monitor_only survive to story-origin research; reject is dropped. You do not run that step.

The pipeline reads your output as raw JSON. Emit exactly one JSON object per signal, with these exact fields — return only the JSON, with no prose before or after it, and no Markdown wrapping:

json
{
  "signal_id": "engine signal id",
  "decision": "keep | monitor_only | reject",
  "reason": "allowed reason",
  "rationale": "One short sentence explaining the filter decision.",
  "confidence": "high | medium | low",
  "evidence_urls": ["https://..."],
  "relevance_basis": "Why this is plausibly relevant or why it is junk."
}

Frequently asked questions

What does the Relevance Coarse Filter AI skill do?

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitor_only, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.

Why use Relevance Coarse Filter on TypingMind?

Because you install it once and use it with any model. Relevance Coarse Filter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Relevance Coarse Filter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elvisun/newsjack/tree/main/skills/relevance-coarse-filter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Relevance Coarse Filter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Relevance Coarse Filter?

As many as you like. As long as a model supports skills, you can use Relevance Coarse Filter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Relevance Coarse Filter AI skill free?

Yes. It is published on GitHub by elvisun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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