Agent skill

Relevance Coarse Filter

by elvisun in elvisun/newsjack

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment.

MITAuto-check passed

Install Relevance Coarse Filter

skills CLI
$ npx skills add elvisun/newsjack --skill relevance-coarse-filter -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install elvisun/newsjack relevance-coarse-filter --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/elvisun/newsjack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/relevance-coarse-filter .claude/skills/relevance-coarse-filter && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
relevance-coarse-filter
GitHub stars
1.5k
Token cost
~1.9k tokens
SKILL.md length
941 words
Files
1
Skills in repo
30
Repo updated
First seen
Licence
MIT

At a glance

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment.

  • SKILL.md covers Inputs, Decisions and reasons, Rubric and Engines, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Relevance Coarse Filter is an agent skill from elvisun/newsjack. 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, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: The open-source skills that turn your agent into a full PR team. The licence is MIT.

Example prompts

  • “/relevance-coarse-filter”

What it can do on your machine

Read from SKILL.md and the folder at commit b5a8dc8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Relevance Coarse Filter loads about 1.9k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 941 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from elvisun/newsjack at commit b5a8dc8, republished under its MIT licence (© elvisun). 941 words, ~1,884 tokens.

Download SKILL.mdSave it as .claude/skills/relevance-coarse-filter/SKILL.md (or your agent's skills folder).
name
relevance-coarse-filter
description
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.
when_to_use
Use as the coarse relevance pass of the newsjack-detector pipeline, or whenever a candidate signal pool needs cheap junk removal before expensive…
metadata.category
Detect

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.
Show full SKILL.md (318 more words)Show less

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."
}

© elvisun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/relevance-coarse-filter of elvisun/newsjack.

Open the folder on GitHubat commit b5a8dc8

Compare with similar skills

Relevance Coarse Filter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Relevance Coarse Filter compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Relevance Coarse Filter this skillelvisun/newsjack1.5k—~1.9kAutomated safety check: PassMIT
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Hindsight Recallvectorize-io/hindsight48k—~312Automated safety check: PassMIT
Recallparcadei/Continuous-Claude-v33.9k1 repos~314Automated safety check: PassMIT
Recallcursor/plugins11k7 repos~1.3kAutomated safety check: PassNone
Dead Code Removercode-yeongyu/oh-my-openagent70k—~1.8kAutomated safety check: PassCustom licence

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Questions about Relevance Coarse Filter

What does Relevance Coarse Filter do?

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Relevance Coarse Filter is an agent skill from elvisun/newsjack. Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment.

How do I install Relevance Coarse Filter in Claude Code?

Run `npx skills add elvisun/newsjack --skill relevance-coarse-filter -a claude-code`. Or copy the skill folder (skills/relevance-coarse-filter in elvisun/newsjack) into .claude/skills/relevance-coarse-filter in your project. Claude Code loads it when a task matches its description.

How do I install Relevance Coarse Filter in Codex?

Run `npx skills add elvisun/newsjack --skill relevance-coarse-filter -a codex`. Or copy the skill folder (skills/relevance-coarse-filter in elvisun/newsjack) into .agents/skills/relevance-coarse-filter in your project. Codex loads it when a task matches its description.

Can I use Relevance Coarse Filter in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add elvisun/newsjack --skill relevance-coarse-filter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/relevance-coarse-filter, .gemini/skills/relevance-coarse-filter, .github/skills/relevance-coarse-filter and .opencode/skills/relevance-coarse-filter in your project.

What does Relevance Coarse Filter need to run?

SKILL.md names no scripts, command-line tools or credentials: Relevance Coarse Filter is instructions for the agent only.

Does Relevance Coarse Filter access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Relevance Coarse Filter safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Relevance Coarse Filter use?

Relevance Coarse Filter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Relevance Coarse Filter use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Relevance Coarse Filter?

Skills that share tags, products or a category with Relevance Coarse Filter: Filter (zalando/skipper, 3.3k stars), Hindsight Recall (vectorize-io/hindsight, 48k stars), Recall (parcadei/Continuous-Claude-v3, 3.9k stars) and Recall (cursor/plugins, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Relevance Coarse Filter?

elvisun (a GitHub user) maintains it in elvisun/newsjack, which has 1,533 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 7, 2026.

Source: elvisun/newsjack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.