Borrowing it
Nothing to install: this file belongs to FranRom/pupila. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/FranRom/pupila/main/.claude/skills/pupila-filters/SKILL.mdgit clone --depth 1 https://github.com/FranRom/pupilaWrote 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.
[](https://agentmods.dev/skills/franrom/pupila/pupila-filters)<a href="https://agentmods.dev/skills/franrom/pupila/pupila-filters"><img src="https://agentmods.dev/badge/skills/franrom/pupila/pupila-filters/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.
<a href="https://agentmods.dev/skills/franrom/pupila/pupila-filters"><img src="https://agentmods.dev/badge/skills/franrom/pupila/pupila-filters.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.02846 |
| Opus 5 | $0.00032 | $0.01423 |
| Sonnet 5 | $0.00013 | $0.00569 |
| Haiku 4.5 | $0.00006 | $0.00285 |
Grade A, and why
pupila-filters 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
All filter logic lives in src/filters.ts. Weights + keyword lists load from config/profile.json at runtime via loadProfile() (NOT a static import — the file is gitignored and auto-bootstrapped from config/profile.default.json on first run). Adjusting weights or keywords is a non-code edit to profile.json.
Decision tree: what kind of change are you making?
| Task | Edit | Skill section |
|---|---|---|
| Change a scoring weight (e.g. seniority +15 → +20) | config/profile.json#weights.<field> |
"Tuning weights" below |
| Add/remove a keyword (e.g. another Rust framework) | config/profile.json#keywords.<list> |
"Tuning keywords" below |
| Add a new hard-drop rule | src/filters.ts (HARD_RULES array) |
"Adding a hard-drop rule" below |
| Add a new positive signal type | src/types.ts + config/profile.json#weights + src/filters.ts |
"Adding a new positive signal" below |
| Diagnose why a job has fitScore=N | Inspect _signals on the job |
"Debugging via _signals" below |
Order of operations (in applyFilters)
- Hard excludes — URL safety, junior/intern titles, location-incompatible (persona-neutral — see "Location & work type" below), non-engineering compounds, leadership, non-frontend eng (since user is a frontend engineer), non-tech roles.
- Body preparation —
preparedScoringBody()strips boilerplate (EEO, privacy, "About us") and truncates toscoringBodyMaxChars(default 1500). Keyword scoring runs against this — prevents footer text like "we use Anthropic Claude internally" from landing a +20 AI signal on a backend role. Hard-drops still see the full body. - Soft scoring — additive, capped at
maxScore(100):- Categories — each configured
CategoryDefwhose keywords match adds itsweightonce (binary); defaultweight0 = pure label. Replaces the old hardcoded web3/ai signals. A job is tagged with every match (Job.categories). - Stack (+10 React/Next/TS, +5 RN/Expo, +5 GraphQL/Tailwind/Vite) — tiered
- Seniority (+15 lead/staff/principal/head, +10 senior/sr) — binary
- Frontend title (+10) — binary
- Frontend body (+10) — tiered
- Location (+10 when the job matches an accepted region / is remote — driven by the
locationblock) — binary - Freshness (+10 within 7d, +5 within 14d) — binary
- Categories — each configured
- Negative —
outOfRegionPenalty(default -10) if the job is region-locked outside the candidate's accepted regions and they haven't opted into hard-excluding. Applied after capping. (Persona-neutral; replaced the old US-centric penalty.) - Drop — anything with
fitScore < minScoreToKeep(default 30). - Categories —
job.categories= ids of everyCategoryDefwhose keywords matched (multi-label, config order);[]when none (renders under synthetic "Other"). Defined inconfig/profile.json#categories, generated from the brief on Regenerate or edited on the Profile tab. Each entry:{ id, label, keywords, scope?, weight?, limit? }.
What ships with it
1 file 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.
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.
- 11d ago First seen · 159 lines · 64 tokens per session scan A 04751efec425
pupila-filters is a skill published in the GitHub repository FranRom/pupila (36 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 2,846 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.
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