job-seeker: Skill for Claude Code

.agents/skills/memory/SKILL.md

memory is a skill for Claude Code, Codex from galiprandi/job-seeker. It costs 36 tokens per session (1,867 once invoked), scanned A, original, MIT.

A background preference manager that notices the user’s stated or implied choices and stores them in a database. It loads active preferences at the start of other workflows.

In plain words
What is it for?
Recording preferences such as desired roles, locations, salary, language, tone, response length, and application methods. It then makes those preferences available to other workflows.
Why use it?
It prevents the agent from repeatedly asking about communication style, job-search criteria, or tool preferences. Corrections can replace older preferences so later work reflects current choices.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is galiprandi/job-seeker's own configuration. It tells Claude Code and Codex how to work on job-seeker itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything job-seeker configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is node scripts/db.js "INSERT INTO preferences (user_id, category, key, value, confidence, source) VALUES (1, '<category>', '<key>', '<value>', <confidence>, '<sou.

Reuse

Borrowing it

Nothing to install: this file belongs to galiprandi/job-seeker. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/galiprandi/job-seeker/main/.agents/skills/memory/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/galiprandi/job-seeker

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memory

README.md
[![agentmods](https://agentmods.dev/badge/skills/galiprandi/job-seeker/memory/github.svg)](https://agentmods.dev/skills/galiprandi/job-seeker/memory)
Your own site
<a href="https://agentmods.dev/skills/galiprandi/job-seeker/memory"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/memory/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.

agentmods 80×15 button for memory

Your own site · 80×15
<a href="https://agentmods.dev/skills/galiprandi/job-seeker/memory"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,867 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 103
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.01867
Opus 5 $0.00018 $0.00933
Sonnet 5 $0.00007 $0.00373
Haiku 4.5 $0.00004 $0.00187

Measured 9d ago against content hash 90d817814c28, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

memory 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 9d 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.

.agents/skills/memory/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory — Autonomous preference management

This skill implements Gold Rule 3 (user preferences always up to date). The agent proactively detects, stores, and injects user preferences without being asked.

When it runs

Always. This is not a flow triggered by a keyword. It is a background behavior that runs during every interaction:

  1. Detection: after every user message, evaluate whether a preference was stated, implied, or corrected
  2. Injection: at the pre-flight of every flow, load active preferences into context

Detection

After every user message, scan for 3 signal types:

Signal Example Confidence Source
Explicit "no quiero empresas de crypto" 1.0 explicit_statement
Implicit "respondeme corto" 0.7 inferred
Correction "actualmente busco roles de IC, no manager" 1.0 correction

Save vs skip checklist

Save (proactively, no need to ask):

  • Job search preferences (roles, industries, locations, work mode, salary)
  • Communication preferences (language, tone, length, format)
  • Compensation criteria (range, equity, benefits)
  • Tooling/workflow preferences (which platforms, how to apply)
  • Corrections to anything previously stored
  • Explicit requests: "recordá que..." / "remember that..."
  • Strategy level changes. When the user's situation changes (employment status, urgency), detect and propose a strategy level change. See AGENTS.md "Strategy levels"

Skip:

  • Trivial/obvious info ("user asked about Python")
  • Already in CV or users.data.profile (don't duplicate)
  • Already in users.data.style_profile
  • Re-discoverable facts (can web search)
  • Session-specific ephemera (temporary file paths, one-off debugging)
  • Already in AGENTS.md or other context files

Storage

All preferences live in the preferences table, accessed via scripts/db.js:

preferences (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  category TEXT NOT NULL,        -- job_search, communication, compensation, tooling, workflow
  key TEXT NOT NULL,             -- e.g. "avoid_industries", "reply_language", "salary_min"
  value TEXT NOT NULL,           -- e.g. "crypto,gambling", "spanish", "5000"
  confidence REAL DEFAULT 1.0,   -- 1.0 explicit, 0.7 inferred, 0.5 auto-summarized
  source TEXT DEFAULT 'explicit_statement', -- explicit_statement | inferred | correction
  status TEXT DEFAULT 'active',  -- active | superseded
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW(),
  UNIQUE(user_id, category, key)
)

Read the full file on GitHub · 143 lines

Changes

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.

  1. 9d ago First seen · 143 lines · 36 tokens per session scan A 90d817814c28

Subscribe to this mod's changes

memory is a skill published in the GitHub repository galiprandi/job-seeker (26 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 1,867 once invoked, about $0.0002 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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