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.
curl -O https://raw.githubusercontent.com/galiprandi/job-seeker/main/.agents/skills/memory/SKILL.mdgit clone --depth 1 https://github.com/galiprandi/job-seekerWrote 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/galiprandi/job-seeker/memory)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00036 | $0.01867 |
| Opus 5 | $0.00018 | $0.00933 |
| Sonnet 5 | $0.00007 | $0.00373 |
| Haiku 4.5 | $0.00004 | $0.00187 |
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.
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:
- Detection: after every user message, evaluate whether a preference was stated, implied, or corrected
- 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)
)
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.
- 9d ago First seen · 143 lines · 36 tokens per session scan A 90d817814c28
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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