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 | $0.01898 |
| Opus 5.5 | $0.00014 | $0.00759 |
| Sonnet 5.5 | $0.00007 | $0.00380 |
| Haiku 4.5 | $0.00004 | $0.00190 |
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 2d 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.
- 2d ago Changed 5a6b5ce70bc0
- 1mo ago Changed 9cf4313ca9a1
- 1mo ago First seen · 143 lines · 36 tokens per session scan A 90d817814c28
memory is a skill published in the GitHub repository galiprandi/job-seeker (58 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,898 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
vitaecontext-vitaegraph
Build, deepen, validate, index, and maintain a private hierarchical career knowledge graph from supplied career materials. Use when the user asks to create or update a VitaeGraph, model education with nested courses or thesis work, enrich projects from Git repositories, or supply deep selected career context to…
profile-memory
How Rebound stores and reuses the user's career profile, including the private "situation" (work authorization, timeline). Use when reading, writing, or reasoning about the saved profile at /.rebound/.
ai-dev-jobs-mcp
Search indexed AI and ML jobs, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP.
Cold Email Writer
Write personalized cold outreach emails to hiring managers and founders - specific, human, not a pitch deck.
lessons
How to propose and query distilled lessons (the experiential memory). Only project-steward persists; every other agent proposes/queries. Use when you discover a durable lesson or need prior experience for the current task.
memory-state
State variables the Sentinel must keep across ticks (last order sent, freeze flag, FATAL streak, per-state counters, emergency history, cooldowns). Use this skill on every tick to update memory and decide if a new order is required (edge-triggered).