Borrowing it
Nothing to install: this file belongs to SCStelz/security-investigator. 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/SCStelz/security-investigator/main/.github/skills/context-memory-review/SKILL.mdgit clone --depth 1 https://github.com/SCStelz/security-investigatorWrote 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/scstelz/security-investigator/context-memory-review)<a href="https://agentmods.dev/skills/scstelz/security-investigator/context-memory-review"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/context-memory-review/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/scstelz/security-investigator/context-memory-review"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/context-memory-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00106 | $0.03449 |
| Opus 5.5 | $0.00042 | $0.01380 |
| Sonnet 5.5 | $0.00021 | $0.00690 |
| Haiku 4.5 | $0.00011 | $0.00345 |
Grade A, and why
context-memory-review 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 24d 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Memory Review — Instructions
Purpose
Investigation workflows in this project lean on a tenant-context memory file — a local, gitignored living document that records environment-specific ground truth (known automation/orchestration fingerprints, known-good IPs, account classifications, honeypot/field-device inventory, validated personnel, and documented false-positive rules). Scan automations (e.g. the daily Threat Pulse) read that file to render accurate verdicts.
Over a week of scans, drill-down investigations validate new ground truth — new IPs, new personas,
new FP classes, new device classes — that is not yet captured in the context file. This skill reads two
evidence sources — the last N days of scan reports and the Mission Control findings log
(state/findings.json, the structured record of analyst-triggered skill drill-downs) — compares them
against the current context file, and produces a propose-only review document: a list of discrete,
human-reviewable candidate changes (ADD / MODIFY / FLAG) with section anchors, proposed text, supporting
evidence, recurrence counts, and confidence.
Memory file location. In this project the context file is a relative filename under
.copilot/memories/repo/(Copilot's own repo-memory folder, gitignored). The invoking prompt may pass just the basename; resolve it under that folder. The file is environment-specific and never committed.
This skill is the first half of a deliberate two-phase, human-in-the-loop workflow:
| Phase | Who | Action |
|---|---|---|
| 1. Propose (this skill) | Automation / interactive | Read reports + context file → emit review doc. No edits. |
| 2. Apply (separate, manual) | Human-directed interactive session | Operator reviews the doc, says "apply items X, Y, Z" → surgical edits to the context file. |
🔴 CRITICAL RULES — READ FIRST
- PROPOSE-ONLY. NEVER edit the context file in this skill. Do not write, append to, or modify the
context memory file. Do not
git commit, push, or open a PR. The only file this skill writes is the review document in the output directory.
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.
- 24d ago Changed 7c82131a4a3b
- 1mo ago First seen · 254 lines · 106 tokens per session scan A 71f7932869a2
context-memory-review is a skill published in the GitHub repository SCStelz/security-investigator (250 stars, last pushed 19d ago), licensed MIT. It adds 106 tokens to every session and 3,449 once invoked, about $0.0004 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.
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