scope-knowledge-update

A procedure for updating SCOPE's stored security knowledge after evidence review, a final decision, or an approved save. It organizes stable environment facts, observations, coverage gaps, and reviewed reasoning notes into Markdown files.

In plain words
What is it for?
Use it to record confirmed environment details, normal patterns, false positives, control notes, telemetry gaps, and improvements to exploit or investigation reasoning.
Why use it?
It preserves useful findings and known limitations for later AWS security runs without allowing unreviewed sub-agent suggestions to change the knowledge base.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tayontech/scope/scope-knowledge-update
Any agent
npx skills add tayontech/SCOPE --skill scope-knowledge-update
Clone the repo
git clone --depth 1 https://github.com/tayontech/SCOPE

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 867 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00036 $0.00867
Opus 5 $0.00018 $0.00434
Sonnet 5 $0.00007 $0.00173
Haiku 4.5 $0.00004 $0.00087

Measured 2d ago against content hash 527ef6811a3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scope-knowledge-update 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.

skills/scope-knowledge-update/SKILL.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SCOPE Knowledge Update

Use this skill after a top-level agent has evidence-backed results and the operator-approved workflow allows persistence.

Subagents may propose knowledge_updates[], but only the top-level orchestrator applies updates through this skill.

Inputs

The caller provides:

  • ACCOUNT_ID
  • AGENT: scope-audit, scope-controls, scope-exploit, or scope-investigate
  • RUN_DIR or evidence file path when available
  • Final disposition or outcome
  • Evidence-backed candidate updates

Files To Update

Create Markdown files from the templates in knowledge/ when missing.

  • knowledge/environment.md: operator-supplied stable environment facts only
  • knowledge/observations.md: durable lessons, baselines, false positives, known-good patterns, automation notes, deployed-control notes, and org-wide patterns
  • knowledge/coverage-gaps.md: telemetry gaps and AWS audit, enumeration, or authorization gaps
  • knowledge/exploit-reasoning-notes.md: operator-reviewed expert reasoning note improvements for exploit analysis
  • knowledge/hunt-reasoning-notes.md: operator-reviewed expert reasoning note improvements for investigations and hunts

There are no persistent investigation or research records under durable knowledge. Keep run-specific investigation and research artifacts under the run directory only.

Update Status Values

Every durable update must use one of:

  • confirmed
  • likely_normal
  • suspicious
  • false_positive
  • coverage_gap
  • needs_review

Safety Rules

  • Do not write secrets, access keys, session tokens, passwords, raw credential material, or private keys.
  • Do not write ARNs, account IDs, bucket names, role names, key IDs, or access key IDs. Resource identifiers are session-scoped.
  • Do not write unsupported claims.
  • Do not write run-specific noise unless it will help future investigations.
  • Do not promote a pattern to org-wide unless at least two distinct account sections support it.
  • Do not overwrite existing knowledge. Append or merge.
  • Dedupe before appending.
  • Every update must cite evidence: run directory, report path, query ID, event ID, finding ID, or analyst-approved note.
  • Generalize resource-specific evidence before persistence. Use service names, control categories, event patterns, and behavior classes instead of exact identifiers.
  • Before writing, scan candidate entries for exact identifier patterns: arn:aws:, 12-digit account IDs, access key IDs, key IDs, role names, user names, bucket names, and secret names. Redact or generalize the value. If the value cannot be generalized without losing meaning, skip it and report the skip reason.
  • Evidence citations may point to run directories, reports, query IDs, event IDs, or finding IDs. Do not copy raw resource identifiers into the durable knowledge entry.
  • Use dates in YYYY-MM-DD format.

Read the full file on GitHub · 89 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. 2d ago First seen · 89 lines · 36 tokens per session scan A 527ef6811a3c

Subscribe to this mod's changes

scope-knowledge-update is a skill published in the GitHub repository tayontech/SCOPE (54 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens