contextstream-workflow

A workflow for keeping plans, tasks, decisions, lessons, and implementation details in ContextStream, a persistent memory system for AI sessions. It describes how to start sessions, load context, and record ongoing work.

In plain words
What is it for?
Use it to initialize project sessions, retrieve relevant memory and lessons, capture multi-step plans, and preserve decisions for future work.
Why use it?
Important project information can otherwise be lost when an AI session ends. Persistent records help later sessions continue with the right background and decisions.

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/cortexprism/cortex/contextstream-workflow
Any agent
npx skills add CortexPrism/cortex --skill contextstream-workflow
Clone the repo
git clone --depth 1 https://github.com/CortexPrism/cortex

Made for: Claude Code, Codex.

Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 773 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 $0.00016 $0.00773
Opus 5 $0.00008 $0.00387
Sonnet 5 $0.00003 $0.00155
Haiku 4.5 $0.00002 $0.00077

Measured 2d ago against content hash 18f39f2b4ade, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

contextstream-workflow 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.

.github/skills/contextstream-workflow/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ContextStream Workflow Skill

Purpose

Use ContextStream to keep plans, tasks, decisions, lessons, and implementation context available across Copilot sessions.

Session Lifecycle

1. Start the session

Always call init at the beginning of a new session:

init(
  folder_path="<project_path>",
  context_hint="<user's first message>"
)

Then call context with the current request:

context(
  user_message="<current user message>"
)

For later messages in the same session, call context first before doing more work.

Before inventing a workflow from memory, check whether ContextStream already surfaced relevant skills, docs, lessons, or decisions for the task. Use skill(action="list"), memory(action="list_docs"), session(action="get_lessons"), and memory(action="decisions", workspace_id="<current_workspace_id>", project_id="<current_project_id>") when ids are available and the task is unfamiliar or likely already documented. Reuse the current project_id returned by init or context for project-scoped docs, events, and skills instead of guessing.

2. Plan multi-step work

Capture a persistent plan:

session(
  action="capture_plan",
  title="Implement feature X",
  steps=[
    {"id": "1", "title": "Research the current code path", "order": 1},
    {"id": "2", "title": "Implement the change", "order": 2},
    {"id": "3", "title": "Add verification", "order": 3}
  ]
)

Then create linked tasks:

memory(
  action="create_task",
  title="Implement the change",
  plan_id="<plan_id>",
  plan_step_id="2",
  priority="high"
)

3. Track progress while working

Start a task:

memory(
  action="update_task",
  task_id="<task_id>",
  status="in_progress"
)

Capture a technical decision:

session(
  action="capture",
  event_type="decision",
  title="Use repository pattern for data access",
  content="Chose a repository layer to isolate persistence logic and simplify testing."
)

Finish a task:

memory(
  action="update_task",
  task_id="<task_id>",
  status="completed"
)

Read the full file on GitHub · 149 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 · 149 lines · 16 tokens per session scan A 18f39f2b4ade

Subscribe to this mod's changes

contextstream-workflow is a skill published in the GitHub repository CortexPrism/cortex (192 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 773 once invoked, about $0.0001 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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