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Context Mode

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mksglu
context-mode

Use context-mode tools (ctx_execute, ctx_execute_file) instead of Bash/cat when processing large outputs. Triggers: "analyze logs", "summarize output", "process data", "parse JSON", "filter results", "extract errors", "check build output", "analyze dependencies", "process API response", "large file analysis", "page snapshot", "browser snapshot", "DOM structure", "inspect page", "accessibility tree", "Playwright snapshot", "run tests", "test output", "coverage report", "git log", "recent commits", "diff between branches", "list containers", "pod status", "disk usage", "fetch docs", "API reference", "index documentation", "call API", "check response", "query results", "find TODOs", "count lines", "codebase statistics", "security audit", "outdated packages", "dependency tree", "cloud resources", "CI/CD output". Also triggers on ANY MCP tool output that may exceed 20 lines. Subagent routing is handled automatically via PreToolUse hook.

Overview

Publishermksglu
Repositorycontext-mode
Skill namecontext-mode
Stars
23.4K
Forks
1.7K
Bundled files
4
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mksglu on GitHub. Read the source before you install it.

Installation

Install the Context Mode AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mksglu/context-mode.git /tmp/context-mode
mkdir -p .claude/skills
cp -r /tmp/context-mode/skills/context-mode .claude/skills/context-mode
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Mode in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Context Mode on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Context Mode is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Context Mode: Default for All Large Output

MANDATORY RULE

<context_mode_logic> <mandatory_rule> Default to context-mode for ALL commands. Only use Bash for guaranteed-small-output operations. </mandatory_rule> </context_mode_logic>

Bash whitelist (safe to run directly):

  • File mutations: mkdir, mv, cp, rm, touch, chmod
  • Git writes: git add, git commit, git push, git checkout, git branch, git merge
  • Navigation: cd, pwd, which
  • Process control: kill, pkill
  • Package management: npm install, npm publish, pip install
  • Simple output: echo, printf

Everything else → ctx_execute or ctx_execute_file. Any command that reads, queries, fetches, lists, logs, tests, builds, diffs, inspects, or calls an external service. This includes ALL CLIs (gh, aws, kubectl, docker, terraform, wrangler, fly, heroku, gcloud, etc.) — there are thousands and we cannot list them all.

When uncertain, use context-mode. Every KB of unnecessary context reduces the quality and speed of the entire session.

Decision Tree

About to run a command / read a file / call an API?
├── Command is on the Bash whitelist (file mutations, git writes, navigation, echo)?
│   └── Use Bash
├── Output MIGHT be large or you're UNSURE?
│   └── Use context-mode ctx_execute or ctx_execute_file
├── Fetching web documentation or HTML page?
│   └── Use ctx_fetch_and_index → ctx_search
├── Using Playwright (navigate, snapshot, console, network)?
│   └── ALWAYS use filename parameter to save to file, then:
│       browser_snapshot(filename) → ctx_index(path) or ctx_execute_file(path)
│       browser_console_messages(filename) → ctx_execute_file(path)
│       browser_network_requests(filename) → ctx_execute_file(path)
│       ⚠ browser_navigate returns a snapshot automatically — ignore it,
│         use browser_snapshot(filename) for any inspection.
│       ⚠ Playwright MCP uses a SINGLE browser instance — NOT parallel-safe.
│         For parallel browser ops, use agent-browser via execute instead.
├── Using agent-browser (parallel-safe browser automation)?
│   └── Run via execute (shell) — each call gets its own subprocess:
│       execute("agent-browser open example.com && agent-browser snapshot -i -c")
│       ✓ Supports sessions for isolated browser instances
│       ✓ Safe for parallel subagent execution
│       ✓ Lightweight accessibility tree with ref-based interaction
├── Processing output from another MCP tool (Context7, GitHub API, etc.)?
│   ├── Output already in context from a previous tool call?
│   │   └── Use it directly. Do NOT re-index with ctx_index(content: ...).
│   ├── Need to search the output multiple times?
│   │   └── Save to file via ctx_execute, then ctx_index(path) → ctx_search
│   └── One-shot extraction?
│       └── Save to file via ctx_execute, then ctx_execute_file(path)
└── Reading a file to analyze/summarize (not edit)?
    └── Use ctx_execute_file (file loads into FILE_CONTENT, not context)

When to Use Each Tool

SituationToolExample
Hit an API endpointctx_executefetch('http://localhost:3000/api/orders')
Run CLI that returns datactx_executegh pr list, aws s3 ls, kubectl get pods
Run testsctx_executenpm test, pytest, go test ./...
Git operationsctx_executegit log --oneline -50, git diff HEAD~5
Docker/K8s inspectionctx_executedocker stats --no-stream, kubectl describe pod
Read a log filectx_execute_fileParse access.log, error.log, build output
Read a data filectx_execute_fileAnalyze CSV, JSON, YAML, XML
Read source code to analyzectx_execute_fileCount functions, find patterns, extract metrics
Fetch web docsctx_fetch_and_indexIndex React/Next.js/Zod docs, then search
Playwright snapshotbrowser_snapshot(filename)ctx_index(path)ctx_searchSave to file, index server-side, query
Playwright snapshot (one-shot)browser_snapshot(filename)ctx_execute_file(path)Save to file, extract in sandbox
Playwright console/networkbrowser_*(filename)ctx_execute_file(path)Save to file, analyze in sandbox
MCP output (already in context)Use directlyDon't re-index — it's already loaded
MCP output (need multi-query)ctx_execute to save → ctx_index(path)ctx_searchSave to file first, index server-side
Wipe indexed KB contentctx_purge(confirm: true)Permanently deletes all indexed content

Automatic Triggers

Use context-mode for ANY of these, without being asked:

  • API debugging: "hit this endpoint", "call the API", "check the response", "find the bug in the response"
  • Log analysis: "check the logs", "what errors", "read access.log", "debug the 500s"
  • Test runs: "run the tests", "check if tests pass", "test suite output"
  • Git history: "show recent commits", "git log", "what changed", "diff between branches"
  • Data inspection: "look at the CSV", "parse the JSON", "analyze the config"
  • Infrastructure: "list containers", "check pods", "S3 buckets", "show running services"
  • Dependency audit: "check dependencies", "outdated packages", "security audit"
  • Build output: "build the project", "check for warnings", "compile errors"
  • Code metrics: "count lines", "find TODOs", "function count", "analyze codebase"
  • Web docs lookup: "look up the docs", "check the API reference", "find examples"

Language Selection

SituationLanguageWhy
HTTP/API calls, JSONjavascriptNative fetch, JSON.parse, async/await
Data analysis, CSV, statspythoncsv, statistics, collections, re
Shell commands with pipesshellgrep, awk, jq, native tools
File pattern matchingshellfind, wc, sort, uniq

Search Query Strategy

  • BM25 uses OR semantics — results matching more terms rank higher automatically
  • Use 2-4 specific technical terms per query
  • Always use source parameter when multiple docs are indexed to avoid cross-source contamination
    • Partial match works: source: "Node" matches "Node.js v22 CHANGELOG"
  • Always use queries array — batch ALL search questions in ONE call:
    • ctx_search(queries: ["transform pipe", "refine superRefine", "coerce codec"], source: "Zod")
    • NEVER make multiple separate ctx_search() calls — put all queries in one array

External Documentation

  • Always use ctx_fetch_and_index for external docs — NEVER cat or ctx_execute with local paths for packages you don't own
  • For GitHub-hosted projects, use the raw URL: https://raw.githubusercontent.com/org/repo/main/CHANGELOG.md
  • After indexing, use the source parameter in search to scope results to that specific document

Critical Rules

  1. Always console.log/print your findings. stdout is all that enters context. No output = wasted call.
  2. Write analysis code, not just data dumps. Don't console.log(JSON.stringify(data)) — analyze first, print findings.
  3. Be specific in output. Print bug details with IDs, line numbers, exact values — not just counts.
  4. For files you need to EDIT: Use the normal Read tool. context-mode is for analysis, not editing.
  5. For Bash whitelist commands only: Use Bash for file mutations, git writes, navigation, process control, package install, and echo. Everything else goes through context-mode.
  6. Never use ctx_index(content: large_data). Use ctx_index(path: ...) to read files server-side. The content parameter sends data through context as a tool parameter — use it only for small inline text.
  7. Always use filename parameter on Playwright tools (browser_snapshot, browser_console_messages, browser_network_requests). Without it, the full output enters context.
  8. Don't re-index data already in context. If an MCP tool returned data in a previous response, it's already loaded — use it directly or save to file first.

Sandboxed Data Workflow

<sandboxed_data_workflow> <critical_rule> When using tools that support saving to a file: ALWAYS use the 'filename' parameter. NEVER return large raw datasets directly to context. </critical_rule> LargeDataTool(filename: "path") → mcp__context-mode__ctx_index(path: "path") → ctx_search() </sandboxed_data_workflow>

This is the universal pattern for context preservation regardless of the source tool (Playwright, GitHub API, AWS CLI, etc.).

Examples

Debug an API endpoint

javascript
const resp = await fetch('http://localhost:3000/api/orders');
const { orders } = await resp.json();

const bugs = [];
const negQty = orders.filter(o => o.quantity < 0);
if (negQty.length) bugs.push(`Negative qty: ${negQty.map(o => o.id).join(', ')}`);

const nullFields = orders.filter(o => !o.product || !o.customer);
if (nullFields.length) bugs.push(`Null fields: ${nullFields.map(o => o.id).join(', ')}`);

console.log(`${orders.length} orders, ${bugs.length} bugs found:`);
bugs.forEach(b => console.log(`- ${b}`));

Analyze test output

shell
npm test 2>&1
echo "EXIT=$?"

Check GitHub PRs

shell
gh pr list --json number,title,state,reviewDecision --jq '.[] | "\(.number) [\(.state)] \(.title) — \(.reviewDecision // "no review")"'

Read and analyze a large file

python
# FILE_CONTENT is pre-loaded by ctx_execute_file
import json
data = json.loads(FILE_CONTENT)
print(f"Records: {len(data)}")
# ... analyze and print findings

Browser & Playwright Integration

When a task involves Playwright snapshots, screenshots, or page inspection, ALWAYS route through file → sandbox.

Playwright browser_snapshot returns 10K–135K tokens of accessibility tree data. Calling it without filename dumps all of that into context. Passing the output to ctx_index(content: ...) sends it into context a SECOND time as a parameter. Both are wrong.

The key insight: browser_snapshot has a filename parameter that saves to file instead of returning to context. ctx_index has a path parameter that reads files server-side. ctx_execute_file processes files in a sandbox. None of these touch context.

Workflow A: Snapshot → File → Index → Search (multiple queries)

Step 1: browser_snapshot(filename: "/tmp/playwright-snapshot.md")
        → saves to file, returns ~50B confirmation (NOT 135K tokens)

Step 2: ctx_index(path: "/tmp/playwright-snapshot.md", source: "Playwright snapshot")
        → reads file SERVER-SIDE, indexes into FTS5, returns ~80B confirmation

Step 3: ctx_search(queries: ["login form email password"], source: "Playwright")
        → returns only matching chunks (~300B)

Total context: ~430B instead of 270K tokens. Real 99% savings.

Workflow B: Snapshot → File → Execute File (one-shot extraction)

Step 1: browser_snapshot(filename: "/tmp/playwright-snapshot.md")
        → saves to file, returns ~50B confirmation

Step 2: ctx_execute_file(path: "/tmp/playwright-snapshot.md", language: "javascript", code: "
          const links = [...FILE_CONTENT.matchAll(/- link \"([^\"]+)\"/g)].map(m => m[1]);
          const buttons = [...FILE_CONTENT.matchAll(/- button \"([^\"]+)\"/g)].map(m => m[1]);
          const inputs = [...FILE_CONTENT.matchAll(/- textbox|- checkbox|- radio/g)];
          console.log('Links:', links.length, '| Buttons:', buttons.length, '| Inputs:', inputs.length);
          console.log('Navigation:', links.slice(0, 10).join(', '));
        ")
        → processes in sandbox, returns ~200B summary

Total context: ~250B instead of 135K tokens.

Workflow C: Console & Network (save to file if large)

browser_console_messages(level: "error", filename: "/tmp/console.md")
→ ctx_execute_file(path: "/tmp/console.md", ...) or ctx_index(path: "/tmp/console.md", ...)

browser_network_requests(includeStatic: false, filename: "/tmp/network.md")
→ ctx_execute_file(path: "/tmp/network.md", ...) or ctx_index(path: "/tmp/network.md", ...)

CRITICAL: Why filename + path is mandatory

ApproachContext costCorrect?
browser_snapshot() → raw into context135K tokensNO
browser_snapshot()ctx_index(content: raw)270K tokens (doubled!)NO
browser_snapshot(filename)ctx_index(path)ctx_search~430BYES
browser_snapshot(filename)ctx_execute_file(path)~250BYES

Key Rule

ALWAYS use filename parameter when calling browser_snapshot, browser_console_messages, or browser_network_requests. Then process via ctx_index(path: ...) or ctx_execute_file(path: ...) — never ctx_index(content: ...).

Data flow: Playwright → file → server-side read → context. Never: Playwright → context → ctx_index(content) → context again.

Subagent Usage

Subagents automatically receive context-mode tool routing via a PreToolUse hook. You do NOT need to manually add tool names to subagent prompts — the hook injects them. Just write natural task descriptions.

Anti-Patterns

  • Using curl http://api/endpoint via Bash → 50KB floods context. Use ctx_execute with fetch instead.
  • Using cat large-file.json via Bash → entire file in context. Use ctx_execute_file instead.
  • Using gh pr list via Bash → raw JSON in context. Use ctx_execute with --jq filter instead.
  • Piping Bash output through | head -20 → you lose the rest. Use ctx_execute to analyze ALL data and print summary.
  • Narrowing ctx_execute output upstream of capture → ctx_execute captures, ctx_search filters; merging the layers drops data that the index never sees. See references/anti-patterns.md §8.
  • Running npm test via Bash → full test output in context. Use ctx_execute to capture and summarize.
  • Calling browser_snapshot() WITHOUT filename parameter → 135K tokens flood context. Always use browser_snapshot(filename: "/tmp/snap.md").
  • Calling browser_console_messages() or browser_network_requests() WITHOUT filename → entire output floods context. Always use the filename parameter.
  • Passing ANY large data to ctx_index(content: ...) → data enters context as a parameter. Always use ctx_index(path: ...) to read server-side. The content parameter should only be used for small inline text you're composing yourself.
  • Calling an MCP tool (Context7 query-docs, GitHub API, etc.) then passing the response to ctx_index(content: response)doubles context usage. The response is already in context — use it directly or save to file first.
  • Ignoring browser_navigate auto-snapshot → navigation response includes a full page snapshot. Don't rely on it for inspection — call browser_snapshot(filename) separately.
  • Expecting ctx_stats to reset or wipe anything → ctx_stats is read-only (shows stats only). Use ctx_purge(confirm: true) to permanently delete all indexed content.

Reference Files

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Context Mode AI skill do?

Use context-mode tools (ctx_execute, ctx_execute_file) instead of Bash/cat when processing large outputs. Triggers: "analyze logs", "summarize output", "process data", "parse JSON", "filter results", "extract errors", "check build output", "analyze dependencies", "process API response", "large file analysis", "page snapshot", "browser snapshot", "DOM structure", "inspect page", "accessibility tree", "Playwright snapshot", "run tests", "test output", "coverage report", "git log", "recent commits", "diff between branches", "list containers", "pod status", "disk usage", "fetch docs", "API refe...

Why use Context Mode on TypingMind?

Because you install it once and use it with any model. Context Mode is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Context Mode in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mksglu/context-mode/tree/main/skills/context-mode. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Context Mode?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Context Mode?

As many as you like. As long as a model supports skills, you can use Context Mode with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Context Mode AI skill free?

It is published on GitHub by mksglu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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