autotask-mcp self_improve.instructions.md

autotask-mcp self_improve.instructions.md is an instructions file for GitHub Copilot from TICnine/autotask-mcp. It costs 524 tokens per session, scanned A, a copy of autotask-mcp self_improve.instructions.md, Apache-2.0.

Guidelines for updating VS Code coding rules as recurring patterns, errors, libraries, and best practices appear in a codebase. VS Code is a code editor, and these rules help guide how its coding work is done.

In plain words
What is it for?
Use it to identify patterns worth standardising, improve existing rule examples, add safeguards for recurring bugs, and update guidance for consistently used tools or libraries.
Why use it?
It reduces repeated review comments and prevents common mistakes from being rediscovered in each file. It also keeps project rules aligned with how the code is actually evolving.

Instructions file for GitHub Copilot

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 instructions/ticnine/autotask-mcp/self_improve
Clone the repo
git clone --depth 1 https://github.com/TICnine/autotask-mcp

Made for: GitHub Copilot.

Wrote 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.

agentmods badge for autotask-mcp self_improve.instructions.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/ticnine/autotask-mcp/self_improve.svg)](https://agentmods.dev/instructions/ticnine/autotask-mcp/self_improve)
Your own site
<a href="https://agentmods.dev/instructions/ticnine/autotask-mcp/self_improve"><img src="https://agentmods.dev/badge/instructions/ticnine/autotask-mcp/self_improve.svg" alt="Measured on agentmods" height="20"></a>
Per session 524 This file is loaded in full into every session.
When invoked 524 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00524 $0.00524
Opus 5 $0.00262 $0.00262
Sonnet 5 $0.00105 $0.00105
Haiku 4.5 $0.00052 $0.00052

Measured 3d ago against content hash b328e8193bc6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

autotask-mcp self_improve.instructions.md 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 3d 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.

Origin

This is a copy

100% identical to autotask-mcp self_improve.instructions.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.github/instructions/self_improve.instructions.md · 72 lines

What it actually says

  • Rule Improvement Triggers:

    • New code patterns not covered by existing rules
    • Repeated similar implementations across files
    • Common error patterns that could be prevented
    • New libraries or tools being used consistently
    • Emerging best practices in the codebase
  • Analysis Process:

    • Compare new code with existing rules
    • Identify patterns that should be standardized
    • Look for references to external documentation
    • Check for consistent error handling patterns
    • Monitor test patterns and coverage
  • Rule Updates:

    • Add New Rules When:

      • A new technology/pattern is used in 3+ files
      • Common bugs could be prevented by a rule
      • Code reviews repeatedly mention the same feedback
      • New security or performance patterns emerge
    • Modify Existing Rules When:

      • Better examples exist in the codebase
      • Additional edge cases are discovered
      • Related rules have been updated
      • Implementation details have changed
  • Example Pattern Recognition:

    // If you see repeated patterns like:
    const data = await prisma.user.findMany({
      select: { id: true, email: true },
      where: { status: 'ACTIVE' }
    });
    
    // Consider adding to [prisma.instructions.md](.github/instructions/prisma.instructions.md):
    // - Standard select fields
    // - Common where conditions
    // - Performance optimization patterns
    
  • Rule Quality Checks:

    • Rules should be actionable and specific
    • Examples should come from actual code
    • References should be up to date
    • Patterns should be consistently enforced
  • Continuous Improvement:

    • Monitor code review comments
    • Track common development questions
    • Update rules after major refactors
    • Add links to relevant documentation
    • Cross-reference related rules
  • Rule Deprecation:

    • Mark outdated patterns as deprecated
    • Remove rules that no longer apply
    • Update references to deprecated rules
    • Document migration paths for old patterns
  • Documentation Updates:

    • Keep examples synchronized with code
    • Update references to external docs
    • Maintain links between related rules
    • Document breaking changes Follow vscode_rules.instructions.md for proper rule formatting and structure.
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. 3d ago First seen · 72 lines · 524 tokens per session scan A b328e8193bc6

Subscribe to this mod's changes

autotask-mcp self_improve.instructions.md is an instructions file published in the GitHub repository TICnine/autotask-mcp (0 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 524 tokens to every session, about $0.0026 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to autotask-mcp self_improve.instructions.md, differing in 0 lines, and is treated as a copy.

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