Borrowing it
Nothing to install: this file belongs to leshchenko1979/fast-mcp-telegram. 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/leshchenko1979/fast-mcp-telegram/master/.cursor/skills/feature-development/SKILL.mdgit clone --depth 1 https://github.com/leshchenko1979/fast-mcp-telegramWrote 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/leshchenko1979/fast-mcp-telegram/feature-development)<a href="https://agentmods.dev/skills/leshchenko1979/fast-mcp-telegram/feature-development"><img src="https://agentmods.dev/badge/skills/leshchenko1979/fast-mcp-telegram/feature-development/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/leshchenko1979/fast-mcp-telegram/feature-development"><img src="https://agentmods.dev/badge/skills/leshchenko1979/fast-mcp-telegram/feature-development.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 | $0.00028 | $0.01147 |
| Opus 5.5 | $0.00011 | $0.00459 |
| Sonnet 5.5 | $0.00006 | $0.00229 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
feature-development 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- feature-development — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Development Workflow
Process Overview
Research -> Plan -> Spike -> Implement -> Test -> Clean Up -> Push
Phase 1: Research
Goal: Understand the problem space and existing code
-
Explore codebase with explore subagent:
- Find relevant existing functions and patterns
- Understand data structures and type definitions
- Map relationships between components
-
API Investigation:
- Use Context7 MCP for library documentation
- Use WebSearch for Telegram API specs
- Use telegram-dev MCP for live API probing
- Verify actual return values and structures
-
Edge Case Identification:
- Identify potential issues early
- Note API limitations or quirks
- Check for existing tests that may break
Phase 2: Planning
Goal: Create a detailed, actionable plan
-
Clarify Requirements:
- Ask user for specifics on behavior
- Offer numbered options for implementation choices
- Default to simplest approach
-
Document Technical Findings:
- API behavior verified through research
- Type structures and field access patterns
- Known limitations and edge cases
-
Create Plan (use CreatePlan tool):
- Numbered steps with specific file:line references
- Code snippets for key changes
- Mermaid diagrams for complex relationships
- Complexity assessment table
Phase 3: Spiking
Goal: Verify assumptions with live API tests
Use telegram-dev MCP for live testing:
-
API Response Structure:
- Call methods directly and inspect raw responses
- Verify type definitions (e.g.,
TextWithEntitiesvs plain string) - Check field names and access patterns
-
Edge Cases:
- Test with edge cases (empty results, invalid inputs)
- Verify error handling behavior
- Confirm caching behavior if applicable
-
Document Findings:
- Update plan with corrected assumptions
- Note any API quirks or bugs
- Identify potential issues before implementation
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.
- 1mo ago First seen · 211 lines · 28 tokens per session scan A 1bb309cb9296
feature-development is a skill published in the GitHub repository leshchenko1979/fast-mcp-telegram (50 stars, last pushed 8d ago), licensed MIT. It adds 28 tokens to every session and 1,147 once invoked, about $0.0001 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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