Borrowing it
Nothing to install: this file belongs to christopherekfeldt/mcp-bitbucket-dc. 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/christopherekfeldt/mcp-bitbucket-dc/main/AGENTS.mdgit clone --depth 1 https://github.com/christopherekfeldt/mcp-bitbucket-dcWrote 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/instructions/christopherekfeldt/mcp-bitbucket-dc/agents-md)<a href="https://agentmods.dev/instructions/christopherekfeldt/mcp-bitbucket-dc/agents-md"><img src="https://agentmods.dev/badge/instructions/christopherekfeldt/mcp-bitbucket-dc/agents-md/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/instructions/christopherekfeldt/mcp-bitbucket-dc/agents-md"><img src="https://agentmods.dev/badge/instructions/christopherekfeldt/mcp-bitbucket-dc/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00572 | $0.00572 |
| Opus 5 | $0.00286 | $0.00286 |
| Sonnet 5 | $0.00114 | $0.00114 |
| Haiku 4.5 | $0.00057 | $0.00057 |
Grade A, and why
mcp-bitbucket-dc AGENTS.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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Guidance for AI agents and contributors working in this repository.
Purpose
- Keep changes minimal, focused, and easy to review.
- Prefer root-cause fixes over surface patches.
- Preserve existing public behavior unless the task explicitly requires change.
Repository Map
src/mcp_bitbucket_dc/: server, client, config, models, formatting, and tool modules.src/mcp_bitbucket_dc/tools/: user-facing Bitbucket tool implementations.tests/: unit tests and integration smoke tests..github/workflows/: CI (test.yml) and release (publish.yml).
Local Setup
uv sync
uv run pre-commit install
Required env for real Bitbucket calls:
BITBUCKET_HOSTorBITBUCKET_URLBITBUCKET_API_TOKEN
Day-to-Day Workflow
- Understand scope from user request.
- Read only relevant files first.
- Implement the smallest viable change.
- Run targeted checks, then broader checks if needed.
- Update docs when behavior, tools, or workflows change.
Coding Guidelines
- Match existing style and naming patterns.
- Avoid unrelated refactors.
- Do not add dependencies unless clearly justified.
- Keep tool output formatting consistent (
markdownvsjsonbehavior). - Do not hardcode environment-specific values.
Testing and Validation
Start narrow, then expand:
uv run pytest -q
Integration smoke tests (real/staging Bitbucket):
RUN_LIVE_SMOKE=1 uv run pytest -m integration -q
Lint/format checks via pre-commit hooks:
uv run pre-commit run --all-files
Release Workflow
Releases are automated via publish.yml (workflow dispatch):
- Ensure tests pass and PR is merged to
main. - Go to Actions → Release → Run workflow.
- Enter the version (e.g.
1.0.0) and optionally enable dry run. - The workflow runs tests, creates the git tag, publishes to PyPI, and creates a GitHub Release with auto-generated notes.
Documentation Expectations
- Keep
README.mdaccurate for install, config, tools, and release notes. - Keep acknowledgements and external references public and accessible.
- Add short usage examples for new tools when practical.
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.
- 10d ago First seen · 91 lines · 572 tokens per session scan A 62e84316f124
mcp-bitbucket-dc AGENTS.md is an instructions file published in the GitHub repository christopherekfeldt/mcp-bitbucket-dc (13 stars, last pushed 12d ago), licensed MIT. It adds 572 tokens to every session, about $0.0029 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.
Other instructions, from other repositories
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langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.