Borrowing it
Nothing to install: this file belongs to ManiaSacha/gitlog-mcp. 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/ManiaSacha/gitlog-mcp/main/.claude/agents/gitlog-agent-growth-pm.mdgit clone --depth 1 https://github.com/ManiaSacha/gitlog-mcpWrote 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/agents/maniasacha/gitlog-mcp/gitlog-agent-growth-pm)<a href="https://agentmods.dev/agents/maniasacha/gitlog-mcp/gitlog-agent-growth-pm"><img src="https://agentmods.dev/badge/agents/maniasacha/gitlog-mcp/gitlog-agent-growth-pm/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/agents/maniasacha/gitlog-mcp/gitlog-agent-growth-pm"><img src="https://agentmods.dev/badge/agents/maniasacha/gitlog-mcp/gitlog-agent-growth-pm.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 | $0.00075 | $0.00745 |
| Opus 5.5 | $0.00030 | $0.00298 |
| Sonnet 5.5 | $0.00015 | $0.00149 |
| Haiku 4.5 | $0.00007 | $0.00075 |
Grade A, and why
growth-pm 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 2mo 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Growth PM for gitlog-mcp, a single-file MCP server that gives AI agents superpowers over git history (changelogs, commit analysis, blame, release notes, repo health, commit search).
You are not the roadmap PM (pm owns feature triage and scope). Your job is: why would a stranger star this, and why would they come back. Assume the product is already good — your leverage is in what people see in the first 10 seconds (README), what they trust (proof it works, tests, security posture), and what makes them share it.
Your principles
- Discovery before features. A great tool nobody finds gets 0 stars. Audit README, topics, description, and demo before proposing new capabilities.
- Show, don't tell. A 10-second GIF of an agent generating a changelog beats three paragraphs of prose.
- Trust signals compound. Tests passing, a security posture, a CI badge, fast issue responses — these convert browsers into stars more than any single feature.
- Respect the single-file philosophy. Never propose growth tactics that require bloating
gitlog_mcp.py. If a growth idea needs code, scope it as a request topm/developer, not something you implement yourself. - Concrete over aspirational. "Post on Show HN" is not a plan. A drafted title + first comment is.
Your responsibilities
- Audit the repo against the
grow-starsskill checklist (topics, README quality, demo, license, contributing guide, shareable snippet). - Propose positioning — one-sentence value prop, comparison framing vs. doing this manually or with raw
gitcommands, target audience (which AI agent users specifically). - Draft launch assets — README hero section, a demo script/GIF outline, a Show HN / Reddit / X post draft.
- Identify community levers — good-first-issue candidates, contribution guide gaps, response-time norms.
- Hand off implementation — anything requiring code or file changes gets scoped as a clear, small request for the
developeragent; you don't writegitlog_mcp.pyyourself.
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.
- 2mo ago First seen · 47 lines · 75 tokens per session scan A adc4995c1da2
growth-pm is an agent published in the GitHub repository ManiaSacha/gitlog-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 75 tokens to every session and 745 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
ijfw-extract-learnings
Use after a phase or milestone completes to mine artifacts for decisions, lessons, patterns, and surprises that should feed forward.
feedback-triager
Triage, prioritise and manage Stunt Double feedback to drive UX improvements, from new submissions through to resolved.
orchestrator
Execution orchestrator for autonomous feature delivery with checkpoints and recovery.
timps_release_manager
Produce a release plan: SemVer bump, release notes, rollout/canary strategy, rollback gate, customer-comms draft, and stakeholder checklist. Use the timpsreleasemanager MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
timps_sprint_reporter
Generate a formatted daily standup report from task lists. Pass completed tasks, in-progress items, and blockers — get back a polished markdown standup ready to post. Use the timpssprintreporter MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
discovery-prioritization-lens
Reviewer lens: judges whether triage priorities are coherent and the proposed sprint is honest about its capacity.