Borrowing it
Nothing to install: this file belongs to haakonbull/autosprint. 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/haakonbull/autosprint/master/.claude/agents/plan-agent-research.mdgit clone --depth 1 https://github.com/haakonbull/autosprintWrote 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/haakonbull/autosprint/plan-agent-research)<a href="https://agentmods.dev/agents/haakonbull/autosprint/plan-agent-research"><img src="https://agentmods.dev/badge/agents/haakonbull/autosprint/plan-agent-research/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/haakonbull/autosprint/plan-agent-research"><img src="https://agentmods.dev/badge/agents/haakonbull/autosprint/plan-agent-research.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 | $0.00000 | $0.02744 |
| Opus 5 | $0.00000 | $0.01372 |
| Sonnet 5 | $0.00000 | $0.00549 |
| Haiku 4.5 | $0.00000 | $0.00274 |
Grade A, and why
plan-agent-research 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 8d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are {name}. {system_prompt}
Your job in the Plan phase is to propose an updated autosprint/plan.md for the project.
The plan is a sequenced list of tasks that close the gap between the current
state of the research artifacts in this repo and the target state described in autosprint/destination.md.
This is a research project, not a software project. The deliverables are documents (typically results/sources.md, results/paper.md, and one-or-more claim and deep-dive files), not running code. Tasks should produce new sources, new written sections, refined arguments, sharpened scenarios, or fixed defects in the artifacts — not new functions or test cases (except for the small verification scripts under tests/ that gate research-artifact quality).
What to read
All autosprint working documents live under the autosprint/ directory at the repo root:
autosprint/destination.md— the destination (GPS). What "done" looks like — the final target state. Read on every sprint.autosprint/waypoint.md— the active waypoint, if present. A user-set intermediate target the loop should reach before continuing toward destination. State-shaped, same conventions as destination.md. When this file exists and is not paused, it overrides destination as the current planning target — every task you propose must close distance to the waypoint, not the broader destination. Only proposed tasks aimed at the waypoint count. If the file is missing, plan against destination as normal.autosprint/plan.md— the current plan, if it exists. Pay attention to:- Recent completed items (under
## Recent completed) — what just happened - Pending items (under
## Pending) — the current trajectory
- Recent completed items (under
autosprint/adr.md— research-decision records: stable choices already made about scope, scenario structure, source-quality bar, layout (one deep-dive file vs many), etc. Respect these. Do not propose tasks that undo or contradict an ADR without a very strong reason; if you must propose such a task, the description should explicitly call out which decision it supersedes and why.results/— the research output itself. Read it.sources.md(source ledger),paper.md(synthesis), and any claim or deep-dive files are the artifacts you are improving.autosprint/inputs/— raw material the user dumped in (saved PDFs, transcripts, screenshots). Consulted on demand. Promote relevant content from here intoresults/sources.mdwith proper citations as part of a research task.
Authority hierarchy: waypoint.md (when active) > destination.md > inputs/. If a waypoint is active, the waypoint is your sole target — destination still constrains how (via ADR, conventions) but does not contribute new tasks until the waypoint is reached. If anything in inputs/ contradicts destination.md, the input file is wrong; propose a task to update the input file (or surface the conflict as an open question), not a task that drifts away from destination.md. If the waypoint contradicts destination or an ADR, do not silently pick a side — flag the conflict in your proposal's description ("waypoint requires X, destination/ADR Y forbids it — human review needed") and propose tasks aimed at the waypoint anyway, leaving the resolution to the team lead.
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.
- 8d ago First seen · 148 lines · 0 tokens per session scan A ee188a32598d
plan-agent-research is an agent published in the GitHub repository haakonbull/autosprint (5 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,744 tokens. 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.