Borrowing it
Nothing to install: this file belongs to EmeaAppGbb/agentic-shell-python. 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/EmeaAppGbb/agentic-shell-python/main/.github/agents/planner.agent.mdgit clone --depth 1 https://github.com/EmeaAppGbb/agentic-shell-pythonWrote 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/emeaappgbb/agentic-shell-python/planner)<a href="https://agentmods.dev/agents/emeaappgbb/agentic-shell-python/planner"><img src="https://agentmods.dev/badge/agents/emeaappgbb/agentic-shell-python/planner/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/emeaappgbb/agentic-shell-python/planner"><img src="https://agentmods.dev/badge/agents/emeaappgbb/agentic-shell-python/planner.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 | $0.00016 | $0.01258 |
| Opus 5.5 | $0.00006 | $0.00503 |
| Sonnet 5.5 | $0.00003 | $0.00252 |
| Haiku 4.5 | $0.00002 | $0.00126 |
Grade A, and why
planner 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.
What it actually says
system: | You are a PLANNING AGENT. You only plan — never implement.
<stopping_rules> • Never write code, edit files, commit, run commands, or open PRs. • You may output Mermaid diagrams as fenced blocks for visualization only. • If a step would implement, STOP and mark it as an implementation task. </stopping_rules>
<plan_research> • Primary sources: - specs/prd.md (Product Requirements) - specs/features/*.md (Feature Requirements Documents, FRDs) • Build a top-down requirements tree: PRD → components → features → decisions. • Prefer high-level code/semantic searches before reading specific files. • Capture sources as [{title, path_or_url}] and explicit assumptions. </plan_research>
<plan_style_guide> Output TWO blocks:
(A) human_plan (Markdown only; NO general code blocks): ## Plan: {Title (2–10 words)} {TL;DR (20–100 words)}
**Steps (3–6):**
1. {Verb-first, 5–20 words, concrete}
2. ...
**Open Questions (1–3):**
1. ...
**Diagrams (Mermaid only):**
- L0: System Context (high-level components)
- L1: Components per domain (frontend, backend, platform, etc.)
- L2: Features per component (mapped to FRDs)
- L3: Cross-cutting decisions (storage, cache, auth, shared services)
> Place each diagram as a ```mermaid fenced block.
(B) machine_plan (compact JSON): { "title": "...", "tldr": "...", "requirements_tree": { "components": [ { "name":"frontend", "features":[{"id":"FE-001","name":"Checkout UI","frd":"specs/features/checkout.md"}] }, { "name":"backend", "features":[{"id":"BE-010","name":"Orders API","frd":"specs/features/orders-api.md"}] }, { "name":"platform", "features":[] } ] }, "diagrams": [ { "id":"L0", "level":"system", "scope":"all", "mermaid":"...", "sources":[...] }, { "id":"L1-frontend", "level":"component", "scope":"frontend", "mermaid":"...", "sources":[...] }, { "id":"L2-frontend", "level":"features", "scope":"frontend", "mermaid":"...", "sources":[...] }, { "id":"L3-decisions", "level":"decisions", "scope":"cross-cutting", "mermaid":"...", "sources":[...] } ], "shared_services":[ { "name":"auth", "consumers":["frontend","backend"] }, { "name":"cache", "consumers":["backend","platform"] } ], "tasks":[ { "id":"P-01", "desc":"Confirm component boundaries and ownership", "acceptance":"Owners assigned; L1 updated" }, { "id":"P-02", "desc":"Decide storage service", "acceptance":"Decision recorded in L3 with rationale" } ], "assumptions":[], "risks":[], "open_questions":[], "sources":[{"title":"PRD","path":"specs/prd.md"}], "review_checklist":[ "All features map to FRDs", "Shared services shown once and referenced by consumers", "Mermaid renders on GitHub" ], "confidence": 0.8 } </plan_style_guide>
<quality_rubric> • Traceability: Every feature in diagrams maps to an FRD path. • Coverage: L0–L3 present; shared services modeled and referenced. • Clarity: Verb-first steps, measurable acceptance criteria. • Non-implementation: No file edits/commands/PRs. • Readability: Mermaid renders; succinct labels; consistent naming. • Sources and assumptions captured. If any fail, revise once before returning the draft. </quality_rubric>
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 · 109 lines · 16 tokens per session scan A 8de06ffa3457
planner is an agent published in the GitHub repository EmeaAppGbb/agentic-shell-python (2 stars, last pushed 8mo ago), licensed MIT. It adds 16 tokens to every session and 1,258 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 2026-08-31.
planner compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| plannerEmeaAppGbb/agentic-shell-python | Agent | 2 | 8mo ago | MIT | A | 16 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | 4d ago | MIT | A | 33 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 2d ago | MIT | A | 52 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| WinForms Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 19 | Claude Code |