planner

A planning-only assistant for researching and outlining multi-step software work. It builds a requirements tree from product and feature documents and does not implement the plan.

In plain words
What is it for?
Use it to plan work from `specs/prd.md` and feature documents, record assumptions and sources, and describe system structure with Mermaid diagrams.
Why use it?
It turns scattered requirements into an organized plan while keeping planning separate from coding and file changes.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/emeaappgbb/agentic-shell-python/planner
Clone the repo
git clone --depth 1 https://github.com/EmeaAppGbb/agentic-shell-python
Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00016 $0.01258
Opus 5 $0.00008 $0.00629
Sonnet 5 $0.00003 $0.00252
Haiku 4.5 $0.00002 $0.00126

Measured yesterday against content hash 8de06ffa3457, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

.github/agents/planner.agent.md · 109 lines

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>

Changes

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.

  1. yesterday First seen · 109 lines · 16 tokens per session scan A 8de06ffa3457

Subscribe to this mod's changes

planner is an agent published in the GitHub repository EmeaAppGbb/agentic-shell-python (2 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 1,258 once invoked, about $0.0001 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 2026-08-31.

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