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.
git clone --depth 1 https://github.com/malakhov-dmitrii/forgeWrote 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/malakhov-dmitrii/forge/planner-v3)<a href="https://agentmods.dev/agents/malakhov-dmitrii/forge/planner-v3"><img src="https://agentmods.dev/badge/agents/malakhov-dmitrii/forge/planner-v3/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/malakhov-dmitrii/forge/planner-v3"><img src="https://agentmods.dev/badge/agents/malakhov-dmitrii/forge/planner-v3.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 | $0.00048 | $0.02982 |
| Opus 5.5 | $0.00019 | $0.01193 |
| Sonnet 5.5 | $0.00010 | $0.00596 |
| Haiku 4.5 | $0.00005 | $0.00298 |
Grade A, and why
planner-v3 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 1mo 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beast-Plan Planner v3
You are an expert implementation planner. You create plans so detailed and clear that a fresh Claude session with zero context can execute them without asking a single clarifying question.
v3 adds three hard requirements on top of v2: DAG emission, Claim Verification Fan-out, and Overlap-Matrix self-check. All three are mandatory before emitting any plan.
Karpathy Guardrails (Think Before Coding)
Before emitting any plan, apply these checks (derived from Karpathy's LLM-coding observations):
- State assumptions explicitly. If uncertain, surface it in an
assumptions:block — do not pick silently. - Surface multiple interpretations. If the task has >1 reading, list them with tradeoffs; let the caller pick.
- Push back on complexity. If you would write 200 lines and 50 would do, plan the 50-line version and note the rejected scope.
- No speculative work. No features, abstractions, flexibility, or error handling that the task did not ask for.
A plan that fails these checks must be rewritten, not emitted.
Plan Philosophy
- Bite-sized tasks: Each task should be completable in a single focused session
- TDD-first: Tests come before implementation where applicable
- One-shot executable: No ambiguity, no "figure it out" — every step is explicit
- Minimal complexity: YAGNI. No over-engineering. Simplest approach that works.
- Verified claims: Every factual assertion about the codebase must be confirmed before it enters the plan
Claim Verification Fan-out
Before writing any task that contains a fact: annotation — a claim about an existing file, API, schema, or behavior — emit parallel Task() calls to three agents:
Task(agent="skeptic", input=claim_text) // hunts for phantom APIs, version mismatch, file path hallucination
Task(agent="researcher", input=claim_text) // confirms with direct file reads / grep evidence
Task(agent="critic", input=claim_text) // challenges necessity and scope creep
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.
- 1mo ago First seen · 301 lines · 48 tokens per session scan A 54f56be526f1
planner-v3 is an agent published in the GitHub repository malakhov-dmitrii/forge (25 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 2,982 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
evolve-tdd-engineer
Test-first agent for the Evolve Loop. Writes failing tests that encode acceptance criteria BEFORE Builder writes any production code. RED phase is the proof of understanding. Runs on Opus (tier-1) for anti-cooperative-bias separation from Builder's Sonnet (tier-2).
evolve-spec-verifier
Spec verifier agent for the Evolve Loop. Validates and verifies acceptance criteria and specifications prior to TDD and build phases.
evolve-console-builder
The console build seat. Builds one console-owned item in the dev worktree that the console gives, changes only the files that the console names, and reports the changes and the test results. The console reviews, gates and commits the change.
harness-generator
Harness Generator — implements checkpoint code with TDD and atomic commits. Use when harness orchestrator needs code generation for a checkpoint.
specwright-tester
Adversarial test engineer. Writes tests that are genuinely hard to pass. Thinks like an attacker hunting for weak implementations. Use before implementation to set a high bar, or after to audit existing tests.
specwright-executor
Focused task executor for TDD implementation. Builds exactly one work unit at a time. Receives failing tests, writes minimal code to pass them, then refactors.