Borrowing it
Nothing to install: this file belongs to matrixorigin/memoria. 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/matrixorigin/memoria/main/.kiro/steering/goal-driven-evolution.mdgit clone --depth 1 https://github.com/matrixorigin/memoriaWrote 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/instructions/matrixorigin/memoria/goal-driven-evolution)<a href="https://agentmods.dev/instructions/matrixorigin/memoria/goal-driven-evolution"><img src="https://agentmods.dev/badge/instructions/matrixorigin/memoria/goal-driven-evolution/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/instructions/matrixorigin/memoria/goal-driven-evolution"><img src="https://agentmods.dev/badge/instructions/matrixorigin/memoria/goal-driven-evolution.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 | $0.01375 | $0.01375 |
| Opus 5 | $0.00687 | $0.00687 |
| Sonnet 5 | $0.00275 | $0.00275 |
| Haiku 4.5 | $0.00137 | $0.00137 |
Grade A, and why
memoria goal-driven-evolution.md 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 4d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal-Driven Evolution + Plan Integration
Track goals, plans, progress, lessons, and user feedback across conversations. Integrates with plan panels (Kiro Shift+Tab, Cursor Composer, Claude multi-step).
Before Starting Any Multi-Step Task
Query memory first:
memory_search(query="GOAL [topic]") # existing related goals
memory_search(query="LESSON [topic]") # past learnings
memory_search(query="CORRECTION ANTIPATTERN [topic]") # what NOT to do
If an active goal exists, continue it instead of creating a new one.
Register Goal
For multi-session work (skip for trivial single-session tasks < 3 steps):
memory_search(query="GOAL [keywords]")
memory_store(
content="🎯 GOAL: [description]\nSuccess Criteria: [measurable]\nStatus: ACTIVE\nCreated: [date]",
memory_type="procedural"
)
Plan & Execute
Store the plan, then track each step:
memory_store(content="📋 PLAN for GOAL [name]\nSteps:\n1. [step] — ⏳\nRisks: [risks]\nIteration: #1", memory_type="procedural")
# After each step — use working type (will be cleaned up later)
memory_store(content="✅ STEP [N/total] for GOAL [name] (#X)\nAction: [done]\nResult: [outcome]\nInsight: [learned]", memory_type="working")
memory_store(content="❌ STEP [N/total] for GOAL [name] (#X)\nAction: [tried]\nError: [wrong]\nRoot Cause: [why]\nNext: [adjust]", memory_type="working")
Only store non-obvious insights. Don't store "ran tests, passed".
For high-risk iterations, isolate on a branch:
memory_branch(name="goal_[name]_iter_[N]")
memory_checkout(name="goal_[name]_iter_[N]")
# work on branch... then validate and merge (see Iteration Review)
Capture User Feedback (immediately)
User corrections are highest-value — always store as procedural:
# User corrects direction
memory_store(content="🔧 CORRECTION for GOAL [name]: [old approach] → [corrected approach]. Reason: [why]", memory_type="procedural")
# User confirms something works well
memory_store(content="👍 FEEDBACK for GOAL [name]: [what worked]. Reuse: [when to apply again]", memory_type="procedural")
# User is frustrated — record what NOT to do
memory_store(content="⚠️ ANTIPATTERN for GOAL [name]: [what went wrong]. Rule: NEVER [this] again.", memory_type="procedural")
# User changes direction entirely
memory_correct(query="GOAL: [name]", new_content="🎯 GOAL: [name]\n...\nPivot: [old] → [new]. Reason: [why]", reason="User changed direction")
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.
- 4d ago First seen · 143 lines · 1,375 tokens per session scan A 835ecdec5e95
memoria goal-driven-evolution.md is an instructions file published in the GitHub repository matrixorigin/memoria (596 stars, last pushed 2d ago), licensed Apache-2.0. It adds 1,375 tokens to every session, about $0.0069 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 2026-09-08.
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