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.
npx agentmods add rules/golid-ai/golid/plan-execution-loopgit clone --depth 1 https://github.com/golid-ai/golidWhat 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 | $0.00029 | $0.01888 |
| Opus 5 | $0.00015 | $0.00944 |
| Sonnet 5 | $0.00006 | $0.00378 |
| Haiku 4.5 | $0.00003 | $0.00189 |
Grade A, and why
plan-execution-loop 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 2d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Execution Loop
Thesis: One plan slice at a time — implement, evidence-based audit (≥90/100, file:line proof required), fix, re-audit — then next slice. Parent agent orchestrates; subagents implement and audit.
Invoke when: User says "run the plan", "execute slice X", or points at
docs/plans/*.mdfor implementation.
Per-slice loop (do not skip)
1. IMPLEMENT — Task subagent: read plan slice only; implement; run verification commands; update Progress row
2. AUDIT — Task subagent (readonly): grade /100 vs slice acceptance with file:line evidence; PASS only if ≥90 and every criterion has proof
3. FIX — if FAIL: Task subagent applies audit fix list; no scope creep
4. RE-AUDIT — repeat 2–3 until ≥90 with full evidence
5. NEXT SLICE — only after current slice PASS
Subagent prompts must include
- Exact slice ID and file path to plan section
- Acceptance criteria from plan (copy verbatim)
- Verification commands (
npm run test:coverage,check_spec_drift.sh, etc.) - Forbidden scope (other slices, unrelated refactors)
- Evidence rule: every deduction needs
path:linecitation; every PASS criterion needspath:lineproof or automatic FAIL - Fresh grade (audit dispatches only): grade the artifact as it exists now — start at 100, deduct only for current gaps with
path:lineproof. Do not reward "improvement since last pass", prior fix lists, or Progress table history. Parent must not anchor with prior scores (see Re-audit below). - "Return: grade, PASS/FAIL, evidence table, fix list, files changed, blockers"
Evidence-based audit (mandatory)
Audit subagents grade evidence, not vibes. A numeric score without citations is invalid.
For every deduction — cite path:line (or path:line-line for a range) showing the gap. Example: -5 Scope: backend/internal/handler/foo.go:42 — touches bar.go, outside slice file list.
For every slice acceptance criterion — cite path:line (or command output) proving it is met. If the auditor cannot find proof after reading the diff and running verification commands, that criterion is automatic FAIL regardless of the running score.
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.
- 2d ago First seen · 146 lines · 29 tokens per session scan A 6490a45c7972
plan-execution-loop is a cursor rule published in the GitHub repository golid-ai/golid (40 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 1,888 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 2026-08-30.
Other cursor rules, from other repositories
us-prioritization
Cursor rule "us-prioritization" from KaelSensei/MagicAIBuilder, covering set to false so it only applies when relevant, user story prioritization & weighting, story points (planning poker), what it is and why fibonacci?.
wize-sprint-status
4-implementation: Sprint Status.
project-onboarding-rule
Automatically onboards existing projects into the AI-driven development workflow.
linear-in-review
After finishing a Linear issue, set its status to In Review (never Done).
sprint-planning
BMAD BMM Agent: sprint-planning.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.