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/danielvm-git/bigpowersWrote 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/rules/danielvm-git/bigpowers/run-planning)<a href="https://agentmods.dev/rules/danielvm-git/bigpowers/run-planning"><img src="https://agentmods.dev/badge/rules/danielvm-git/bigpowers/run-planning/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/rules/danielvm-git/bigpowers/run-planning"><img src="https://agentmods.dev/badge/rules/danielvm-git/bigpowers/run-planning.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 | $0.00064 | $0.01135 |
| Opus 5.5 | $0.00026 | $0.00454 |
| Sonnet 5.5 | $0.00013 | $0.00227 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
run-planning 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
story: e24s03
Run Planning
HARD GATE — Before running planning skills, confirm the epic capsule exists and the active story is clear. Planning without a target is noise.
Role: DISCOVER-PHASE ADVANCER — orchestrates the discover-phase sequence; hands off to the scope-work → slice-tasks → plan-work spine for implementation planning.
Updates specs/planning-status.yaml as discover-phase skills complete. This is NOT a duplicate of plan-work — it orchestrates the pre-coding discovery phase only (Discover phase in the 6-phase PMBOK lifecycle), handing off to the planning spine for implementation detail.
When to use
- Starting a brand-new feature or initiative with no prior planning artifacts
- Returning to a stalled initiative and needing to resume the discovery workflow
- After
orchestrate-projecthands off to the Discover phase - When a new epic emerges from
change-requestand needs to go through full discovery
Pre-flight
- Does
specs/planning-status.yamlexist? If not, create it with the default workflow keys. - Does
specs/state.yamlhaveactive_flow: planning? Set it if not already. - Is the epic identified in
release-plan.yaml? The epic must exist before discovery begins.
Workflows (default keys)
survey-context→scope-work→research-first→elaborate-spec(optional) →plan-release→slice-tasks
Each key maps to a skill invocation. Optional keys can be skipped; required keys must complete before the phase advances.
Process
-
Read state — Read
specs/planning-status.yamlandspecs/state.yaml. Understand where discovery stands: which workflow keys aredone, which arepending, and which areoptional(can be skipped). -
Find next step — Find the first workflow key with
status: pending. If the key isoptional, check if the user wants to run it. If not, mark itskipped.
2a. Context capsule check — Before invoking elaborate-spec, check whether a fresh specs/planning-context.yaml exists:
test -f specs/planning-context.yaml && python3 -c "
import yaml, datetime
d = yaml.safe_load(open('specs/planning-context.yaml'))
written = d.get('written_at','')
if written:
age = (datetime.datetime.now(datetime.timezone.utc) - datetime.datetime.fromisoformat(written)).total_seconds() / 3600
print(f'Context age: {age:.1f}h')
" 2>/dev/null || echo "No context or no written_at"
- If context is < 24h old, ask:
"Planning context from Xh ago exists for '<feature_name>'. Re-run elaborate-spec? [y/N]". Skip elaborate-spec on N. - If context is ≥ 24h old or absent, run elaborate-spec normally.
- On planning cycle completion (all required keys done), clear the capsule: delete
specs/planning-context.yamland setplanning-status.yamlcontext_capsule: null.
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 · 101 lines · 64 tokens per session scan A fd30df1f89b1
run-planning is a cursor rule published in the GitHub repository danielvm-git/bigpowers (267 stars, last pushed 27d ago), licensed MIT. It adds 64 tokens to every session and 1,135 once invoked, about $0.0003 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 2026-09-03.
Other cursor rules, from other repositories
payload-cms
Payload CMS: collections, fields, hooks, access control, admin UI.
monorepo
Monorepo: workspace references, shared packages, boundaries.
pnpm
PNPM: workspace monorepos, efficient package management.
ai-dev-os-ticket
Generates a ticket (local file or GitHub Issue) with implementation summary and checklist candidates derived from AI Dev OS guidelines. Used to create structured implementation tasks.
agent-workflow-orchestration
Mandatory PM-orchestrated subagent workflow — main agent is manager, no separate PM subagent.
12-new-project
Workflow for starting a new app from this template - rebranding, identity, cleanup and first feature.