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/genai-jerry/claude-software-factoryWrote 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/commands/genai-jerry/claude-software-factory/planner)<a href="https://agentmods.dev/commands/genai-jerry/claude-software-factory/planner"><img src="https://agentmods.dev/badge/commands/genai-jerry/claude-software-factory/planner.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01129 |
| Opus 5 | $0.00010 | $0.00564 |
| Sonnet 5 | $0.00004 | $0.00226 |
| Haiku 4.5 | $0.00002 | $0.00113 |
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 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Planner of the Software Factory (see FACTORY.md).
Input: an epic issue number labelled factory:spec-approved: $ARGUMENTS
Mission
Decompose the approved spec into an ordered, releasable task breakdown.
Steps
-
Resolve the epic's home branch (FACTORY.md §6/§6a): read
.github/factory-branches.json. When itsepicsistrue, that branch isfactory/epic-<issue-number>— cut it from the repo's default branch if it is not on the remote yet (git push origin origin/<default>:refs/heads/factory/epic-<issue-number>, a no-op if it exists) and check it out. You run before gate G2, so no task has been dispatched and nothing can be stranded off a branch cut now; this is what adopts an epic whose spec gate was closed by a human merging the PR and applying the label, which reaches no gate-approval path. Whenepicsisfalseor the key/file is missing, the home branch is the repo's integration branch (its name is the profile'sbranches.stagingwhen that is a non-null string, else the policy'sstaging, else"staging"), and no epic branch is created.Then find the change folder, which is authoritative wherever it actually is: check out the first of the epic branch, the integration branch and the default branch that carries
openspec/changes/<issue-number>-*/. An epic whose spec merged to the default branch under an older routing still has its folder there, and reading only the branch the policy names would hand you an empty checkout. Work from the branch you found it on, and readproposal.mdandspecs/. -
Write
tasks.mdin the change folder:- Each task = exactly one PR, ≤ half a day of work.
- Max ~10 tasks. If the epic needs more, split into sequential changes
(
<issue>-<slug>-part2, ...) and say so on the epic. - Order so every intermediate merge is releasable. The general shape is
schema/data-model change → the repo that owns the contract → the repos
that consume it; derive the actual chain from each repo's
.factory/profile.jsonestate_role. - Each task names its repo, its dependencies, and the spec scenarios it serves. That task → scenario mapping is not just bookkeeping: where system tests are on (FACTORY.md §4b) the Test Planner reads it to work out which tasks each system test case depends on, so a task with no scenarios named is a case that will be released too early.
-
Mirror
tasks.md1:1 into GitHub sub-issues (in each task's target repo, for cross-repo epics): titletask(<epic>): <task name>, body links the change folder and lists dependencies — machine-readable, one marker per line:Blocked by #Nfor a same-repo dependency,Blocked by <owner>/<repo>#Nwhen the dependency lives in a sibling repo. A sub-issue created in a sibling repo also carriesPart of <owner>/<repo>#<epic>(the epic's own repo) so tooling can find its parent. No spec content in the body. -
Create/update the milestone; post the task tree as a checklist comment on the epic.
-
Commit
tasks.mdto the change folder on branchfactory/<issue-number>-design— create it, if it doesn't exist, from the branch you found the change folder on in step 1 — and open a draft PR titleddesign(<issue>): <slug>, based on that same branch. Never base it on the default branch while an integration branch exists (§6). The Architect will adddesign.mdto this same branch and mark the PR ready — one PR, one G2 approval. Removefactory:spec-approved, applyfactory:planned.
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 Changed · +24 lines 0651da25a4b6
- 8d ago First seen · 52 lines · 19 tokens per session scan A ceeeaf9d29c6
planner is a command published in the GitHub repository genai-jerry/claude-software-factory (2 stars, last pushed 4d ago), licensed MIT. It adds 19 tokens to every session and 1,129 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.
Other commands, from other repositories
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sync-linear
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add-note
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fest-show
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dispatcher
Pick the next-best repo to work on across the portfolio — rank free repos, recommend one, claim its lease atomically, and route to the entry command.
workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.