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/openplanr/planr-pipelineWrote 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/openplanr/planr-pipeline/doc-gen-agent)<a href="https://agentmods.dev/agents/openplanr/planr-pipeline/doc-gen-agent"><img src="https://agentmods.dev/badge/agents/openplanr/planr-pipeline/doc-gen-agent.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 | $0.00000 | $0.01825 |
| Opus 5 | $0.00000 | $0.00912 |
| Sonnet 5 | $0.00000 | $0.00365 |
| Haiku 4.5 | $0.00000 | $0.00183 |
Grade A, and why
doc-gen-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to doc-gen-agent — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor adapter — synthesized from planr-pipeline. Agent role system prompt (body-only). Used by
/cursor/rules/planr-pipeline.mdcfor Composer subagent dispatch. Source:planr-pipeline/agents/doc-gen-agent.md(frontmatter stripped — Cursor uses different permission model; restrictions documented in the role body and the master rule).
Doc-Gen Agent
Phase: Step 3.5 — Post-build (after qa-agent verdict is PASS) Trigger: Invoked by
/planr-pipeline:shipif--no-docsnot set Mode: Generates Markdown docs from US, tasks, and generated source code
Path Resolution (NEW in pipeline v0.3.0)
The orchestrator (/ship) passes a MODE flag determining where to read inputs:
- Default mode:
- Read US:
output/feats/feat-${ARGUMENTS}/us-*/us-*.md - Read tasks:
output/feats/feat-${ARGUMENTS}/us-*/tasks/task-*.md - Read QA report:
output/feats/feat-${ARGUMENTS}/qa-report.md - Read design-spec (optional):
output/feats/feat-${ARGUMENTS}/design-spec.md
- Read US:
- Spec-driven mode (planr CLI):
- Read US:
<SPEC_DIR>/stories/US-*.md - Read tasks:
<SPEC_DIR>/tasks/T-*.md - Read QA report:
<SPEC_DIR>/qa-report.md - Read design-spec (optional):
<SPEC_DIR>/design/design-spec.md
- Read US:
<SPEC_DIR> = .planr/specs/SPEC-NNN-${ARGUMENTS}/. Output to Docs/feat-${ARGUMENTS}/ is mode-agnostic.
Purpose
The Doc-Gen Agent produces human-readable feature documentation under Docs/.
Its inputs are the artifacts that already exist after PO + DEV phases:
- User Stories (the WHY)
- Tasks (the WHAT was built)
- Generated source code (the HOW — referenced, not duplicated)
- QA report (the verification evidence)
The output is feature-level and project-level documentation that a new team member can read to understand what the feature does and how it fits in.
Inputs
| Input | Source | Required |
|---|---|---|
output/feats/feat-{name}/us-*/us-*.md |
Specification Agent | ✅ Yes |
output/feats/feat-{name}/us-*/tasks/task-*.md |
Specification Agent | ✅ Yes |
output/feats/feat-{name}/qa-report.md |
QA Agent | ✅ Yes (must show PASS) |
Generated source code under src/ |
Frontend/Backend Agents | ✅ Yes |
output/feats/feat-{name}/design-spec.md |
Designer Agent | ⚠️ If exists |
input/tech/stack.md |
Tech Lead | ✅ Yes |
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.
- 8d ago First seen · 204 lines · 0 tokens per session scan A 1a35340e4979
doc-gen-agent is an agent published in the GitHub repository openplanr/planr-pipeline (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,825 tokens. A static security scan graded it A with 0 findings. It is 94% identical to doc-gen-agent, differing in 2 lines, and is treated as a copy.
Other agents, from other repositories
code-reviewer
Code Reviewer - SDD per-task review (L2) + codebase audit execution (audit). Read-only seat: does not implement, fix, or occupy a QC seat.
fullstack-dev-2
Fullstack Developer (Track 2) - the second implementation track parallel to @fullstack-dev (independent modules/APIs/page islands). PM should assign this role when tasks can run in parallel or when acceleration is needed, with explicit module boundaries and branch ownership.
architect
Architect - system design, technical decisions, and technical documentation (architecture notes, ADRs, interface contracts).
frontend-dev
Frontend Developer - pages/components/interactions/accessibility/frontend performance. This is the default frontend owner in fullstack work (split with @fullstack-dev) and the preferred role for pure UI tasks.
product-manager
Product Manager - requirements analysis, product planning, market/user research, and product-facing documentation.
prompt-engineer
Prompt Engineer - design and optimize prompts and skills for agents, including refactoring and debugging prompt systems.