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 skills add golemfoundation/octant-council-builder --skill document-agentgit clone --depth 1 https://github.com/golemfoundation/octant-council-builderWrote 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/skills/golemfoundation/octant-council-builder/document-agent)<a href="https://agentmods.dev/skills/golemfoundation/octant-council-builder/document-agent"><img src="https://agentmods.dev/badge/skills/golemfoundation/octant-council-builder/document-agent/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/skills/golemfoundation/octant-council-builder/document-agent"><img src="https://agentmods.dev/badge/skills/golemfoundation/octant-council-builder/document-agent.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 | $0.00008 | $0.00705 |
| Opus 5 | $0.00004 | $0.00352 |
| Sonnet 5 | $0.00002 | $0.00141 |
| Haiku 4.5 | $0.00001 | $0.00071 |
Grade A, and why
document-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 10d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Agent Documentation
Create a documentation page for a council agent, covering what it does, how it works, and how to customize it.
Input
$ARGUMENTS is the agent name (e.g., data-audits, eval-governance, synth-debate).
Process
Step 1: Read inputs
Read agents/$ARGUMENTS.md → agent definition
Read research/$ARGUMENTS.md → domain context (if exists)
Extract from the agent definition:
- Name and description (frontmatter)
- Wave type (from prefix: data/eval/synth)
- Process steps
- Dimensions or sources
- Output format
Step 2: Write documentation
Write docs/$ARGUMENTS.md:
# $ARGUMENTS
**Wave:** [1 — Data Gathering | 2 — Evaluation | 3 — Synthesis]
**Role:** [description from frontmatter]
## What This Agent Does
[2-3 sentences explaining the agent's purpose in plain language. What question does it answer? What data does it produce or what judgment does it render?]
## [Data Sources | Scoring Dimensions | Synthesis Method]
[For data agents: table of sources with URLs and what each provides]
[For eval agents: table of dimensions with descriptions and score calibration]
[For synth agents: description of synthesis methodology]
### [Source/Dimension 1]
[1-2 sentences on what this covers and why it matters]
### [Source/Dimension 2]
...
## Output
The agent writes its output to:
- **Data agents:** `council-out/{project}/data/{name}.md`
- **Eval agents:** `council-out/{project}/eval/{name}.md`
- **Synth agents:** `council-out/{project}/REPORT.md`
### Example Output Structure
[Show the key sections of the output format from the agent definition, condensed]
## Customization
### Changing [Sources | Dimensions | Method]
Edit `agents/$ARGUMENTS.md` and modify the [sources table | dimensions table | synthesis section].
### Common Modifications
- [Modification 1: e.g., "Add a new data source by adding a row to the Sources table and a search step in the Process"]
- [Modification 2: e.g., "Change score calibration by editing the calibration table at the bottom"]
- [Modification 3: e.g., "Adjust dimension weights by modifying the composite score calculation"]
## Dependencies
- **Reads from:** [what data this agent needs — e.g., "All Wave 1 data files" or "N/A for data agents"]
- **Consumed by:** [what reads this agent's output — e.g., "All Wave 2 evaluators" or "synth-chair"]
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.
- 10d ago First seen · 105 lines · 8 tokens per session scan A 292c9eae3354
document-agent is a skill published in the GitHub repository golemfoundation/octant-council-builder (3 stars, last pushed 5mo ago), licensed MIT. It adds 8 tokens to every session and 705 once invoked, about $0.0000 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 skills, from other repositories
great_cto
Use when the CTO describes a feature, task, or project goal. Orchestrates the full SDLC pipeline automatically based on project type.
anti-patterns
Catalogue of known SDLC anti-patterns that greatcto agents must actively reject when reviewing architecture, plans, code, or post-mortems. Used by architect (pre-impl), pm (planning), senior-dev (impl), l3-support (post-incident).
cost-model
Standardized cost-estimation framework for greatcto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.
migration-ready-schema
Data-model rules that make a schema importable from day one, so the migration-import-engineer is never blocked on missing columns. Every SMB Product-Builder product must let a customer bring their data from an incumbent (ServiceTitan/Toast/Mindbody/Shopify) — that requires provenance (sourceref) and rollback…
stack-baseline
The pinned default technology stack for SMB Product-Builder products. One source of truth so the architect, app-scaffolder, auth-engineer, and senior-dev never re-decide the stack per build — they build ON it. Covers framework, ORM/DB, auth, UI, payments/email/SMS, files, jobs, testing, hosting, and observability…
agent-workflow-playbook
AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4…