Borrowing it
Nothing to install: this file belongs to EndogenAI/dogma. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/EndogenAI/dogma/main/.github/skills/research-epic-planning/SKILL.mdgit clone --depth 1 https://github.com/EndogenAI/dogmaWrote 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/endogenai/dogma/research-epic-planning)<a href="https://agentmods.dev/skills/endogenai/dogma/research-epic-planning"><img src="https://agentmods.dev/badge/skills/endogenai/dogma/research-epic-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/skills/endogenai/dogma/research-epic-planning"><img src="https://agentmods.dev/badge/skills/endogenai/dogma/research-epic-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 | $0.00132 | $0.02229 |
| Opus 5 | $0.00066 | $0.01115 |
| Sonnet 5 | $0.00026 | $0.00446 |
| Haiku 4.5 | $0.00013 | $0.00223 |
Grade A, and why
research-epic-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 9d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Epic Planning
This skill enacts the Endogenous-First axiom from MANIFESTO.md: before planning begins, the agent reads all prior research, existing issues, and endogenous sources — so the plan is scaffolded from known context, not guessed. Epic planning discipline is governed by AGENTS.md § Agent Communication → docs/plans/. Read both documents before deviating from any step.
Canonical template: docs/plans/2026-03-08-value-encoding-fidelity.md — the reference implementation of a well-formed research epic workplan.
1. When to Use This Skill
Use this skill when the session involves:
| Criterion | Threshold |
|---|---|
| Number of related research issues | ≥ 3 |
| Issues span multiple execution phases or agent types | Yes |
| A milestone exists or is being created | Yes |
| Future sessions need to pick up phases independently | Yes |
For a single research issue, use the deep-research-sprint skill instead.
2. Pre-Planning: Endogenous Source Gathering
Before writing a single line of the workplan, run this sequence. Record findings in the scratchpad under ## Epic Planning — Scope Analysis.
2.1 Read All Relevant Issues
# Get all open issues (with labels for effort/priority/type)
gh issue list --state open --json number,title,labels --limit 80
# Read the anchor issue or each milestone issue in full
gh issue view <ISSUE_NUMBER>
For each issue, note:
- Core research question or deliverable
- Stated dependencies (
Depends on:lines in body) - Effort label:
xs/s/m/l/xl - Type:
research/feature/chore/docs
2.2 Survey Existing Research Docs
ls docs/research/
Identify which docs/research/*.md docs are the primary endogenous sources. Read the abstract/executive summary of each relevant doc. Do not bulk-read the full corpus — targeted retrieval only.
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.
- 9d ago First seen · 224 lines · 132 tokens per session scan A 0cacb3b11eae
research-epic-planning is a skill published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 15d ago), licensed Apache-2.0. It adds 132 tokens to every session and 2,229 once invoked, about $0.0007 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
air-blackbox-sales-agent
AIR Blackbox's autonomous sales prospecting agent. Finds Python AI projects on GitHub that need EU AI Act compliance, identifies the right person to contact (CEO, CTO, lead maintainer), runs a free compliance scan, and drafts personalized outreach emails that convert to engagement. The sales flow: free scan as the…
interpret-results
Interprets AIR Blackbox scan results and maps findings to specific EU AI Act articles, recitals, and remediation steps. Use when the user has scan output and wants to understand what to fix, why it matters, or how to prioritize.
compliance-scan
Scans a Python AI project for EU AI Act compliance gaps using AIR Blackbox. Use when the user asks to check compliance, scan their code, audit their AI project, or mentions EU AI Act, Articles 9-15, or compliance checking.
nio-scan
Nio code/skill execution-risk scanner. Use when the user wants to scan a file, repo, directory, or skill for execution risks — e.g. "scan this code for risks", "is this file/plugin dangerous", "check this repo for malicious code", "run nio scan on ". Focused single-purpose skill; for other Nio operations use /nio.
nio-external-score
Nio external scoring-endpoint snapshot. Use when the user asks about their current Nio/risk scores from configured external scoring endpoints — e.g. "what's my Nio score", "tell me the nio score", "check my external/guardrail endpoint scores", "what do my scoring services return right now", "list the current risk…
constitutional-ai
Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.