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/secondary-research-sprint/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/secondary-research-sprint)<a href="https://agentmods.dev/skills/endogenai/dogma/secondary-research-sprint"><img src="https://agentmods.dev/badge/skills/endogenai/dogma/secondary-research-sprint/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/secondary-research-sprint"><img src="https://agentmods.dev/badge/skills/endogenai/dogma/secondary-research-sprint.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 | $0.00055 | $0.01891 |
| Opus 5 | $0.00028 | $0.00945 |
| Sonnet 5 | $0.00011 | $0.00378 |
| Haiku 4.5 | $0.00006 | $0.00189 |
Grade A, and why
secondary-research-sprint 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 11d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Secondary Research Sprint
Governing axiom: Endogenous-First (MANIFESTO.md § 1)
This skill encodes a 5-step procedure for executing secondary research sprints on bare-bones GitHub issues that contain only a title and a URL. All steps are governed by AGENTS.md § Agent Communication and § Programmatic-First Principle. Read AGENTS.md before modifying any step.
When to use: An issue arrives with just a URL and no acceptance criteria, no summary, and no context. This skill enriches the issue first, checks the existing corpus for coverage, scouts the target source, synthesizes a D4 doc, and archives the result.
When NOT to use: Full multi-source research topics (use deep-research-sprint); issues that already have a detailed brief; single-source lookups that don't warrant a D4 doc.
Step 1 — Issue Enrichment (before anything else)
Fetch the URL in the issue body and rewrite the issue with structured context before any research begins.
Check before fetch (Algorithms-Before-Tokens: avoid re-downloading cached content):
uv run python scripts/fetch_source.py <url> --check
Fetch if not cached:
uv run python scripts/fetch_source.py <url>
Extract from the fetched content:
- Title
- Abstract or summary (3–5 sentences)
- Key claims (3–5 bullets)
- Any recommended follow-up reads
Write enriched issue body to a temp file (using create_file or inline write — never heredoc):
Body template:
# <Title from source>
**Source:** <URL>
**Fetched:** <date>
## Summary
<3-5 sentence summary>
## Key Claims
- <claim 1>
- <claim 2>
- ...
## Relevance to dogma
<1-2 sentences on why this matters for the project>
## Acceptance Criteria
- [ ] D4 synthesis doc committed at `docs/research/<slug>.md`
- [ ] Issue closed with `Closes #<num>` in PR or via `gh issue close`
- [ ] `validate_synthesis.py` passes on the output doc
Validate before posting:
test -s /tmp/issue_<num>_enriched.md && file /tmp/issue_<num>_enriched.md | grep -q "UTF-8\|ASCII"
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.
- 11d ago First seen · 220 lines · 55 tokens per session scan A aff1a0e334a7
secondary-research-sprint is a skill published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 17d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,891 once invoked, about $0.0003 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.
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