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 agentmods add skills/evoclaw/amplify/results-integrationnpx skills add EvoClaw/amplify --skill results-integrationgit clone --depth 1 https://github.com/EvoClaw/amplifyWrote 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/evoclaw/amplify/results-integration)<a href="https://agentmods.dev/skills/evoclaw/amplify/results-integration"><img src="https://agentmods.dev/badge/skills/evoclaw/amplify/results-integration.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 | $0.00028 | $0.07426 |
| Opus 5 | $0.00014 | $0.03713 |
| Sonnet 5 | $0.00006 | $0.01485 |
| Haiku 4.5 | $0.00003 | $0.00743 |
Grade A, and why
results-integration 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 6d 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 — 696 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Results Integration (Phase 5)
Overview
Raw results are not a paper. This skill transforms completed experiments and analyses into a structured, evidence-backed content plan that the user reviews before any writing begins. It bridges execution (Phase 4) and paper writing (Phase 6).
On-Demand Literature Search (Active Throughout Phase 5)
During results integration, you may need to find related work to contextualize unexpected findings, identify papers that support or contradict your claims, or locate comparison points. Search immediately when needed. Add new papers to docs/02_literature/paper-list.md with tag [Found during Phase 5].
If search fails or returns insufficient results:
- Try alternative search terms (synonyms, broader/narrower scope, different field terminology)
- If still insufficient → note what was searched and what's missing in
docs/02_literature/paper-list.md - Ask the user: "I need papers on [topic] but couldn't find them via [methods tried]. Can you point me to relevant work?"
- Do NOT block on literature — proceed with available information and mark claims with
[citation needed]in the argument blueprint
Core principle: Organize, interpret, verify — then and only then, outline.
Before organizing results, answer this question honestly:
"Do these results contain at least one finding that a domain expert would NOT have predicted before seeing the data?"
For Type C (Tool), the equivalent question is: "Does this tool demonstrably solve a real problem that existing tools cannot (or do significantly worse)?" If the tool is merely "another option" with no clear advantage, it's not publishable.
If the answer is NO — if every finding confirms what was already known (e.g., "PBMC data contains T cells and B cells"), or the tool offers no clear advantage over existing tools — then the results are NOT ready for a research paper. Options:
- Deepen the analysis — return to Phase 4 for additional analyses that might reveal unexpected patterns
- Change the angle — re-examine the data for findings that ARE surprising or novel
- Honestly downgrade — tell the user: "The current results would be better suited for a [lower tier venue / technical note / blog post] because they confirm known findings without adding new insight"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 696 lines · 28 tokens per session scan A 18ded61d1850
results-integration is a skill published in the GitHub repository EvoClaw/amplify (12 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 7,426 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 2026-08-30.
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