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 shinpr/nautilus --skill recipe-reflectgit clone --depth 1 https://github.com/shinpr/nautilusWrote 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/shinpr/nautilus/recipe-reflect)<a href="https://agentmods.dev/skills/shinpr/nautilus/recipe-reflect"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/recipe-reflect/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/shinpr/nautilus/recipe-reflect"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/recipe-reflect.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 | $0.00021 | $0.00835 |
| Opus 5 | $0.00010 | $0.00417 |
| Sonnet 5 | $0.00004 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00084 |
Grade A, and why
recipe-reflect 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 4d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: Drive the feedback loop by reflecting on outcomes, updating target artifacts, and distilling learnings across the knowledge pyramid (see product-principles skill for Tier definitions).
Required Skills [LOAD BEFORE EXECUTION]
- [LOAD IF NOT ACTIVE]
product-principles— Knowledge Pyramid and promotion criteria - [LOAD IF NOT ACTIVE]
hypothesis-discipline— validation results, lifecycle status, and confidence changes
Delegate Level 2 and Level 3 distillation to knowledge-distiller for independent evidence synthesis.
Execution Decision Flow
1. Scope Assessment
Input: Use the path or text supplied with the explicit skill invocation. If no input was supplied and the target cannot be inferred unambiguously, ask for it.
Determine the reflection level (see references/reflection-guide.md):
| Trigger | Level | Target Files |
|---|---|---|
| Hypothesis concluded | Level 1: Hypothesis | The hypothesis file |
| Multiple hypotheses concluded under an Opportunity | Level 2: Opportunity | Opportunity file (Tier 2 Learnings section) |
| PRD delivered, quarterly review, strategic pivot | Level 3: Vision | docs/product/vision.md, docs/product/learnings.md |
2. Result Recording
Level 1: Hypothesis Reflection
- Verify the hypothesis file has been updated with results (validation results, confidence scores, evidence)
- Document a learning when the result changes the parent Opportunity or a later decision
- Check if this result changes understanding of the parent Opportunity
Level 2: Opportunity Reflection
- Start from the Opportunity and load the hypothesis evidence needed to assess candidate learnings and contradictions
- Prepare context for knowledge-distiller (hypothesis summaries, results, confidence changes)
Level 3: Vision Reflection
- Gather the cross-Opportunity evidence needed for the outcome, NSM, or Tier 1 decision
- Review Product Outcomes — are targets still correct?
- Review NSM — still the right connecting metric?
- Prepare context for knowledge-distiller
What ships with it
2 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.
- 4d ago Changed · +2 lines 80521a30d034
- 9d ago First seen · 88 lines · 21 tokens per session scan A e01e12138d55
recipe-reflect is a skill published in the GitHub repository shinpr/nautilus (4 stars, last pushed 8d ago), licensed MIT. It adds 21 tokens to every session and 835 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 2026-08-31.
Other skills, from other repositories
productspec
Use when implementing, reviewing, planning, or changing work governed by a Product Spec. Treat .product-spec.md files as the product contract for the work.
productspec-authoring
Writes, validates, and converts ProductSpec files (.product-spec.md), the Markdown format for recording product intent before implementation. Use when authoring a new Product Spec, converting an existing PRD or feature doc into one, validating spec files locally or in CI, or recording how a spec's intent changed over…
product-principles
Defines 4 Risks confidence thresholds, OST hierarchy levels, Knowledge Pyramid tiers, and state design requirements. Use when evaluating user stories, setting confidence scores, referencing OST levels, scoping MVP, or determining validation sufficiency.
recipe-define
Orchestrate PRD creation from validated hypotheses — standard PRD output with 4 Risks confidence and hypothesis traceability.
recipe-validate
Orchestrate hypothesis validation through type-appropriate methods — prototypes, code analysis, market research, and expert review.
hypothesis-discipline
Manages hypothesis lifecycle, enforces validation criteria, time budgets, and confidence scoring rules. Use when creating hypotheses, updating confidence scores, setting validation criteria, handling timeouts, or recording validation results.