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/haabe/mycelium/reflexionnpx skills add haabe/mycelium --skill reflexiongit clone --depth 1 https://github.com/haabe/myceliumWrote 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/haabe/mycelium/reflexion)<a href="https://agentmods.dev/skills/haabe/mycelium/reflexion"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/reflexion.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 | $0.00034 | $0.01269 |
| Opus 5 | $0.00017 | $0.00634 |
| Sonnet 5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
reflexion 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflexion Skill
Self-correcting implementation loop from the n-trax pattern.
Workflow
Iteration Loop (max 3)
Step 1: Implement
- Create the deliverable according to the specification/acceptance criteria.
- Software: write code. Content: write/produce content. AI tool: write prompts/configs. Service: document workflow.
- Follow ${CLAUDE_PLUGIN_ROOT}/harness/engineering-principles.md (principles apply to all product types).
- Apply patterns from patterns.md.
- Check corrections.md for relevant past mistakes.
Step 2: Validate
- Software: Run tests, linter, type checker, security scan, accessibility checks (if UI).
- Security validation (OWASP): Check input validation, output encoding, parameterized queries, no hardcoded secrets, authentication/authorization patterns, dependency vulnerabilities. Reference OWASP Top 10:2025 categories for each check.
- Content: Review against learning objectives/editorial standards, check accessibility (captions, alt text), fact-check claims.
- AI tool: Run eval test cases, red-team testing, bias assessment.
- Service: Walk through the service blueprint end-to-end, verify documentation completeness.
- All: Verify acceptance criteria.
Step 3: Self-Critique Review the implementation against (select items relevant to product_type):
- Engineering principles: DRY, KISS, YAGNI, SoC (apply to all product types)
- Security: Input validation, output encoding, no secrets, parameterized queries (software, ai_tool)
- Accessibility: Semantic HTML, keyboard nav, contrast, screen reader (software); captions, transcripts, alt text (content)
- Edge cases: What happens with unexpected input? Empty? Adversarial? (software, ai_tool)
- Error handling / user recovery: Are errors handled gracefully? Can users recover? (software, service)
- Quality: Factual accuracy, style consistency, source attribution (content); eval scores, safety scores (ai_tool)
- Naming / clarity: Do names reveal intent? Would a new reader understand this? (all)
- Completeness: Is anything missing that the user would expect? (all)
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 First seen · 95 lines · 34 tokens per session scan A 722b96a3d071
reflexion is a skill published in the GitHub repository haabe/mycelium (45 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 1,269 once invoked, about $0.0002 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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