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 commands/dsifry/metaswarm/self-reflectgit clone --depth 1 https://github.com/dsifry/metaswarmWrote 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/commands/dsifry/metaswarm/self-reflect)<a href="https://agentmods.dev/commands/dsifry/metaswarm/self-reflect"><img src="https://agentmods.dev/badge/commands/dsifry/metaswarm/self-reflect.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 | $0.00018 | $0.02609 |
| Opus 5 | $0.00009 | $0.01305 |
| Sonnet 5 | $0.00004 | $0.00522 |
| Haiku 4.5 | $0.00002 | $0.00261 |
Grade A, and why
self-reflect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BEADS Self-Reflect
You are performing a self-reflection for the BEADS agent swarm. Your job is to analyze PR review comments, conversation history, and session patterns to extract high-quality, reusable learnings.
Philosophy: Be judicious. Quality over quantity. Each learning should make future development measurably better.
Phase A: PR Comment Analysis
Step 1: Fetch PR Comments
GITHUB_TOKEN=$(gh auth token) npx tsx scripts/beads-fetch-pr-comments.ts --days 7
This outputs PR comments to .beads/temp/pr-comments.json.
Step 2: Extract CodeRabbit's Structured Learnings
cat .beads/temp/pr-comments.json | jq -r '.comments[].body' | grep -A5 "^Learnt from:" | grep "^Learning:" | sed 's/^Learning: //' | sort -u
Evaluate Each CodeRabbit Learning
NOT all CodeRabbit learnings are equal. Evaluate each one:
ACCEPT (High Value)
- Applies to patterns:
Applies to **/*.test.ts: ...- Actionable file-scoped rules - NEVER/ALWAYS rules: Clear, enforceable constraints
- Security/Performance: Critical quality gates
- Gotchas with context: Explains WHY something is problematic
REJECT or DEFER (Low Value)
- PR-specific context: "In PR #X, someone did Y" - Too specific unless the pattern generalizes
- Personality observations: "Developer provides detailed updates" - Not actionable
- Process descriptions: Meta, not code
- Duplicate with slight rewording: Check if we already have this fact
- Obvious/trivial: Things any developer should know
TRANSFORM (Medium Value -> Make High Value)
- Before: "In PR #593, developer explains optional AIProvider methods support backward compatibility"
- After: "Optional interface methods (generateWithTools?, generateObject?) follow Interface Segregation Principle - add runtime guards before use"
Quality Filter Questions
For each potential learning, ask:
- Would this prevent a bug? If yes, high priority.
- Would this save review cycles? If yes, add it.
- Is this codebase-specific or universal? Tag accordingly.
- Do we already have this? Check for semantic duplicates.
- Can an agent act on this? If not actionable, skip it.
- Would this confuse a naive agent? If a future agent without context wouldn't benefit, skip it.
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.
- 3d ago First seen · 285 lines · 18 tokens per session scan A 8a2157edf85a
self-reflect is a command published in the GitHub repository dsifry/metaswarm (407 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 2,609 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.
Other commands, from other repositories
git
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.