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 cyberelf/agent_skills --skill retrospectgit clone --depth 1 https://github.com/cyberelf/agent_skillsWrote 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/cyberelf/agent_skills/retrospect)<a href="https://agentmods.dev/skills/cyberelf/agent_skills/retrospect"><img src="https://agentmods.dev/badge/skills/cyberelf/agent_skills/retrospect/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/cyberelf/agent_skills/retrospect"><img src="https://agentmods.dev/badge/skills/cyberelf/agent_skills/retrospect.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 | $0.00044 | $0.02752 |
| Opus 5 | $0.00022 | $0.01376 |
| Sonnet 5 | $0.00009 | $0.00550 |
| Haiku 4.5 | $0.00004 | $0.00275 |
Grade A, and why
retrospect 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 9d 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 — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retrospect Skill
This skill performs meta-analysis on the current conversation and project instructions to continuously improve code and process quality and adherence to established patterns.
Purpose
This skill helps maintain and improve the quality of AI-assisted development by:
- Identifying patterns where user corrections reveal gaps in instructions
- Detecting violations of existing coding standards and conventions
- Updating instructions to prevent repeating the same mistakes
- Creating a feedback loop for continuous improvement
When to Use This Skill
Use this skill when:
- A user points out a recurring mistake or anti-pattern
- Multiple corrections have been made during a session
- You want to ensure coding standards are being followed
- A user explicitly requests instruction review or updates
- After completing a significant feature or fix
Retrospect Process
Phase 1: Session Analysis
Objective: Review the current chat session to identify learning opportunities.
1.1 Collect User Corrections
Scan the conversation history for:
- Direct corrections: "No, you should..." or "That's wrong, the correct way is..."
- Repeated mistakes: Same issue corrected multiple times
- Clarifications: "Actually, we use X instead of Y"
- New requirements: "From now on, always do X when Y"
- Anti-patterns: "Don't do X, prefer Y"
1.2 Identify Instruction Gaps
For each correction, determine:
- Is this covered by existing instructions?
- Is this a new pattern that should be documented?
- Is this specific to this project or a general principle?
- Should this be a new rule, guideline, or example?
1.3 Categorize Lessons
Classify lessons by domain and type:
- Architecture: Design patterns, system organization, separation of concerns
- Process: Workflows, procedures, operational patterns
- Behavioral: Agent decision-making, interaction patterns, role adherence
- Communication: Documentation standards, reporting formats, terminology
- Domain-Specific: Business logic, domain rules, specialized requirements
- Quality: Standards, conventions, validation patterns
- Integration: Cross-system patterns, API usage, external dependencies
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.
- 9d ago First seen · 378 lines · 44 tokens per session scan A 8c07ff697913
retrospect is a skill published in the GitHub repository cyberelf/agent_skills (2 stars, last pushed 13d ago), licensed MIT. It adds 44 tokens to every session and 2,752 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…