reflect

A guided conversation review that turns the current discussion into a structured record of failures, corrections, decisions, activated skills and blocked actions.

In plain words
What is it for?
Use it for a retrospective after coding work, including recording what went wrong, what changed and why key choices were made.
Why use it?
It makes the reasoning and lessons from a completed task easier to preserve and revisit.

Command

Install

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.

agentmods
npx agentmods add commands/technicalpickles/pickled-claude-plugins/reflect
Clone the repo
git clone --depth 1 https://github.com/technicalpickles/pickled-claude-plugins
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 864 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00010 $0.00864
Opus 5 $0.00005 $0.00432
Sonnet 5 $0.00002 $0.00173
Haiku 4.5 $0.00001 $0.00086

Measured 2d ago against content hash 0ac9e900ee9a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 2d 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.

plugins/dev-tools/commands/reflect.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reflect on This Conversation

Help capture a retrospective of this conversation. You'll analyze what happened, ask the user a few questions, then generate a structured reflection document.

Step 1: Analyze the Conversation

Review the conversation so far and identify:

Failed Attempts

What was tried and didn't work? How was each failure resolved?

  • Look for error messages, retries, or approaches that were abandoned
  • Note what the resolution was (user correction, different approach, etc.)

Corrections Made

Where did the user provide feedback that changed direction?

  • Explicit corrections ("no, do it this way")
  • Redirections ("let's try something else")
  • Clarifications that revealed a misunderstanding

Skills Activated

Which skills were invoked during this conversation?

  • Look for Skill() tool calls
  • Look for announcements like "I'm using the X skill"
  • Note the skill names

Key Decisions

What significant choices were made about approach, tools, or architecture?

  • Why was each decision made?
  • Were alternatives considered?

Hook Interventions

Were any tool calls blocked or redirected by hooks?

  • What was blocked?
  • What was the workaround?

@reflect Notes

Scan user messages for @reflect tags — these are observations the user wanted captured.

  • Collect all @reflect <note> entries
  • These are proactive observations, not corrections

Step 2: Ask the User

Ask these questions to get the user's perspective. Use the AskUserQuestion tool with all questions at once:

Q1 - Outcome: "Did we accomplish what you set out to do?"

  • Options: Fully complete / Partially complete / Blocked / Abandoned

Q2 - Approach: "How did the approach feel? Any decisions that seemed off?"

  • Open-ended text

Q3 - Skills: (Only if skills were used) "Did any skills help or get in the way?"

  • Options: Helped / Got in the way / Mixed / No opinion

Q4 - Friction: "Where did you have to intervene or correct course?"

  • Open-ended text

Step 3: Generate the Retrospective

Read the full file on GitHub · 128 lines

Changes

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.

  1. 2d ago First seen · 128 lines · 10 tokens per session scan A 0ac9e900ee9a

Subscribe to this mod's changes

reflect is a command published in the GitHub repository technicalpickles/pickled-claude-plugins (10 stars, last pushed 4d ago), licensed MIT. It adds 10 tokens to every session and 864 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.