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 tobihagemann/turbo --skill recall-reasoninggit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/recall-reasoning)<a href="https://agentmods.dev/skills/tobihagemann/turbo/recall-reasoning"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/recall-reasoning/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/tobihagemann/turbo/recall-reasoning"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/recall-reasoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.00952 |
| Opus 5 | $0.00038 | $0.00476 |
| Sonnet 5 | $0.00015 | $0.00190 |
| Haiku 4.5 | $0.00008 | $0.00095 |
Grade A, and why
recall-reasoning 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 11d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall Reasoning
Locate the Claude Code transcript that produced a given change and extract the implementer's reasoning. Useful for answering reviewer questions, writing post-hoc explanations, or recovering forgotten context.
Inputs
Accept any of:
- A commit SHA
- A file path, optionally with a line number (
<path>:<line>) - A reviewer question plus surrounding context (file and line)
If only a file is given, git blame resolves the commit that last touched the line.
Step 1: Resolve the Commit and Run the Script
Call scripts/find_transcript.py with either --commit <sha> or --file <path>[:<line>]. Pass --cwd /path/to/repo when searching a different repo than the current working directory.
python3 <skill-dir>/scripts/find_transcript.py --file <path>:<line>
python3 <skill-dir>/scripts/find_transcript.py --commit <sha>
The script:
- Resolves the commit via
git rev-parseorgit blame - Enumerates directories under the effective Claude configuration home's
projects/tree and filters their transcripts by authoritativecwdrecords, covering path encoding, truncation, collisions, and subdirectory launches - Ranks candidate transcripts whose mtime is within
--window-daysof the commit (default 14) - Scores candidates by mentions of touched files and tool-use edits on them
- Extracts cleaned user prompts and substantive assistant text from the top candidates
The JSON output has status, commit, project_dir (the top candidate's directory when matched), project_dirs (directories with matching cwd records in the time window), and candidates with session_id, score, match_reasons, and excerpts.
Status values:
ok— candidates returnedno-commit— couldn't resolve a commitno-transcripts— the effective Claude configuration home has no project transcript directoriesno-match— transcripts exist but none match the touched files in the window
Step 2: Read and Synthesize
If status is ok:
What ships with it
1 file 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.
- 11d ago First seen · 91 lines · 76 tokens per session scan A d9a6cbd33365
recall-reasoning is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 952 once invoked, about $0.0004 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 skills, from other repositories
napkin
Maintain a per-repo napkin as a continuously curated runbook (not a session log). Activates EVERY session. Read and curate it before work, keep only recurring high-value guidance, organize by priority-sorted categories, and cap each category at top 10 items. The napkin lives at .claude/napkin.md.
install-memu
Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.
agent-wiki
Incremental LLM-friendly wiki generator for Obsidian note vaults. Use when: (1) Building wiki from notes, (2) Ingesting notes to wiki, (3) Obsidian LLM wiki, (4) Incremental knowledge base management. Triggers: 'build wiki from notes', 'ingest notes to wiki', 'Obsidian LLM wiki', 'incremental knowledge base'.
capability-evolver
Self-evolution workflow for the agent. Before substantive work, recall past outcomes from evolution memory; while editing, detect improvement signals; at task end, record the outcome; when reusable, distill or search the EvoMap network for proven genes/capsules. Use when the user starts non-trivial work (a feature, a…
self-evolving-memory-graph
Grants the AI long-term episodic memory. The agent autonomously documents the user's coding preferences, past mistakes to avoid, and architectural decisions into a persistent learning graph.
token-saver
Skill to implement token saving scheme, concise, and focused on essential changes / Skill untuk menerapkan skema penghematan token, ringkas, dan fokus pada perubahan esensial tanpa basa-basi.