pchalasani/claude-code-tools is a collection of command-line tools, skills, agents, hooks, plugins, and commands for improving work with Claude Code, Codex-CLI, and similar coding agents. It is used by developers who want reusable productivity features and workflows for terminal-based AI coding assistants. The catalogue entries are components of, or extensions for, these coding-agent workflows.
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.
git clone --depth 1 https://github.com/pchalasani/claude-code-toolsWrote 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/agents/pchalasani/claude-code-tools/session-searcher)<a href="https://agentmods.dev/agents/pchalasani/claude-code-tools/session-searcher"><img src="https://agentmods.dev/badge/agents/pchalasani/claude-code-tools/session-searcher/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/agents/pchalasani/claude-code-tools/session-searcher"><img src="https://agentmods.dev/badge/agents/pchalasani/claude-code-tools/session-searcher.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 | $0.00064 | $0.00922 |
| Opus 5.5 | $0.00026 | $0.00369 |
| Sonnet 5.5 | $0.00013 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
session-searcher 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- session-searcher — 94% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
-
Choose your approach:
Path A — Direct examination (when session file is already known):
- If a specific file path was provided, or context makes it obvious which session to examine, go directly to that file.
- Read the session file and extract the relevant information.
- Skip to step 3.
Path B — Search first (when you need to discover sessions):
- Run
aichat search --json -n 10 "[query]"(use-gfor global,--dir "project"to filter) - Parse results with
jqto extract: session_id, project, created, snippet, file_path - Read session files at the
file_pathfrom results (max 3 files) - Run
aichat search --helpto see all options (date filters, branch filters, etc.)
-
Verify if needed: If referencing current codebase files (TO-DOS.md, etc.), read them to confirm current state
-
Summarize: Return a focused summary with key findings and session references
<output_format> Return a concise but comprehensive summary containing:
- Key Findings: 2-3 bullet points answering the query
- Relevant Sessions: Session IDs and dates for reference
- Specific Content: Code snippets or quotes if directly relevant
- Context: Brief explanation of how findings relate to query
Format as clean markdown, not raw JSON. </output_format>
Search: aichat search --json -n 10 "authentication design"
Summary:
Key Findings
- Session abc123 (Dec 10): Discussed JWT vs session-based auth, decided on JWT for API
- Session def456 (Dec 8): Implemented refresh token rotation pattern
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.
- 1mo ago First seen · 89 lines · 64 tokens per session scan A c12220a7f9bf
session-searcher is an agent published in the GitHub repository pchalasani/claude-code-tools (2,016 stars, last pushed 3d ago), licensed MIT. It adds 64 tokens to every session and 922 once invoked, about $0.0003 per session on Opus 5.5. 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 agents, from other repositories
seance
Read a historical session and nested transcripts to answer a consult.
context-manager
Manages context across multiple agents and long-running tasks. Use when coordinating complex multi-agent workflows or when context needs to be preserved across multiple sessions. MUST BE USED for projects exceeding 10k tokens.
Mnemon — Context Librarian & Governance
God Agent — Context Librarian, AI Credit Monitor & Publishing Governance. Mnemon (μνήμων) — the mindful one, the context-keeper. In Greek, mnemon is someone charged.
God Agent Mnemosyne — Knowledge Agent
God Agent Mnemosyne — Knowledge Management & Institutional Memory. Titan of memory. Mother of the nine Muses. Mnemosyne holds everything — so the team never.
reference-builder
Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable reference materials. Use PROACTIVELY for API docs, configuration references, or complete technical specifications.
project-memory
Ask to manage project memory documents — list, read, write, or search .md files in the project memory directory.