TÂCHES Claude Code Resources is a collection of custom commands, skills, and agents that structure Claude Code workflows such as planning, debugging, automation, and subagent creation. It is intended for developers who use Claude Code for real software projects. The catalogue entries are examples of the resources included in the collection.
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/glittercowboy/taches-cc-resourcesWrote 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/glittercowboy/taches-cc-resources/run-plan)<a href="https://agentmods.dev/commands/glittercowboy/taches-cc-resources/run-plan"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/run-plan/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/commands/glittercowboy/taches-cc-resources/run-plan"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/run-plan.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 | $0.00011 | $0.01056 |
| Opus 5 | $0.00005 | $0.00528 |
| Sonnet 5 | $0.00002 | $0.00211 |
| Haiku 4.5 | $0.00001 | $0.00106 |
Grade A, and why
run-plan 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execute the plan at {{plan_path}} using intelligent segmentation for optimal quality.
Process:
-
Verify plan exists and is unexecuted:
- Read {{plan_path}}
- Check if corresponding SUMMARY.md exists in same directory
- If SUMMARY exists: inform user plan already executed, ask if they want to re-run
- If plan doesn't exist: error and exit
-
Parse plan and determine execution strategy:
- Extract
<objective>,<execution_context>,<context>,<tasks>,<verification>,<success_criteria>sections - Analyze checkpoint structure:
grep "type=\"checkpoint" {{plan_path}} - Determine routing strategy:
Strategy A: Fully Autonomous (no checkpoints)
- Spawn single subagent to execute entire plan
- Subagent reads plan, executes all tasks, creates SUMMARY, commits
- Main context: Orchestration only (~5% usage)
- Go to step 3A
Strategy B: Segmented Execution (has verify-only checkpoints)
- Parse into segments separated by checkpoints
- Check if checkpoints are verify-only (checkpoint:human-verify)
- If all checkpoints are verify-only: segment execution enabled
- Go to step 3B
Strategy C: Decision-Dependent (has decision/action checkpoints)
- Has checkpoint:decision or checkpoint:human-action checkpoints
- Following tasks depend on checkpoint outcomes
- Must execute sequentially in main context
- Go to step 3C
- Extract
-
Execute based on strategy:
3A: Fully Autonomous Execution
Spawn Task tool (subagent_type="general-purpose"): Prompt: "Execute plan at {{plan_path}} This is a fully autonomous plan (no checkpoints). - Read the plan for full objective, context, and tasks - Execute ALL tasks sequentially - Follow all deviation rules and authentication gate protocols - Create SUMMARY.md in same directory as PLAN.md - Update ROADMAP.md plan count - Commit with format: feat({phase}-{plan}): [summary] - Report: tasks completed, files modified, commit hash" Wait for completion → Done3B: Segmented Execution (verify-only checkpoints)
For each segment (autonomous block between checkpoints): IF segment is autonomous: Spawn subagent: "Execute tasks [X-Y] from {{plan_path}} Read plan for context and deviation rules. DO NOT create SUMMARY or commit. Report: tasks done, files modified, deviations" Wait for subagent completion Capture results ELSE IF task is checkpoint: Execute in main context: - Load checkpoint task details - Present checkpoint to user (action/verify/decision) - Wait for user response - Continue to next segment After all segments complete: - Aggregate results from all segments - Create SUMMARY.md with aggregated data - Update ROADMAP.md - Commit all changes - Done3C: Decision-Dependent Execution
Execute in main context: Read execution context from plan <execution_context> section Read domain context from plan <context> section For each task in <tasks>: IF type="auto": execute in main, track deviations IF type="checkpoint:*": execute in main, wait for user After all tasks: - Create SUMMARY.md - Update ROADMAP.md - Commit - Done -
Summary and completion:
- Verify SUMMARY.md created
- Verify commit successful
- Present completion message with next steps
Critical Rules:
- Read execution_context first: Always load files from
<execution_context>section before executing - Minimal context loading: Only read files explicitly mentioned in
<execution_context>and<context>sections - No skill invocation: Execute directly using native tools - don't invoke create-plans skill
- All deviations tracked: Apply deviation rules from execute-phase.md, document everything in Summary
- Checkpoints are blocking: Never skip user interaction for checkpoint tasks
- Verification is mandatory: Don't mark complete without running verification checks
- Follow execute-phase.md protocol: Loaded context contains all execution instructions
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 · 130 lines · 11 tokens per session scan A 805dfecf3c77
run-plan is a command published in the GitHub repository glittercowboy/taches-cc-resources (1,975 stars, last pushed 5mo ago), licensed MIT. It adds 11 tokens to every session and 1,056 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.
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