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/heal-skill)<a href="https://agentmods.dev/commands/glittercowboy/taches-cc-resources/heal-skill"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/heal-skill/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/heal-skill"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/heal-skill.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 | $0.00012 | $0.00952 |
| Opus 5 | $0.00006 | $0.00476 |
| Sonnet 5 | $0.00002 | $0.00190 |
| Haiku 4.5 | $0.00001 | $0.00095 |
Grade A, and why
heal-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- heal-skill — 94% identical, 6 lines differ
- heal-skill — 91% identical, 15 lines differ
What it actually says
Analyze the conversation to detect which skill is running, reflect on what went wrong, propose specific fixes, get user approval, then apply changes with optional commit.
<quick_start>
- Detect skill from conversation context (invocation messages, recent SKILL.md references)
- Reflect on what went wrong and how you discovered the fix
- Present proposed changes with before/after diffs
- Get approval before making any edits
- Apply changes and optionally commit
</quick_start>
- Look for skill invocation messages
- Check which SKILL.md was recently referenced
- Examine current task context
Set: SKILL_NAME=[skill-name] and SKILL_DIR=./skills/$SKILL_NAME
If unclear, ask the user. </step_1>
<step_2 name="reflection_and_analysis"> Focus on $ARGUMENTS if provided, otherwise analyze broader context.
Determine:
- What was wrong: Quote specific sections from SKILL.md that are incorrect
- Discovery method: Context7, error messages, trial and error, documentation lookup
- Root cause: Outdated API, incorrect parameters, wrong endpoint, missing context
- Scope of impact: Single section or multiple? Related files affected?
- Proposed fix: Which files, which sections, before/after for each </step_2>
<step_3 name="scan_affected_files">
ls -la $SKILL_DIR/
ls -la $SKILL_DIR/references/ 2>/dev/null
ls -la $SKILL_DIR/scripts/ 2>/dev/null
</step_3>
<step_4 name="present_proposed_changes"> Present changes in this format:
**Skill being healed:** [skill-name]
**Issue discovered:** [1-2 sentence summary]
**Root cause:** [brief explanation]
**Files to be modified:**
- [ ] SKILL.md
- [ ] references/[file].md
- [ ] scripts/[file].py
**Proposed changes:**
### Change 1: SKILL.md - [Section name]
**Location:** Line [X] in SKILL.md
**Current (incorrect):**
[exact text from current file]
**Corrected:**
[new text]
**Reason:** [why this fixes the issue]
[repeat for each change across all files]
**Impact assessment:**
- Affects: [authentication/API endpoints/parameters/examples/etc.]
**Verification:**
These changes will prevent: [specific error that prompted this]
</step_4>
<step_5 name="request_approval">
Should I apply these changes?
1. Yes, apply and commit all changes
2. Apply but don't commit (let me review first)
3. Revise the changes (I'll provide feedback)
4. Cancel (don't make changes)
Choose (1-4):
Wait for user response. Do not proceed without approval. </step_5>
<step_6 name="apply_changes"> Only after approval (option 1 or 2):
- Use Edit tool for each correction across all files
- Read back modified sections to verify
- If option 1, commit with structured message showing what was healed
- Confirm completion with file list </step_6>
<success_criteria>
- Skill correctly detected from conversation context
- All incorrect sections identified with before/after
- User approved changes before application
- All edits applied across SKILL.md and related files
- Changes verified by reading back
- Commit created if user chose option 1
- Completion confirmed with file list </success_criteria>
- Read back each modified section to confirm changes applied
- Ensure cross-file consistency (SKILL.md examples match references/)
- Verify git commit created if option 1 was selected
- Check no unintended files were modified
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 · 142 lines · 12 tokens per session scan A 3e4b9649a291
heal-skill is a command published in the GitHub repository glittercowboy/taches-cc-resources (1,976 stars, last pushed 5mo ago), licensed MIT. It adds 12 tokens to every session and 952 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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document-all
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fix-comments
Address PR review comments by implementing requested changes automatically.