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 agentmods add skills/marcusgoll/spec-flow/debug-phasenpx skills add marcusgoll/Spec-Flow --skill debug-phasegit clone --depth 1 https://github.com/marcusgoll/Spec-FlowWhat 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 | $0.00052 | $0.03846 |
| Opus 5 | $0.00026 | $0.01923 |
| Sonnet 5 | $0.00010 | $0.00769 |
| Haiku 4.5 | $0.00005 | $0.00385 |
Grade A, and why
debug-phase scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:3000/api/endpoint \ How it starts
The opening of the file, as written. The whole thing — 579 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<quick_start> Debug errors systematically:
- Reproduce error consistently (100% reliable reproduction)
- Classify by type (syntax/runtime/logic/integration/performance) and severity (critical/high/medium/low)
- Isolate root cause using binary search, logging, breakpoints, or 5 Whys
- Implement fix with failing test first (TDD)
- Add regression tests to prevent recurrence
- Document in error-log.md with ERR-XXXX ID
Key principle: Fix root causes, not symptoms. Use 5 Whys to drill down. </quick_start>
Check error-log.md for similar historical errors before starting.
Check error-log.md for past occurrences:
# Search by error message
grep -i "connection timeout" error-log.md
# Search by component
grep "Component: StudentProgressService" error-log.md
# Search by error type
grep "Type: Integration" error-log.md
If similar error found:
- Review previous fix (workaround or root cause?)
- Check if error recurred (>2 occurrences = need permanent fix)
- Note patterns (same component, same conditions, timing)
If recurring error: Prioritize permanent fix over workaround.
Create minimal reproduction:
- Gather context: error message, stack trace, timestamp, user actions, environment
- Create minimal test case that triggers error 100% of time
- If intermittent: identify conditions (timing, data state, race conditions)
Example reproduction:
# API error
curl -X POST http://localhost:3000/api/endpoint \
-H "Content-Type: application/json" \
-d '{"field": "value"}'
# Frontend error
# 1. Navigate to /dashboard
# 2. Click "Load Data" button
# 3. Error appears in console
Validation: Can trigger error reliably before proceeding.
By type:
- Syntax: Code doesn't compile/parse (typos, missing brackets, linting errors)
- Runtime: Code runs but crashes (null pointer, type error, uncaught exception)
- Logic: Code runs but wrong result (calculation error, wrong branch taken)
- Integration: External dependency fails (API timeout, database connection, service unavailable)
- Performance: Code works but too slow (timeout, memory leak, N+1 queries)
By severity:
- Critical: Data loss, security breach, total system failure
- High: Feature broken, blocks users from core functionality
- Medium: Feature degraded, workaround exists
- Low: Minor UX issue, cosmetic, no functional impact
Example classification:
Type: Integration (API call to external service fails)
Severity: High (dashboard doesn't load, blocks teachers)
Component: StudentProgressService.fetchExternalData()
Frequency: 30% of requests (intermittent)
See references/error-classification.md for detailed matrix.
Use systematic techniques:
Binary search (for large codebases):
- Add logging at midpoint of suspected code
- If error before midpoint → investigate first half
- If error after midpoint → investigate second half
- Repeat until narrowed to specific function
Increase logging:
# Add debug logs around suspected area
logger.debug(f"Before API call: student_id={student_id}, params={params}")
response = api.fetch_data(student_id)
logger.debug(f"After API call: status={response.status}, data_len={len(response.data)}")
Use breakpoints (interactive debugging):
What ships with it
4 files 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.
- 2d ago First seen · 579 lines · 52 tokens per session scan A 562190153644
debug-phase is a skill published in the GitHub repository marcusgoll/Spec-Flow (91 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 3,846 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
writing-for-agents
Writing documents for agents. Use when creating or editing a skill, an AGENTS.md or CLAUDE.md, a subagent definition, a prompt file, or any doc an agent reaches through a pointer.
tdd
Fix a bug test-first: a focused regression test that fails before the fix and passes after it.
grilling
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
aria
Designs the data model, API contracts, and structural foundation of the system.
github-pr-description
Generate and create pull request descriptions automatically using GitHub CLI. Use when the user asks to create a PR, generate a PR description, make a pull request, or submit changes for review. Analyzes git diff and commit history to create comprehensive, meaningful PR descriptions that explain what changed, why it…
rule
Skill "rule" from x-cmd/x-cmd, covering rule — rules to rule, 核心命令, 规则文件格式, 示例 and 验证规则文件格式.