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 commands/thierryn/fire-flow/fire-analyticsgit clone --depth 1 https://github.com/ThierryN/fire-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 | $0.00007 | $0.05634 |
| Opus 5 | $0.00003 | $0.02817 |
| Sonnet 5 | $0.00001 | $0.01127 |
| Haiku 4.5 | $0.00001 | $0.00563 |
Grade A, and why
fire-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fire-analytics
Skills usage analytics with effectiveness metrics and learning insights
Purpose
Analyze skills library usage patterns across the project. Shows which skills have been applied, their effectiveness, time savings, success rates, and identifies gaps in the skills repertoire. Helps optimize skill selection for future phases.
Arguments
| Argument | Required | Description |
|---|---|---|
--category [name] |
No | Filter to specific category (e.g., database-solutions) |
--time-saved |
No | Focus on time savings report |
--gaps |
No | Identify missing skills and recommendations |
--period [days] |
No | Limit analysis to last N days (default: all time) |
--export [format] |
No | Export report as md, json, or csv |
Process
Step 1: Load Analytics Data
Read skills usage data from project state files.
# Required files
SKILLS_INDEX=".planning/SKILLS-INDEX.md"
STATE_FILE=".planning/CONSCIENCE.md"
SUMMARIES=".planning/phases/*/RECORD.md"
if [ ! -f "$SKILLS_INDEX" ]; then
echo "No skills data found. Run /fire-search or /fire-3-execute first."
exit 1
fi
Parse:
.planning/SKILLS-INDEX.md- Skills applied per phase/plan.planning/CONSCIENCE.md- Analytics section with usage stats.planning/phases/*/RECORD.md- Skills applied per execution- Git history - Time between commits for duration estimates
Step 2: Compute Analytics
Calculate usage statistics and effectiveness metrics:
Usage Statistics:
- Skills by usage count (descending)
- Category distribution
- Skills per phase average
- Unique skills count
Effectiveness Metrics:
- Time saved estimates (based on skill complexity ratings)
- Success rate (skills that led to passing verification)
- Impact score (performance improvements, bug prevention)
Learning Insights:
- Patterns in skill usage
- Skills that tend to be used together
- Gaps based on phase requirements vs available skills
Step 3: Display Analytics
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 · 500 lines · 7 tokens per session scan A b5340c2f6666
fire-analytics is a command published in the GitHub repository ThierryN/fire-flow (77 stars, last pushed 20d ago), licensed MIT. It adds 7 tokens to every session and 5,634 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.