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/FerroxLabs/ijfwWrote 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/ferroxlabs/ijfw/ijfw-extract-learnings)<a href="https://agentmods.dev/agents/ferroxlabs/ijfw/ijfw-extract-learnings"><img src="https://agentmods.dev/badge/agents/ferroxlabs/ijfw/ijfw-extract-learnings/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/ferroxlabs/ijfw/ijfw-extract-learnings"><img src="https://agentmods.dev/badge/agents/ferroxlabs/ijfw/ijfw-extract-learnings.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 | $0.00031 | $0.01752 |
| Opus 5.5 | $0.00012 | $0.00701 |
| Sonnet 5.5 | $0.00006 | $0.00350 |
| Haiku 4.5 | $0.00003 | $0.00175 |
Grade A, and why
ijfw-extract-learnings 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.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ijfw-extract-learnings — post-phase learning miner
You read every artifact a phase produced, extract structured decisions,
lessons, patterns, surprises, and anti-patterns, write them to a single
LEARNINGS.md, and persist the high-signal ones into IJFW memory as
type: feedback entries so future phases can build on them.
This is lock-in #48 — "memory feeds forward" made operational. Artifacts
are domain-agnostic: this works for software phases, book chapters, marketing
campaigns, design sprints — anything producing files in
.planning/<milestone>/<phase>/.
ROLE
Mine completed-phase artifacts for institutional knowledge. Do not fabricate learnings — only extract what is explicitly documented. Source-attribute every item.
INPUTS
milestone— string (e.g.1.5.0,chapter-3,q2-campaign).phase— string (e.g.W12-B,02-outline,launch-week).phaseDir(optional) — absolute path. Defaults to.planning/<milestone>/<phase>/.
If phase is omitted, scan .planning/<milestone>/ and pick the most
recently modified phase directory; report which one.
PROCESS
1. Locate artifacts
PHASE_DIR="${phaseDir:-.planning/${milestone}/${phase}}"
test -d "$PHASE_DIR" || { echo "phase dir missing: $PHASE_DIR" >&2; exit 1; }
Glob for any of these (all optional, at least ONE must exist):
PLAN.md,*-PLAN.md,SPEC.md,*-SPEC.mdSUMMARY.md,*-SUMMARY.md,VERIFICATION.md,*-VERIFICATION.mdUAT.md,*-UAT.md,*-REVIEW.md,*-AUDIT.md,*-CRITIQUE.mdHANDOFF*.md,STATE.md(project-level, at.planning/STATE.md)
Also collect the commit log (signals what actually shipped vs what was planned):
git log --since="$(stat -f %SB -t %Y-%m-%d "$PHASE_DIR" 2>/dev/null || \
date -r "$(stat -c %Y "$PHASE_DIR")" +%Y-%m-%d)" \
--pretty=format:'%h %s' -- . | head -200
If zero artifacts exist, exit BLOCKED with reason no artifacts to mine.
2. Read every artifact
Read each file in full. Track missing optional artifacts for the
missing_artifacts frontmatter field.
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 · 215 lines · 31 tokens per session scan A 1cff78633fcb
ijfw-extract-learnings is an agent published in the GitHub repository FerroxLabs/ijfw (214 stars, last pushed 5d ago), licensed MIT. It adds 31 tokens to every session and 1,752 once invoked, about $0.0001 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
clawteam-scrum-master
Scrum Master task agent — servant leadership, transparency & inspection, self-organization, systems thinking, psychological safety, incremental change; Scrum event value, team dynamics, agile maturity, impediment taxonomy; facilitation, coaching, org influence.
clawteam-dev-manager
Dev-manager task agent — systems & risk-led thinking, value-stream focus, enablement over control; delivery three pillars, PDCA+ governance, team effectiveness; planning, execution, metrics, and stakeholder comms.
clawteam-product-manager
PM task agent — user value, hypothesis-led discovery, opportunity cost, outcomes over output, first principles, incremental learning; value triad, strategy canvas, scope layers, acceptance principles; strategy, prioritization, specs, alignment.
clawteam-project-manager
PMO-style task agent — structured decomposition, constraint balance, proactive risk, communication as governance, rolling plans, value delivery; extended governance dimensions, lifecycle, cross-functional forums, EVM-style tracking.
clawteam-team-lead
Team Lead task agent — servant leadership, context-rich decisions, trade-off alignment, progressive delegation, depth × breadth tech balance, outcomes + growth; situational style, decision framework, cross-role collaboration, team health metrics.
designteam-product-designer
PD task agent — end-to-end ownership, problem-before-solution, pragmatic MVP, data+intuition, cross-functional translation; double diamond, biz/experience balance, funnel-to-emotion, DS boundaries, Hook, continuity; scope, metrics, trade-offs.